Presentation system for abnormal situation prevention on process plant

FIELD: information technology.

SUBSTANCE: system comprises signal processing data collection units for generating statistical data, frequency analysis data, autoregression data and wavelet data. The system displays images representative of the devices, as well as data obtained based on signal processing data associated with one or more devices. For example, signal processing data of a specific device can be displayed. In another embodiment, data generated by converting signal processing data can be displayed.

EFFECT: possibility of presenting a correlation matrix of process control parametres in order to predict or detect emergency situations.

22 cl, 47 dwg

 

The scope of the invention

This patent primarily relates to the implementation of diagnostics and maintenance on the company and, more specifically, to providing opportunities for predictive diagnostics at the manufacturing plant with the help of ways to reduce or prevent the likelihood of non-standard situations in the company.

The level of technology

Management system processes used, for example, chemical plant, refinery or other enterprises typically include one or more centralized or decentralized process controllers, connected by a communication line to at least one host or workstation operator and one or more devices of the process control and instrumentation devices, such as field devices via analog, digital or combined analog / digital buses. Field devices, which may represent, for example, valves, valve positioners, switches, transmitters and sensors (e.g. temperature sensors, pressure, flow), are placed within the production environment of the enterprise and perform certain functions as part of the production process, for example, opening or closing valves, edit the drilling process parameters, increase or decrease fluid flow, etc. Smart field devices, such as field devices that support the well-known Protocol FOUNDATION™ Fieldbus (hereinafter "Fieldbus") or HART®Protocol may also perform control calculations, functions, alarms and other control functions, usually implemented by the process controller.

Controllers process, which are usually placed directly within the production environment of the company, receive a signal reflecting the measured characteristics of the process or process variables generated by or associated with the field device and/or other data related to the field devices, and ensure the execution of the software application controllers. Software application controllers implement, for example, different control modules, decision management process, generate control signals based on the received information and coordinate actions with the control modules or blocks operating in the field devices, such as field devices HART and Fieldbus. The management modules in the controller processes send the control signals on lines or lines transmitting signals to field devices and control by means of these signals the progress of the production process.

The information is bound from field devices and controllers processes, usually provided to one or more hardware devices, such as workstations, operators, workstations, maintenance, personal computers, handheld devices, devices, archival data, reporting, centralized databases, etc. that allows the operator or service technician to perform the required steps in the process, such as changing the settings of the program management process, the change of the functions of the control modules within the process controllers or intelligent field devices, viewing the current status of the process or individual devices within the enterprise, viewing alarms generated by field devices and the controllers of the process, the modeling process for the purpose of training personnel or testing the software management process, diagnosing problems or equipment failures in the enterprise, etc.

In a typical manufacturing enterprise, there are many devices process control and instrumentation devices, such as valves, transmitters, sensors, etc. associated with one or more controllers processes, however, there are many other accessories that are also required for the implementation of the process or include the s with him. Such other devices include, for example, power supply equipment, equipment for the generation and distribution of electric energy, rotating equipment such as turbines, engines, etc. that are posted on the typical enterprise in many places. This additional equipment is not necessarily generates or uses variables of the process and, in many cases, is not subject to control or even is not connected to the process controller with the aim of influencing the process, however, this equipment is also important and, ultimately, necessary for the proper implementation of the process.

As you know, in a production environment there are often problems, especially in the enterprise, with a large number of field devices and accessories. Such problems can be a failure or malfunction of devices, logic elements, such as incorrect modes of operation programs, incorrectly configured control loops process, one or more failures in communication between devices in the enterprise, etc., These and other problems, the number of which, by definition, highly, in the General case leads to the functioning of the production process in normal mode (i.e. to the occurrence of the abnormal situation on the enterprise is AI), that usually means the enterprise performance less than optimal. To date, there are many diagnostic tools and applications designed to detect problems, determine their causes and to assist the operator or service technician to diagnose and fix problems after they occur and detection. For example, workstations, operators who, as a rule, is connected with the controller processes the channels of communication, such as wired or wireless bus, Ethernet, modem, phone line, etc. that contain processors and memory modules designed to perform application or firmware, for example, the control system DeltaV™ and Ovation production Emerson Process Management, which contain a wide range of diagnostic control modules and control loops. Similarly, workstations, technical service, which can be connected to the device control process, such as field devices on the same communication channels that, and application software of the controller, or through other communication channels, such as connection technology linking and embedding for process control (Object Linking &Embedding for Process Control, OPC), the soybean is inane based portable devices, etc., have, as a rule, one or more software applications designed to view alarm and warning signals relating to maintenance, generated by field devices in the enterprise, inspection devices in the enterprise and operations, maintenance of field devices and other devices in a manufacturing company. Similar diagnostic software applications have been developed to help diagnose problems with auxiliary equipment in the enterprise.

For example, software application management solutions Asset Management Solutions, AMS) (at least partially described in U.S. patent No. 5960214 "Integrated communications network intended for use in the control system field devices") manufactured by Emerson Process Management allows you to communicate with field devices and provides storage related data to identify and track operational status of field devices. In some cases, the AMS application can be used for communication with the field device to modify the parameters of the field device, running in a field device of its own software applications, such as, for example, self-calibration or self-test, with the purpose of sex is to receive information about the status or degree of serviceability of field devices, etc. This information may include, for example, status information (for example, whether there has been an accident or similar event), the configuration information of the device (for example, information about current or possible configuration of the field device, as units of measurement used by the field device), device settings (for example, the range of values of the field device and other parameters), etc. of Course, this information can be used by service personnel for surveillance, maintenance, and/or diagnose problems of the field devices.

Similarly, many companies have software application for monitoring and diagnostic equipment, such as RBMware production CSI Systems, or any other known applications used to monitor, diagnose, and optimize the operating state of the rotating equipment. The maintenance personnel typically use these applications for maintenance and monitoring rotating equipment at the enterprise search problems with rotating equipment, as well as definitions of terms and the need for repair or replacement of rotating equipment. Similarly, many companies have software application for the management and diagnosis of power, such as the er, manufactured by Liebert and ASCO, designed for management and maintenance of the generation and distribution of electricity. In addition, enterprises are software applications that serve to optimize the management, for example, optimizers real-time (real-time optimizers, RTO), designed to optimize the management of the company. In such applications, the optimization usually uses complex algorithms and/or business model that allows to predict the required changes to the input data, allowing to optimize the performance of the company relative to some of the considered variable optimization, such as profit.

These and other applications of diagnostics and optimization are implemented, as a rule, the entire system on one or more workstations operator or service technician and can provide the operator or service technician pre-configured visual description of the working condition of the company, or of devices and equipment in the enterprise. Typical kinds of visual descriptions are screens that display alarms generated by the controller processes or other devices in the enterprise, the management screens that display the working state is the controller processes and other devices in the enterprise, the maintenance screens that display the operating status of devices in the enterprise, etc. Similarly, these and other diagnostic applications can provide the operator or service technician the ability to reconfigure the control circuit or the discharge of other control parameters, to perform a test on one or more field devices to determine the current status of these field devices to calibrate field devices or other equipment or perform other actions to detect and fix problems with devices and equipment in the enterprise.

These various applications and tools for optimal identification and correction of problems in the enterprise, however, these diagnostic applications in General are intended for use only after the problem the company has already occurred, i.e. when the company already has in place an emergency situation. Unfortunately, from the appearance of an emergency prior to its detection, identification and correction using these tools may take some time, during which this situation occurs, which reduces the effectiveness of the company for the time spent on the detection, identification and correction problemao many cases, the scenario is as follows. First, the control operator detects a problem on emergency signals, warnings or inefficient company. After that, the operator shall notify the service personnel of a potential problem. Service staff is able to detect or not detect the actual problem and may need to request additional information prior to the actual execution of tests or other diagnostic applications or perform other actions on the identification of the real problem. After problem identification services staff may need to order parts and assign a maintenance procedure; all these circumstances can lead to a significant pause between the problem and the fix for this issue, during which the company will operate under emergency conditions, which in General entails inefficiency of the company.

In addition, many enterprises may be an emergency, which leads to a significant cost or damage to the company within a relatively short time. For example, some emergency situations may cause, substantial damage to equipment, loss of raw materials or a substantial period of time of the unplanned outage at the company even in t is m case if an emergency situation exists only for a short time. Thus, the detection of problems in the company after the occurrence of the problem can lead to significant loss or damage to the company regardless of how quickly the problem will be fixed. Therefore, unlike a simple reaction to an emergency situation and correct problems on the enterprise after its occurrence, it is advisable to try to proactively prevent the occurrence of accidents.

Currently, there is a method that can be used to collect data, giving the user the ability to predict the occurrence of certain emergency situations in the company before the actual occurrence of such emergencies, and to take measures to prevent the predicted emergency situation before applying any significant losses to the company. This procedure is described in patent application U.S. No. 09/972078 Diagnostics "root causes" (based in part on the available patent application U.S. No. 08/623569, currently pending U.S. patent application No. 6017143). All the descriptions of both of these applications are thus included in this document as reference. In General, this technique involves the placement of blocks of collecting and processing statistical data or blocks of statistical process control (statistical processing monitoring, SPM) in CA is the home device, such as the field device in the enterprise. Blocks of collecting and processing statistical data is collected, for example, the data process variables and calculate certain statistics associated with the collected data, such as average value, median value, standard deviation, etc. Then these statistics may be transmitted to the user and analyzed in order to identify samples that points to the future of the known occurrence of an emergency. Upon detection of a specific forecast of emergency can be taken to fix the underlying problem that will advance to avoid emergency situations. However, for a normal operator maintenance collection and analysis of these data can be time-consuming and tedious, especially in enterprises with a large number of field devices that collect such statistics. In addition, the service technician may be able to collect statistical data, but do not know how best to analyze or examine the data or to determine what future emergency situation may occur according to these data, if it can occur.

In addition, generally speaking, the configuration of the collection and review of all statistical data generated by the SPM blocks on PR is the market, is quite cumbersome and tedious, especially in the case of large processes. In fact, currently, the user must generally create client LFS, which individually controls each of the significant parameters of different field devices, in other words, each field device must be individually configured to collect these data. This configuration process is very time consuming and susceptible to human error.

Disclosure of inventions

A system of visual representation of data, receiving the data signals, which are generated by the blocks of the data collection signal processing relating to the devices in the enterprise. The blocks of the data collection signal processing can generate data, such as statistics, data analysis frequency, data, var, data, wavelets, etc. the System creates a visual representation of the devices and the representation of the context of devices in the enterprise. In addition, the data is displayed based on data from the signal processing relating to one or more devices. For example, you can display data signal processing device. In another embodiment, display data that can be generated based on the data signal processing. In addition system the EMA may have a user interface, which allows the user to select one or more devices for which you want to display data derived from the data signal processing.

Brief description of drawings

Other features and advantages of the invention will become apparent from the following descriptions with reference to the accompanying drawings, which illustrate a variant embodiment of the invention, without introducing any limitations. In the drawings:

figure 1 presents an exemplary diagram of companies with a distributed network management and maintenance, which includes one or more workstations, operators and service technicians, controllers, field devices and accessories;

figure 2 presents an exemplary diagram of the enterprise figure 1, illustrating the communication linkages between the various components of the system of prevention of emergency situations, located in different elements of the enterprise;

figure 3 shows the screen configuration of the set of blocks of statistical process control device in the enterprise figure 1 or 2;

figure 4 presents a diagram illustrating the method of configuration blocks of the statistics gathering process at the plant and collecting statistical data from these blocks in the process of functioning of the enterprise;

figure 5 image is Agen screen, which shows the hierarchy of the company, built by the OPC server in the enterprise figure 1 or 2;

figure 6 shows the screen where it shows the hierarchy of elements of the enterprise associated with devices that have blocks of statistical process control;

figure 7 shows the screen on which the user can select the set of parameters of statistical process control, subject to control by the unit statistical process control;

on Fig depicts a screen, which can display the collected data, statistical process control, generated in the devices with blocks of statistical process control;

figure 9 shows the screen where it shows the hierarchy in the "Explorer", including the elements of statistical data derived from the block of the data collection device;

figure 10 shows the screen where it shows how to add or configure the unit statistical data collection in the field device;

figure 11 shows the screen where it shows how user navigation when viewing trend data;

on Fig shows the screen where it shows how user navigation when viewing the raw data obtained from a block of collection of statistical data;

on Fig depicts a screen that shows grafy the dependence parameter of statistical control of the process from time to time;

on Fig depicts a screen that shows four graphs of the dependencies of the various statistical parameters of the control process from time to time, with each of them showing one or more parameters;

on Fig depicts a screen, which shows a histogram of the statistical parameter control process control limits and specification;

on Fig depicts a screen that shows X-chart according to a statistical parameter of the process control from time to time;

on Fig shows the screen where it shows the S-chart according to a statistical parameter of the process control from time to time;

on Fig depicts a screen that shows a two-dimensional chart scatter several statistical parameters of the control process;

on Fig depicts a screen showing a three-dimensional scatter diagram of the three statistical parameters of the control process;

on Fig depicts a screen that shows the four-dimensional scatter diagram of the four statistical parameters of the control process;

on Fig depicts a screen that shows the correlation matrix of a set of statistical parameters of the control process;

on Fig depicts a screen showing a three-dimensional histogram representing the portion of the correlation matrix on Fig;

on Fig shows the screen on the cat the rum shows a graph of the correlation field, illustrating the deviation from the desired area correlation;

on Fig depicts a screen that shows the correlation matrix color-coded;

on Fig depicts a screen that shows a comparative chart with two measured values of the process variable for the selected device, and the user interface elements that allow the user to view other comparisons;

on Fig depicts a screen that shows a graph of two statistical parameters of the control process from time to time, reflecting the well-known correlation between these parameters;

on Fig depicts a screen that shows a plot of the correlation values from time to time;

on Fig depicts a screen that shows a graph of multiple correlations from time to time;

on Fig depicts a screen that shows a plot of the correlation values and the base values from time to time;

on Fig depicts a screen that shows the matrix of deviations of the correlation values for a set of statistical control of process parameters;

on Fig depicts a screen that shows the matrix of deviations of the correlation values with color-coded;

on Fig depicts a screen, which shows a graph of the dependence of the total correlation values from the belts;

on Fig depicts a screen that shows the matrix of deviations of the correlation values with color coding and a plot of the values of the full correlation from time to time;

on Fig shows a polar graph of the correlation values and the angle corresponding to the slope of a line by the method of best approximation;

on Fig depicts a screen that shows a polar graph multiple values correlation with angles corresponding to the slopes of the lines by the method of best approximation;

on Fig depicts a screen that shows a polar graph of the multiple values of the variance of correlation angles corresponding to the slopes of the lines by the method of best approximation;

on Fig is a block diagram of the system build and run of the rule engine, which gives the user the ability to create and apply rules to the statistical data of the monitoring process, the data collected at the enterprise;

on Fig depicts a screen that shows the configuration screen that allows the user to create a rule for system development and execution of the rule engine on Fig;

on Fig depicts a screen that shows an overview of the mechanism of implementation of the regulations containing the used rules and alerts generated by the rule engine on Fig;

on Fig shows the screen to the torus shows the second configuration screen, which allows the user to create a rule for system build and run the rule engine on Fig;

on Fig depicts a screen that shows the third configuration screen that allows the user to create a rule for system build and run the rule engine on Fig;

on Fig depicts a screen that shows the part of the company and see information about alerts and accidents;

on Fig depicts another screen, showing the part of the company and see information about alerts and accidents;

on Fig depicts another screen that shows the part of the company and see information about alerts and accidents;

on Fig depicts another screen that shows the part of the company and see information about alerts and accidents;

on Fig shown connected to the interface device at the other enterprise, designed to detect and prevent emergency situations;

on Fig shown connected to the front-end device to another enterprise, designed for the detection and prevention of emergency situations.

The implementation of the invention

Figure 1 presents an exemplary enterprise 10, which can be introduced a system of prevention of emergency situations, including set of systems management is the development and maintenance connected together by means of auxiliary equipment for one or more communication networks; in particular, the enterprise 10 in figure 1 contains one or more of the systems 12 and 14 process control. 12 system management process may be a traditional process control system, such as system PROVOX or RS3, or any other control system; it includes the interface 12A of the operator, connected to a controller 12V and cards 12C I/o, which in turn is connected to various field devices, such as analog devices 15 and the remote transmitter 15 with a trunk addressing (Highway Addressable Remote Transmitter, HART). 14 management process, which may be a distributed process control system includes one or more interfaces 14A operator connected to one or more distributed controllers 14B via the bus, for example, the Ethernet Fieldbus. The controllers 14B may constitute, for example, the DeltaV controllers™ manufactured by Emerson Process Management, Austin, Texas, or controllers of any other required types. The controllers 14B are connected through input/output with one or more field devices 16, for example, field devices HART or Fieldbus or any other intellectual or nonintellectual on what avimi devices including, for example, devices that support any of the following protocols: PROFIBUS® WORLDFIP®, Device-Net®, AS-lnterface, CAN. As is known, the field device 16 can transmit controllers 14B analog or digital information associated with process variables, and other data devices. The interfaces 14A operator can maintain and ensure the operation of the instruments by which the operator of the process control can influence the process, including, for example, to manage SEO, diagnostic expert systems, neural networks, tuners and so on, system maintenance, such as computers running the service application network management system (AMS) or any other application monitoring devices and communication applications can be associated with the systems 12 and 14 management processes or separate devices to perform actions for maintenance and control. For example, the computer 18 can be connected to the controller 12B and/or devices 15 via any desired communication lines or networks (including wireless networks or networks of portable devices) to communicate with the devices 15 and, in some cases, reconfigure or perform other maintenance actions devices 15. And the illogical way the application service, such as applications, services, network management, can be installed and run on one or more user interfaces 14A associated with the distributed system 14 process control in order to perform maintenance and monitoring, including the collection of data about the operational status of devices 16.

10 also has a variety of equipment 20 with rotating parts, such as turbines, engines, etc. that are associated with the computer 22 service some permanent or temporary communication channel (such as the bus system radio or portable devices that connect to the equipment 20 for reading and then off). The computer 22 can contain and to ensure the functioning of known applications 23 monitoring and diagnostics, for example, the production of CSI (Emerson Process Management Company) or any other known applications used to diagnose, monitor and optimize the operating status of the equipment 20 with rotating parts. Staff typically uses application 23 for maintenance and monitoring equipment 20 with rotating parts 10, detect problems with equipment 20 with rotating parts and determine when or whether to proceed with the repair or replacement oborudovanie with rotating parts. In some cases, involved external consultants or service organizations, which may be temporarily get or measure data related to the equipment 20, and to use these data for research equipment 20 to detect problems, inefficiencies, and other problems with the equipment 20. In these cases, the computers that run the study may be unrelated to the rest of the system 10 via any of the communication line or may be connected only temporarily. Similarly, the system 24 of the generation and distribution of electricity, which includes equipment 25 generation and distribution of electricity 10, is connected, for example, via the bus with another computer 26, which provides control and monitoring equipment 25 generation and distribution of electricity in the enterprise 10. The computer 26 can function well-known applications 27 control and diagnostics of the power supply, for example, production Liebert, ASCO or other companies that perform control functions and service equipment 25 of the generation and distribution of electricity. As in the above case, in many cases, involved external consultants or service organizations that can use the service application, temporarily receiving the data, related equipment 25, directly or by measurement, and use this information when doing research equipment 25 to detect problems, inefficiencies, and other problems with equipment 25. In these cases, the computers (for example, computer 26), running studies, may not be associated with the rest of the system 10 for any communication line or may be connected only temporarily.

As shown in figure 1, on the basis of computer system 30 is implemented at least part of the system 35 prevention of emergency situations, in particular, to a computer system 30 is stored and operates the application 38 configuration and data collection, the application 40 view or interface, which can contain modules of statistical data collection and processing, and the application 42 build and run of the rule engine; in addition, it stores the base 43 of the data statistical process control, which stores statistical data generated on certain devices within the process. Generally speaking, the application 38 configuration and data collection provides the configuration and interaction with each of a set of modules collection and analysis of statistical data (not shown in figure 1), located in field devices 15, 16, controllers, 12V, 14V, the equipment is 20 with rotating parts or the computer 22 service equipment 25 the generation of electricity or computer 26 maintenance and any other required devices and equipment 10 to collect statistical data (or, in some cases, data process variables from each of these modules for the implementation of prevention of emergency situations. The application 38 configuration and data collection may be connected by a communication channel through a hardware bus 45 to each computer or device 10 or, alternatively, may be connected via any other desired communication channel, such as a wireless connection, a specialized connection with the use of ORS or temporary connection, for example, on the basis of portable devices used to collect data, etc. in a Similar way, the application 38 may receive data related to the field devices and equipment 10 through the LAN or a public connection, such as Internet, telephone connection, and so (figure 1 shows the connection 46 on Internet), when this data is collected, for example, service provider a third party. Further, the application 38 may be connected by a communication channel with computers/devices in the enterprise 10, using various methods and/or protocols, including, for example, Ethernet, Modbus, HTML, XML, custom is the R methods/protocols, etc. Thus, although in this case, specific examples from the channel of communication application 38 with computers/devices in the enterprise of 10 based on LFS, the specialist in the art will understand that you can also use different methods of communication application 38 with computers/devices in the enterprise 10. The application 38 in the General case can save the collected data in the database 43 of the data.

After the collection of statistical data (or data variables), you can use the application 40 for data processing and/or display of the collected or processed statistical data (e.g., stored in database 43 data in different ways; thus, the user, for example, the Servicer receives a more convenient way to determine the presence or predicted the development of emergency and to take proactive steps to correct the situation. Appendix 42 build and run of the rule engine can be used one or more stored rules, based on which analyzes the collected data to determine the presence or anticipated occurrence of the emergency situation at the enterprise 10. In addition, the application 42 build and run the rule engine may provide the operator or another user in the opportunity to create additional rules, which will also be used in the rule engine for the detection or prediction of emergency situations.

Figure 2 shows part of the 50 sample enterprise 10 in figure 1 to describe one way in which can run the statistical data collection system 35 prevention of emergency situations. Figure 2 shows the relationship between applications 38, 40 and 42 of the system of prevention of emergency situations and the base 43 of the data and one or more blocks of data collection in field devices HART and Fieldbus, however, it should be understood that a similar relationship can be established between applications 38, 40 and 42 of the system of prevention of emergency situations and other devices and equipment on the plant 10, including any devices and equipment shown in figure 1. Part 50 of the enterprise 10, shown in figure 2, includes a distributed system 54 management process, which has one or more controllers 60 process associated with one or more field devices 64 and 66 through the card I/o or device 68 and 70, which can be a device I/o any required types of supporting any desired communication Protocol or management. Field device 64 is shown as a field device HART field device 66 is shown as Fieldbus field devices, however, these field the disorder can use any other required communication protocols. Next, the field devices 64 and 66 may represent any type of device, such as sensors, valves, transmitters, controllers, etc. and can support any required outdoor, private, or other communication Protocol or programming; it should be noted that the device input/ output 68 and 70 must be compatible with the required Protocol used by the field devices 64 and 66.

In any case, one or more user interfaces or computers 72 and 74 (which may represent any type of personal computers, workstations, etc)available to company personnel, such as engineers, configuration, operators, process control, maintenance staff, plant managers, inspectors, etc. connected to the controller 60 of the process on the communication line or bus 76, which may be implemented with any desired wired or wireless communication infrastructure and using any desired or suitable communication Protocol, for example, Ethernet Protocol. In addition, the base 78 of the data may also be connected to bus 76 is connected and function as a means of archiving data collecting and storing configuration data and current data variables, parameter data, status data, and other data, wired the e controller 60 of the process and the field devices 64 and 66 in the enterprise 10. Thus, the base 78 of the data can function as the configuration database and to store the current configuration, including modules, process configuration, and data configuration management system 54 management process, downloaded and stored in the controller 60 of the process and the field devices 64 and 66. Similarly, in the 78 data can be stored historical data prevention of emergency situations, including statistical data collected by the field devices 64 and 66 in the company for 10, or statistical data, defined on the basis of process variables collected by the field devices 64 and 66. The controller 60 of the process, the devices 68 and 70 of the I/o and field devices 64 and 66, as a rule, are distributed in an enterprise environment, sometimes adverse, and workstations 72 and 74 and the base 78 of the data are usually located in control rooms, maintenance rooms or other more favorable locations easily accessible by operators, maintenance staff, etc.

In the General case, the controller 60 of the process reside and execute one or more applications controller that implement management strategies using a variety of different, independently executed, modules or control units. Each of the control modules may consist of what is usually ODA is determined as function blocks, each function block is a part or sub-program total program management and in conjunction with other function blocks (via links, called connections) performs cycles of process control 10. As is known, the functional blocks, which may be objects in an object-oriented Protocol programming, as a rule, do one of the following: the input function, for example, associated with the transmitter, sensor or other device for the measurement of the process parameter; the control function, for example, associated with a control routine that performs PID control, fuzzy logic, etc.; the output function, which controls the operation of some device, such as a valve, to perform some physical function within the process plant 10. Of course, there are hybrid and other types of complex functional blocks, such as controllers forecasting model predictive controllers, MPC), SEOs, etc. Should be understood that in the Fieldbus Protocol and system Protocol DeltaV™ uses the control modules and function blocks designed and implemented using object-oriented Protocol programming, however, the control modules can be designed using any desired scheme of the program is Finance, for example, sequential function blocks, with multi-level logic, etc. and their development is not limited to the use of functional blocks or other concrete methodology of programming.

Figure 2 shows the workstation 74 of the service, contains the processor A, memory B and device C display. In memory V stored applications 38, 40 and 42 prevent accidents described with reference to figure 1, so that these applications can run on the processor A and display information to the user on the display S (or any other display device such as a printer). In addition, as shown in figure 2, some (and potentially all) of the field devices 64 and 66 include blocks 80 and 82 of collecting and processing data. The blocks 80 and 82 are shown for illustration purposes in figure 2 as blocks advanced diagnostics advanced diagnostics blocks, ADBs), which are known Foundation Fieldbus function blocks that can be set in the Fieldbus for the implementation of the collection and processing of statistical data in the Fieldbus device, however, the blocks 80 and 82 may constitute or contain modules or units of any other type, located in the processing unit that collects data from devices and calculating or determining one or more statistical values or settings on the I these data regardless of are these blocks in the Fieldbus device and do they support the Fieldbus Protocol. The blocks 80 and 82 in figure 2 is shown located in one of the devices 64 and one of the devices 66, however, these or similar units may be located in any number of field devices 64 and 66, as well as in other devices such as the controller 60, the device 68, 70 I/o or any of the devices shown in figure 1. In addition, the blocks 80 and 82 may include any subset of the devices 64 and 66.

In the General case, the blocks 80 and 82, or the sub-blocks these blocks, collect data such as process variables, including the device in which they are located, and perform statistical processing or analysis of data for any number of reasons. For example, the block 80, which is shown as related to the valve, can have the program detect jamming of the valve, which analyzes the data variables of the process valve to determine if the valve is jammed in the state. In addition, the block 80 contains a set of four blocks or modules of statistical process control (statistical process monitoring, SPM) SPM1-SPM4, which can collect data process variables or other valve data and perform one or more operations of the statistical calculations on the collected data to determine, for example, the average value, the median value is tion, the standard deviation, RMS (root-mean-square, RMS), rate of change, range, minimum, maximum, etc. for the data collected and/or detection of events, such as nursing, offset, noise, splash, etc. in the data collected. Specific generated statistical data, as well as the method of their generation, are not important. Thus, in addition to, or instead of, the above-described specific types of statistics can be generated and other types of such data, in addition to generate these data can be used a variety of techniques, including known techniques. The term "unit statistical process monitoring (SPM)" in this case denotes the function which statistical monitoring of process regarding at least one variable of the process or another process parameter, and perform any required software, firmware or hardware in the device for which data is collected, or outside of it. It should be understood that the SPM is usually given in those devices in which data is collected device, therefore, the SPM can get a greater number of relatively better (accurate) data process variables. Thus, the SPM blocks in General provide p the receive better results statistical calculations regarding the collected data variables of the process in comparison with a block outside of the device where you run the data collection process variables.

Another exemplary block 82 in figure 2, which is shown as associated with the transmitter, may have a detection unit connected line, which analyzes the data variables collected by the transmitter, to determine the inclusion of a line at the enterprise. In addition, the block 82 contains a set of four SPM blocks or modules SPM1-SPM4 which may collect process variables or other data in the transmitter and to perform one or more operations of the statistical calculations on the collected data to determine, for example, average value, median value, standard deviation, etc. for the data collected. If necessary, the operation of the blocks 80 and 82 in the main can be performed as described in the patent document U.S. No. 6017143 mentioned earlier. The blocks 80 and 82 is shown as comprising four unit each SPM, however, the blocks 80 and 82 may have any other number of blocks SPM for the collection and definition of statistics. Similarly, the blocks 80 and 82 is shown as having a software detection, allows the detection of specific conditions in the company for 10, but they do not necessarily have the same software detection. Further, the described blocks SPM is provided as sub-elements of ADB, however, they can be Autonomous units located within the device. In addition, the described blocks SPM can be a famous SPM blocks Foundation Fieldbus, however, the term "unit statistical process monitoring (SPM)" in this case is used to denote a block or item of any type, collect data, such as data variables, and performs statistical processing of these data to determine a statistical measure, such as average value, median value, standard deviation, etc. Thus, the term shall mean the software, firmware or other elements that perform this function, regardless of whether these elements form the functional blocks or units of other types of programs, routines or elements, and whether these elements are the Foundation Fieldbus Protocol or any other Protocol, such as PROFIBUS, WORLDFIP, Device-Net, AS-lnterface, HART, CAN, etc.

In one embodiment, each block SPM ADB 80 and 82 may be active or inactive. Active is the SPM block, which currently monitors a process variable (or another process parameter), while the inactive SPM block is not currently monitors a process variable. In the General case, the Loki SPM default inactive, therefore, for each of them in the General case it is necessary to separately configure the control variable of the process. Figure 3 shows a sample screen 84 configuration, which can be presented to the user, engineer, etc. to view and change the current configuration of the SPM for the device. As shown in the screen 84, for a given device configured SPM blocks 1, 2 and 3, and the SPM block 4 is not configured. Each of the configured blocks SPM - SPM1, SPM2 and SPM3, associated with a particular block in the device (marked block), the block type, the index of the parameter within the block (i.e. the controlled parameter and the user that specifies the control function for this unit SPM. Further, each configured SPM block has a set of thresholds that are compared to statistical parameters, including, for example, the limit average limit of large changes (specifies a value that indicates an excessively large signal change) and the limit of low dynamics (specifies a value that indicates too little signal change). Essentially, the detection of changes in average values may indicate the offset process "up" or "down", the detection of large changes may mean that the item is in the process of experiencing an unintended effects of noise (for example, caused by increased vibration), narushenie low dynamics may mean the signal process is filtered, or item became eerily quiet, such as a stuck valve. Next, for each block in the SPM can be installed basic values, such as average value and standard deviation. These baseline values can be used to determine whether met or exceeded the limits of the device. The SPM blocks 1 and 3 in figure 3 are active, as they are received from the user commands the start of the control. On the other hand, the SPM block 2 is inactive, as it is in an inactive state. In addition, in this example, the capabilities of the SPM is available for the entire device, as shown by a frame 86, and have tracking or calculating every five minutes, as shown by a frame 88. Of course, an authorized user may re-configure for SPM blocks within the device observation of other blocks, such as other functional blocks in the device, other parameters associated with them or other blocks in the device, and specify different thresholds, basic values, etc.

On the screen 84 figure 3 shows the concrete blocks of statistical control, but it should be understood that in addition to these parameters, or instead, may be monitored, and other parameters. For example, the SPM blocks or ADB described with reference to figure 2, can in order to calculate the statistical parameters of the process and may result in some warnings depending on changes in these values. For example, the SPM blocks Fieldbus can monitor process variables and to produce 15 different parameters associated with the capture process. This set includes the following parameters: label block, block type, mean value, standard deviation, change, average change standard deviation, basic average value of the underlying standard deviation, the upper limit changes, the lower limit of the dynamics, the limit of the average, state, index option, the timestamp, the user's command. Currently, the most significant are two parameters-mean and standard deviation. However, often other important parameters SPM: basic average value of the underlying standard deviation, the change in the mean value, the change of the standard deviation and the state. Of course, the SPM blocks may define any other required statistics or parameters and to provide the user or requesting application, and other parameters associated with the block. Thus, the blocks that SPM is not limited to the described in this document.

The parameters of the SPM blocks (SPM1-SPM4) figure 2 field devices can be transmitted to the external client, such as a workstation 74, through a bus or network 76 the ligature and the controller 60. Further, or alternatively, access to the workstation 74 to the parameters and other information collected or generated by the SPM blocks (SPM1-SPM4) ADB 80 and 82 may be arranged, for example, through server 89 ORS. This connection may be a wireless connection, a wired connection, a temporary connection (for example, using one or more portable devices) or any other desired communication connection with the use of any desired or suitable communication Protocol. Of course, in any of the described communication connections can be used server communication LFS engaged in merging data coming from devices of various types, common or compatible format. Further, the SPM blocks may be placed in the main device, other devices in addition to field devices or other field devices, and can perform statistical process control outside devices, collecting or generating the raw data, such as raw data variables. Thus, for example, the application 38 in figure 2 may include one or more SPM blocks that collect raw data variables through, for example, a server 89 LFS and calculate some statistic or parameter, for example the mean value is e, the standard deviation, etc. for the data variables of the process. These blocks SPM are outside of the device, collect data, and therefore not, in General, may collect such large amounts of data variables of a process for performing statistical calculations because of the requirements for the transfer of these data, however, these blocks can be effectively used in the determination of statistical parameters for devices or process variables in devices that do not have or do not support the functions of the SPM. In addition, the available bandwidth of the network in the future may increase, because the technology is constantly improving, and the SPM blocks located outside the device that collects the raw data, may be able to collect more data variables of a process for performing statistical calculations. In the following description it will be clear that any statistics or parameters generated according to the description, the SPM blocks may be generated as blocks SPM type blocks SPM1-SPM4 ADB 80 and 82, and the SPM blocks in main or other devices, including other field devices.

In terms of increasing the number of blocks of the collection of statistical data or SPM at a manufacturing plant, it is advisable to have an automated mechanism for collecting statistical data the parameters of the SPM blocks in different devices to determine the trends of the data and transmission of the results of determination in the expert system to further summarize the data and make decisions. In fact, the view of statistical data process in the case of a large-scale process is currently very cumbersome and time-consuming task. Currently, to do this you must create a client LFS that individually monitors each of the required parameters SPM, and to this end must separately configure each device to collect data SPM. As shown above, the data configuration and view statistical data is very time consuming and vulnerable to human error.

Annex 38 for configuration and data collection provides automatic configuration of the SPM blocks in devices such as valves, transmitters, etc. and the collection is available SPM-process data from these blocks SPM during the process. 4 shows the block diagram 90, describing an exemplary method that may be used by the application 38 to configure devices within the production enterprise to collect data SPM and automatically collect this data during the operation of the enterprise 10. Figure 4 circles indicated operations performed by the company using the application 38, and the rectangles indicated object is or elements, which are used or created by the application 38. It should be understood that although this example describes data collection SPM from specific types of transmitters using the Fieldbus Protocol and the use of Fieldbus blocks, collecting statistical data, the described or similar technique may be used to collect statistical data (or other parameters) from other devices using other communication protocols and functional units or from other devices or elements in devices that use a programming paradigm that is different from the programming paradigm based on functional blocks.

In any case, the application 38 in the first block 92 performs the analysis of the hierarchy of the network control process (e.g., enterprise) to build a list of those devices in the enterprise, which contain blocks of collection of statistical data (such as the ADB). In the present description, for example, it is assumed that blocks the collection of statistical data have the form of SPM blocks located in ADB Fieldbus, as described above, however, the block 92 can search and other types of units the collection of statistical data at the same time or in addition to SPM Fieldbus ADB, and this method is not limited to use ADB Fieldbus or SPM blocks in ADB Fieldbus. In one embodiment, can be used by the OPC server (for example, serv the R 89 figure 2), providing to the client, such as the application 38, the data access control and device information. For example, the product OPC Automation 2.0 implements the standard ways of viewing the contents of the OPC server, and these or other ways of viewing can be used for automatic analysis of hierarchy ORS performed to search for devices containing ADB. In addition, the new OCR specification includes the definition of the XML that can be used to combine data and providing access to Web.

Figure 5 shows a portion 94 of the hierarchy exemplary enterprise, built by the OPC server, which shows the device and other elements of the company that have been scanned by the OPC server. At the top level of the hierarchy 94 are nodes 96 and 98, which are called Modules and IO; node 96 Modules contains the data management strategy, the node 98 IO contains data equipment/devices. As you can see in the example hierarchy in figure 5, 10 knots 98 include subnodes associated with the controller (CTLR), cards (S) and ports (P), in this example, ports (P) associated with the real Fieldbus segments in the network controller. In the sections corresponding ports in descending order of hierarchy lists the Fieldbus. In the example in figure 5, each Fieldbus device that contains ADB, includes the node name TRANSDUCER800 or TRANSDUCER1300 the device. (ADB device in Istvan Rosemount 3051F have the name TRANSDUCER800, while ADB devices Rosemount 3051S have the name TRANSDUCER1300.) In the hierarchy of figure 5 shows one such node 100 with the name TRANSDUCER800. The node 100 ADB contains the required diagnostic information. In this particular case, the application 38 requires the parameters of the statistical process monitoring (SPM) in the node 100 ADB, which is disclosed in the hierarchy of figure 5 to illustrate some of the elements associated with ADB in Rosemount 3051F. Of course, the names "TRANSDUCER800" and "TRANSDUCER1300" are just examples of names of known functional blocks supplied by one well-known manufacturer. Other blocks ADB or the SPM blocks may have other names, in addition, in the system other than the system using OPC may also be present other names. In another embodiment, different names may correspond to the blocks of the ADB or the SPM blocks other blocks converters, function blocks, etc. developed and/or delivered later by other manufacturers and/or described in the specifications Foundation Fieldbus, or they can be blocks or other software elements in any other intellectual communication Protocol (for example, a digital Protocol); this may be, for example, any element in the protocols Profibus, HART, CAN, AS~Interface, HTML, XML, and so on (shown only a few of them).

To detect ADB and, therefore, SPM blocks in ADB, block 92 (figure 4) automatically and the Alize or search through the hierarchy 94 ORS to determine the location of all devices contains ADB, within the enterprise. Of course, in block 92 may be initially programmed with a specific format used by the tree 94 ORS, due to which the block 92 will be able to analyze or view the tree 94 and find the device that contains ADB, more effective way. The described method is based on the tree DeltaV OPC, however, this method can be modified for use on other servers ORS, as well as for hierarchies enterprises, generated by means of viewing other types.

The search hierarchy or tree 94 in the General case raises the problem of the tradeoff between speed and reliability. In particular, the search hierarchy 94 in General does not give 100% guarantee detection of all devices with ADB and only the device with ADB. As a rule, more accurate way to detect the device with ADB and is slower. For example, if different manufacturers have devices that tree 94 LFS find the blocks that have the same name as the blocks ADB in the transmitter 3051F, search hierarchy, this device can be falsely identified as having ADB. On the other hand, if the block 92 will make every attempt to resolve this issue by looking at too many nodes to verify that it contains only nodes with real-ADB, the speed of this method is reduced.

In any SL is tea, in one embodiment, the block 92 can search at each node in the hierarchy or tree 94 to determine the location of each node with a known name and associated with the ADB in a device. In some cases, for example, in large enterprises, this search may take a significant amount of time, but it will be the most accurate method of detection of each ADB and, therefore, each SPM in the enterprise. On the other hand, the block 92 may search down the hierarchy until reaching or finding a node whose name is connected with the control unit of statistics, for example, TRANSDUCER800, or TRANSDUCER1300, or any other known native name used by any device manufacturer to refer to the known control unit statistics. If such a node is found, then the parent node associated with these nodes can be defined as a device with ADB. This method is slightly less accurate compared to search on each node within a specific hierarchy ORS or wood, but in General it is faster. However, if another manufacturer will create a device node d'orsay, with the name TRANSDUCER800, this method will still result in the definition of another device with ADB.

In an alternative embodiment, the block 92 can search in each of the nodes having the me, which corresponds to a known ADB, with the aim of finding an additional element in the device, which is also known as uniquely associated with ADB or contains its data. Thus, the block 92 after finding a site with a name that is known to be used as at least one manufacturer to specify ADB can search for a sub-node to determine whether the line item specifications and block tags is "ADVANCED DIAGNOSTICS". In this example, the line item specifications and block tags is set to "ADVANCED DIAGNOSTICS" only for devices with ADB. This method is very reliable from the point of view of search only devices with ADBs, however, this method requires reading the value from the device via the OPC server, which takes considerably more time than a simple view hierarchy ORS. So this way, being quite accurate, in certain conditions, may be too slow.

Another method that may be implemented by a block 92 in figure 4, providing the search tree 94 LFS and providing a compromise between speed and reliability, includes the search hierarchy ORS in the nodes with a known name associated with the ADB, with the aim of finding a sub-node having the name, also known as associated with ADB. For example, searching by this method may begin at the starting point of the tree 94 ORS (figure 5) and have the ü aim search 10 knots 98. Further, according to the method may be performed recursively search each sub-node of node 98 IO. If there were a sub-node named TRANSDUCER800 or TRANSDUCER1300 (or another name, which is known as associated with the control unit statistics type ADB), according to the method determines whether this node sub-node with the name SPM_ACTIVE or any other subnode, which is definitely connected with the control unit statistics. If SPM_ACTIVE be found, for example, in the node TRANSDUCER800, then the control unit 92 determines the parent node of the node TRANSDUCER800 as a device that contains ADB.

Of course, in block 92 may be used any of the above methods or any combination of these methods or any other required hardware search methods with ADB (and, therefore, having SPM). For example, in one embodiment, may be an attempt to identify at least all known ADB implemented in the device is at least one manufacturer, but it can provide and not provide identification of all ADB in the enterprise. In another exemplary embodiment, may be an attempt to identify all known ADB implemented in multiple devices from different manufacturers. Moreover, the stage view has been described as performed using the hierarchy ORS generated by the OPC server, but is this method can be applied to or used for hierarchies, generated by other devices, such as controllers, devices, data archiving, storing the configuration hierarchy in the enterprise, the workstation that stores the hierarchy of devices, etc. So, in other variants of implementation may not be used by the OPC server and/or hierarchy LFS, but can be used for a wide range of other computing devices, communication protocols and Protocol hierarchies, including, for example, known and later developed computing devices, communication protocols, and Protocol hierarchies. In other embodiments, the implementation can be used by Web servers, HMH and/or their own computing devices and protocols.

In the discovery process, or device that contains ADB, block 92 can save the list of devices identified as having ADB, the SPM blocks or blocks of data collection of a different type, as indicated by the frame 108 figure 4. If necessary, the devices listed in the frame 108 can be displayed on the screen as a tree according to their hierarchy. An example of such a hierarchical screen 110 shown in Fig.6. As will be made clear hereinafter, the hierarchy 110, shown in Fig.6, is a subset of the hierarchy, which would be displayed when viewing the control network, generated by the controller, since, as a rule, not all devices on e is a wound management will include ADB. In fact, the screen 110 figure 6 is a copy of the hierarchy controller that includes only devices containing ADB. As will be clear further on, on the screen, figure 6 shows that in each of the devices RT 101 and RT-102 (connected to port P01 card COI device 101 I/o controller with name CTLR-002EC6) and devices RT-103, FT-201 and FT-202 (connected to port P02 Board C01 device 101 I/o controller with name CTLR-002EC6) has ADB.

To read any of the parameters of SPM from the device in the General case it is necessary to know the item ID of the LFS for this parameter. As a rule, for example, in the case of SPM blocks Fieldbus, the item ID of the OPC parameter SPM includes a device identifier, followed by a specifier element. To locate the device ID block 92 can search subnode SPM_ACTIVE for each node device, to which were defined ADB. Then block 92 can get the ID of the element ORS for branches "CV". For example, the item ID of the LFS can be the following:

"DEVICE:0011513051022201100534-030003969/800/SPM ACTIVE.CV". Then the device ID is the ID of the element LFS minus the suffix "SPM ACTIVE.CV". Thus, in this example, the device identifier "DEVICE:0011513051022201100534-030003969/800/". Of course, describes only one SP is a way to determine the device ID in the system ORS, and you can use other ways, together with data or instead.

In any case, after the unit 92 performs the analysis of the hierarchy with the aim of identifying devices with ADB, Annex 38 receives or can easily determine the label of the device, the device ID and the device location for each of these devices. An example of these data for a simple system containing 5 devices with ADB, are shown in table 1 below.

Figure 4 block 114 may then determine which of the stored devices specified in the frame 108, the configured implementation of statistical process control. To perform this function, the block 114 may read the value SPM_ACTIVE.CV with the OPC server for each of the devices specified in the frame 108. For example, for a transmitter RT-101 in the above table, the block 114 may be considered to be an element of d'orsay

DEVICE:0011513051022201100534-030003969/800/SPM ACTIVE.CV. This element LFS can take the value 0 or 255. In the case of SPM blocks Fieldbus value of 0 indicates that the SPM block for this device is disabled, a value of 255 indicates that the SPM block for this device is included. After checking the switching status of the SPM for each device block 114 may divide the device into two categories, namely the device is already configured SPM and the device is not yet configured SM. These categories or lists of the devices presented in figure 4 frames 116 and 118.

After the unit 114 determines the switching state of the SPM in each of the devices listed in box 108, the block 120 may check the status of each of the SPM blocks in each of the devices that have enabled SPM, i.e. devices that are present or stored in field 116. Unit 120 mainly performs this step for determining is configured to control process variables on each of the SPM block devices that currently have enabled SPM, and, if positive, determine the specific controlled process variable. In this example, you can define controls whether the unit SPM currently a process variable by reading the status of the SPM block. In the SPM blocks Fieldbus status can be checked by reading the SPM element[n] STATUS.CV from the OPC server. Thus, for example, to read the state of the SPM block 1 in the device RT-101 indicated in the above table, the block 120 may read the ID of the element OCR DEVICE:0011513051022201100534030003969/800/SPM1 STATUS.CV.

Generally speaking, the status value is an 8-bit number ranging from 0 to 255. The condition is a combination of 8 different bits that can be set or unset. The bits have the following meanings: inactive (1), analysis (2), PR is Verka (4), nothing found (8), changing the medium (16), a significant change in (32), low dynamics (64), not licensed (128). All licensed, but is not configured, the SPM blocks have a status of inactive. If the SPM block has a status of "inactive" or "not licensed", then the control unit 120 may decide that this block will not be controlled, because it does not generate any significant information. If he has any of the other conditions, the block 120 may control the SPM block.

Similarly, the block 122 may automatically configure each device that have not enabled SPM (i.e. the devices listed in box 118), and, thus, to ensure operation in these devices, at least one block of the SPM to determine or control the process variable for plotting statistical data regarding this variable process. In many cases, for example, when using transmitters Rosemount 3051F and 3051, the device is shipped from the factory is not configured SPM; the user is required to manually configure SPM on each device. In enterprises with hundreds or thousands of devices with ADB it would be a very tedious process. To facilitate these operations manual configuration unit 122 automatically configures at least one block SPM for each device. For the filling up of this operation, the configuration unit 122 may determine or maintain the characteristics of a particular process variable, you want to control on the device. This variable can represent the input data in the main process, the output of the PID block or any number of other variables function block (input or output)available in the Fieldbus device. The signs of the variable that you want to control, can be set in the configuration process, defined by the user in sporadic cases or can be user-defined in General before you begin the program 38.

While can be monitored any of the process variables, a Boolean variable to control for statistical purposes is a primary analog input device. For transmitters Rosemount 3051F/S this variable represents the measured pressure or flow (e.g., differential pressure). Thus, at block 122 can be configured to automatically configure the control of primary analog input or output device on one of the SPM blocks in ADB devices. If necessary, the user can manually configure the other blocks SPM device. In an alternative embodiment, the block 122 may store a list of process variables that must be controlled for each type of device and can use this the list to select or determine the controlled variable (variables) of process in any given situation. In this case, the described configuration unit 122 controls a single process variable by one unit SPM in the device, however, the block 122 may configure and several blocks SPM on one specific device and, thus, to control several process variables associated with the device.

Next, the server DeltaV OPC allows the user (with sufficient administrative privileges) to write the required values in certain elements in the device. In this case, you can change the settings of SPM in the device by writing the corresponding item in the OPC server. Thus, the block 122 may automatically configure the device control SPM for the main variables of the process by writing a sequence of values to the OPC server. Table 2 below shows the values for the approximate case, write to the OPC server.

Here [DeviceID] should be replaced with the device ID indicated in table 2. For example, for a device RT-101, you must record the following first element LFS: DEVICE:0011513051022201100534-030003969/800/SPM MONITORING CYCLE.CV. After recording all of these items in the OPC server on the device is configured to control the variable main pressure in the SPM block 1. Of course, here is just one example of the recording unit SPM specific type device is wah Fieldbus and you should understand what can be used other ways of recording in the SPM blocks of other types, instead of or together with described, when a write command is determined in accordance with the communication Protocol used by these units SPM.

In any case, the function blocks 120 and 122 in figure 4 creates a set or list of controlled SPM blocks in the device with ADB. This list is shown in figure 4 as stored or associated with a field 124. Next, in box 126 figure 4 defined set of parameters SPM, which should be controlled by the application 38 for each of the test blocks SPM. A list of 126 parameters SPM can be determined or selected by the user before or during the application 38 or may be selected and determined individually for each controlled unit SPM in the configuration process. The following table 3 summarizes the parameters of SPM, which can be read from the OPC server for each block SPM Fieldbus.

On the other hand, perhaps not all of these parameters must be monitored for each auditable unit SPM. In fact, when excessive amounts of controlled elements of a possible overload of the OPC server. Therefore, the application 38 may provide a mechanism that allows the user to choose a set of controlled parameters SPM. An example of a screen with such a selection is shown in F. g, on which the user can select parameters SPM, which it needs to control, for each of the SPM blocks listed in box 124.

Unit 128 uses the list of monitored parameters SPM (shown in box 126) and the list of controlled SPM blocks (shown in box 124) to create a set of items SPM LFS, which will be controlled by the application 38 during the process. The block 128 may store the set of elements of ORS, as shown in box 130, for use in further steps of the control process. Generally speaking, the unit 128 generates the elements of the SPM ORS for each controlled parameter SPM (shown in box 126) for each of the controlled units SPM (shown in box 124). In other words, on the basis of a given set of controlled SPM blocks and a set of controllable parameters SPM for each such unit 128 generates a set of controlled items ORS as elements of ORS for each possible combination of controlled SPM blocks and controlled parameters of the SPM. In this case, for example, if there are ten controlled SPM blocks and five controlled parameters SPM in each block SPM, the unit 128 generates a total of 50 items ORS. In this example, the ID of the element LFS is a combination of the device ID and suffix ORS from the above tables. For example, to read the average value is Oia for SPM1 device RT-101 will be used with the following element ID LFS: DEVICE:0011513051022201100534030003969/800/SPM1 MEAN.CV.

After all the elements of the ORSA will be identified and stored in box 130, the blocks 132 and 134 are beginning to control the parameters of SPM to monitor changes during the process. Some parameters SPM may vary, for example every 5-60 minutes, depending on the configuration of the blocks SPM, while other parameters SPM can only be changed when configuring the unit SPM. As a result, the block 132 may initially read the current value of all parameters SPM (represented by the elements of ORS in field 130) in the beginning of the process control parameters SPM. In one embodiment, the block 132 may execute the read operation via SyncRead performing a read request each item ID LFS. The result of the reading of each of the parameters SPM is a set of data points SPM indicated by the frame 136 figure 4.

After the operation of the original reading parameters SPM block 134 may begin the anticipation of changes in the parameters of the SPM. Thus, once with the OPC server will read the initial value of each of the test parameters SPM to obtain the first set of data points SPM, block 134 receives or accepts additional data indicating changes in any of the test parameters SPM. Depending on the configuration of the blocks SPM, the parameters of the mean and standard deviation megalithomania, for example every 5-60 minutes. However, when any of the parameter changes SPM server OPC causes the DataChange event, which is recorded by the client LFS, for example by the application 38. In an alternative embodiment, the block 134 may request or read each of the controlled parameters SPM periodically or at predetermined points in time to obtain new data points (box 136). Thus, the data parameter SPM read even if it has not changed. Of course, the block 134 in the process can operate continuously and, consequently, to obtain new parameters SPM, and save these settings SPM in the database for viewing by the user for use by the rule engine, which is described later in more detail, or for any other purpose. Of course, if necessary, the program 90 figure 4 can detect and configure the SPM blocks or other blocks of statistical data collection in the main device for providing these blocks SPM transfer capabilities of statistical indicators or parameters of other system elements 35 prevention of emergency situations (figure 1).

In fact, at any time after the reading of each data point SPM box 136 block 138 can store or save these data points in the local database (for example, the database 43 figures 1 and 2), and these data points can Bud is t to apply in the future to view trends or other purposes. In addition, the block 140 may be used to represent SPM data to the user in any desired or convenient format, for any purpose, for example, for the detection or prediction of emergency situations at the facility. If necessary, the block 140 may be implemented in the form of Annex 40 view shown in figure 1 and 2.

In the General case, the application 40 view (which may be a block 140 figure 4) may present the user with data parameters SPM in any desired or convenient format and provide it, for example, the ability to quickly view new SPM data. For example, the application 40 view can display data SPM in a standard form, such as "Explorer". An example of such a screen is shown on Fig, where the hierarchy 110 figure 6 presents in "Windows Explorer" on the left side of the screen, check the settings SPM (as shown on the screen 7) are displayed in the right side of the screen 115 for each of the test blocks SPM. It should be noted that SPM data in the screen 115 is sorted by devices to facilitate search or view data for a specific device. Of course, the user can select any of the elements or nodes in the hierarchy 110 to view the data SPM associated with these elements or nodes. Further, if necessary, the application 40 view may display a screen Wire the ICA", for example, figure 9, which contains block elements SPM and parameters SPM, for items controlled unit SPM. Thus, in an exemplary hierarchy 141 figure 9 shows the block 142 SPM name SPM 1, located in the device name 3051-Flow. Elements 143 under the block 142 SPM 1 denote the parameters of the SPM, which are controlled and available for viewing by the user. In this case, the set of specified parameters include the average value, average value, standard deviation, change, standard deviation, mean value, divided by the standard deviation, and the standard deviation divided by the mean value.

If necessary, the application 40, you may allow or give the user the ability to add or reconfigure one or more SPM blocks in the field device or even within the main or other device, in which there are these blocks. Figure 10 shows the screen 144, which in this case gives the user the ability to add a new device on a port named P01 and, in addition, to add or configure the SPM block in this device, as shown in box 145. Here, the SPM block is called SPM1, associated with the tag device FT3051-COLD1 (which is shown in the hierarchy in the left side of the screen 144 as a device 3051_LEVEL) and is associated with (works with) change the th Dr of the OUT parameter of the function block Analog Input with name A. In this case, the application 40 view also allows the user to determine the required (i.e. controlled) parameters SPM, as well as baseline and threshold values, such as average change average change standard deviation, etc. for SPM block.

Further, the application 40 view may enable the user to navigate through the hierarchy to retrieve represent certain types of data directly from the SPM blocks (or other control units) or data generated, for example, by the application 40. For example, figure 11 shows the screen 146, which depicts the hierarchy of the enterprise 147 in the left side of the screen and one or more SPM or other blocks associated with the devices in the hierarchy in the representation of 148 in the right side of the screen 146. After selecting one of the blocks SPM (in this case, SPM 1 in the device 3051 S-1), the user can choose whether to view the data coming from the unit SPM1, through a drop-down or pop-up window 149. Figure 11 the user has chosen to view the graph of the trend, and the following pop-up or drop-down box allows the user to define a specific data parameter SPM (or combinations thereof) to be graphed trends. In this case, it should be understood that some of the possible types of data, a trend which you want to install the e l e C can be defined as the combination of data coming from one or more SPM blocks, and these combinations can be calculated on the host device (for example, by the application 40), or in a field device, or other device having access to this raw data.

On Fig shows the screen 146, on which the user chose to view the data directly in the pop-up window 149. Here, of course, the choice of the data in the next pop-up window may be different and point to the raw data collected or generated by the SPM block without specifying options for the data generated in the main device (for example, the average value divided by the standard deviation, and so on), and it should be understood that the application 40 can receive data from the SPM block or, in some cases, may generate the data based on the raw statistical data obtained from the SPM block. Further, when viewing data received from the SPM blocks or generated based on the data from the SPM blocks) you can use other types of views or options, such as histograms. In addition, the user can use the screen 146 and a pop-up window 149 to perform other functions, such as deleting data SPM, start a new cycle of data collection, etc.

On Fig shows an exemplary graph 150 trends which could be generated by the application 40; it shows the dependence of average values of SPM from time to time. On this screen using the buttons 152 control the user can view earlier or later data, move to the beginning or end of the data, search for limits in data, etc. In any case, the window trends, for example, shown in Fig, allows the user to view the history of the conduct of any parameter SPM. Depending on the process, on the basis of trends of various process variables can be characterized accident conditions. On the other hand, there are virtually no restrictions on the actions of the user with statistics of the process, and it should be understood that the user can use these data for other purposes in addition to the detection of current or anticipated emergencies at the facility. Finally, the user can view the statistical data collected in any format or presentation of these data, to facilitate their interpretation, analysis and use for detection and prediction of events in the enterprise.

At first glance, the graph on Fig looks like a regular schedule of changes in the process variable over a long period of time. However, it should be noted that this graph is not the graph of clean data process variable over a long period of time, and schedule medium meant the second process variable, calculated in a certain interval. For plotting the dependence of the mean of the process variable from the time you can use the device backup of the data transmission system; the difference is that the average value of the process variable is calculated in the device, which, as a rule, initially collects these data, and, accordingly, the reception data is much faster. Therefore, it is assumed that the noise measurement on the chart on Fig will be present to a much lesser extent compared with the schedule created by the device data archiving. In addition, statistic, such as mean value, should be more accurate because it is usually based on a larger amount of collected data.

In this way, the application 40 may comprise a graph of any other parameters SPM (e.g., standard deviation, change, average change standard deviation and so on) from time to time, and any mathematical combination of parameters SPM (e.g., standard deviation divided by the mean value, etc) in Addition, the application 40 can place any combination of any of these graphs on one chart or page and to facilitate the comparison of different statistical data easier to use the on of the motor. On Fig shows a set of graphs of various statistics process variables on the same time scale, and they can all be presented to the user simultaneously on the same display screen or at different times on the same or different screens. On Fig upper left graph 156 shows the dependence of the standard deviation from the time, the top right graph 158 reflects the dependence of the mean value, divided by the standard deviation, from time to time, the bottom left graph 160 shows the relationship of three different values (from different blocks SPM) from time to time in the same scale, the lower right graph 162 reflects the dependence of three standard deviations (from different blocks SPM) from time to time in the same scale. Of course, the application 40, you may submit any of the parameters SPM or any mathematical combination of these parameters on the chart for a long time and can output any number of different parameters SPM (or their mathematical combination) for a long time on one chart that helps the user to get an idea of the processes in the enterprise.

Statistical process control is often used in the field of process control to determine whether some process variable is outside normal pre the eeee. There are usually upper and lower control limits (UCL, LCL) and upper and lower limits of the specification (USL, LSL), which can be calculated based on the SPM data collected by the application 38. The control limits can, in one example, be expressed as UCL=µ+3σ and LCL=µ-3σ, where µ and σ - base average and the baseline standard deviation, respectively. Further, the limits of the specifications can be expressed as follows:

where the user-specified limit of the average percentage values. Of course, the application 40 view can calculate these values directly, or allow the user to enter these values. For these or similar points of application 40, you may schedule the distribution of the average value depending on the underlying medium, and reference levels, thus providing a visual display of reaching or exceeding the limit of the average during the operation of the enterprise. The result is essentially a histogram, which might look like the graph 166 on Fig. As you can see, the upper and lower control limits are shown by lines 167 and 168, respectively, the upper and lower limits specifications are shown by lines 169 and 170, respectively. Next point average (i.e. the average number of points in each of the m value) is displayed on line 172, but the basic point average are displayed as histograms 174. If the process is under control, as shown in figure 166, all of the data lie between these limits. In the case of an emergency, some data may exceed (go beyond) the control limits or the limits of the specifications 167-170. This graph 166 also differs from the usual histogram fact that the graph 166 displays the average (and the underlying average) values of the process, not the actual process performance.

If necessary, the application 40, you may add the control limits and the limits of the specifications, such as described above, on the graphs of the average, standard deviation, or any other desired statistic (e.g. median values and so on) from time to time. Adding limits on the graph of the average time the resulting graph is called the X-chart. Example X-chart 178 for the statistical mean is shown in Fig. Here the dependence of the average from the time represented by line 180, the upper and lower control limits are indicated by lines 181 and 182, respectively, the upper and lower limits of the specifications indicated by lines 183 and 184, respectively.

In this case, you may need to adjust the calculation of the upper and lower control limits, because agenie 40 view is not the graph is not the process variable, and the average over a certain time interval. The noise measurement is reduced, so the change that you would see on a normal X-diagram representing a schedule of values of the process variable is missing. One possible correction of the upper and lower control limits is as follows: part 3sigma is divided by the square root of the number of data points used to calculate each average. According to this formula, the upper and lower control limits will be calculated as follows:

where N=(cycle control)*(60)*(samples per second)

Here, the control loop is equal to the number of minutes for which the calculated average value and standard deviation. The default value of 15 minutes can be used. Samples per second based on the sampling frequency of the device, taking the dimensions{measurement}, which, for example, 10 to Rosemount 3051F transmitter and 22 for the transmitter Rosemount 3051, although other sampling rates could be used.

Further, the application 40 may create an S-graph, which contains a plot of the standard deviation from the time limits of the specifications and control limits. In this case, the upper and lower control limits and the limits of the specifications can be determined is received as follows:

where ΔHV- user-defined upper limit on the change in percent

ΔLD- user-defined lower limit of the dynamics, and ΔLD<0.

Example S-chart 190 shown in Fig. Here presents a plot of standard deviation against time (line 192), upper and lower control limits (lines 193 and 194, respectively, upper and lower limits of the specification (lines 195 and 196, respectively). In the example on Fig standard deviation of the process variable crosses the upper and lower control limits many times and crosses the upper limit specifications noticeable number of times that potentially indicates the current or likely future availability of emergency.

Next, based on the collected data, the application 40 may determine other statistics or values. For example, the application 40 may calculate the index or measure of the distribution for the variable x, which can be any statistical variable, for example:

The application 40 can calculate the index or probability indicator:

and can calculate ratios is NT correlation between two variables (which may represent a statistical variables):

In another example, the correlation coefficient between two variables can be calculated as follows:

Of course, the application 40 view can perform in the system, and other calculations for any variable or variables (including statistical variables, as well as process variables) according to the requirements or needed to detect one or more emergency situations at the facility. Thus, for example, an application 40 or some routine can perform principal components analysis, regression analysis, analysis using a neural network or any other analysis of one or more variables in the collected data to detect and prevent an emergency situation. Generally speaking, the graphics on Fig, 14, 16 and 17 based on the schedule of dependence of one or more parameters of the SPM from time to time. However, the application of 40, you may create graphics that reflect or illustrate the correlation values between one or more variables SPM without regard to time. In one example, the application 40, you may create a scatter diagram, which is a graph showing the dependence of one parameter SPM from the other. The application 40 view or the user is predelete the correlation coefficient, which shows how strongly correlate two parameters SPM (or some combination of the two parameters SPM). On Fig shows a graph 200 of the dispersion, which contains a plot of the two middle settings SPM relative to each other. In General we can see that two middle proportionally correlated, as indicated by a simple rectilinear nature of the line of points of the dispersion (i.e. raising one medium other medium also tends to increase). Point considerably removed from the main areas of variation may indicate a potential problem in the enterprise.

Of course, the app features 40 of view are not limited to creating two-dimensional scatter diagrams, an example of which is shown in Fig. In fact, the application 40 of view can create scatter plots in three or more dimensions by plotting the dependence of three or more parameters SPM from each other. For example, on Fig shows a three-dimensional chart 210 scatter, which is a graph of the dependency of the three parameters SPM from each other and, in particular, the average values of the three process variables relative to each other. On Fig shows a matrix 220 four-dimensional scatter plots illustrating the correlation between the four parameters of the SPM. Essentially, the matrix 220 scatter plots contains 16 different is cnyh two-dimensional scatter diagrams, moreover, in each of these 16 charts scatter has a graph of one of the four parameters SPM another option from this set of four parameters SPM. Here the user can quickly proanalizirovat correlation or relationship between the various parameters SPM and try to detect the current emergency situation or to predict the possible occurrence of an emergency situation at the facility.

Scatter plots on Fig-20 also differ from other known scatter plots of the fact that these scatter plots reflect the average of one or more variables, but not the actual data points, process variables. Therefore, reduced noise, usually present in process variables, which allows to obtain smoother and more visual description data. In addition, the application 40 is not limited to scheduling only average values and can create a graph of the relationship between other statistical variables, such as standard deviation, median value, and so Forth, the application 40 may schedule different types of statistical variables, such as average value and standard deviation in relation to each other, as well as combinations of statistical variables, such as the standard deviation divided by the and the average value for one process variable based on the average values of another variable of the process. For example, the application 40 may create a graph of the mean, standard deviation, change, average change standard deviation or any mathematical combination of these variables SPM for any block SPM controlling a process variable.

Optionally, and as generally noted above, the application 40, you may calculate or determine the correlation coefficient for any pair of parameters SPM using any standard or known methods of calculating the correlation. If the correlation coefficient is close to 1 (or -1)indicates a strong linear correlation (or negative linear correlation between the two parameters SPM. For a set of more than two variables SPM can be determined the correlation matrix, with each element in the correlation matrix determines the coefficient of correlation between some parameters of the SPM. On Fig shows part of the sample matrix 230 correlation, contains the correlation coefficients between average values for at least nine sensors in cascade cycle manufacturing enterprises.

From the matrix 230 correlation Fig you can determine which parameters SPM most strongly correlated with each other. Obviously, the matrix of numbers, similar to that shown in Fig is difficult to explain. However, the Annex 40 may display this matrix in the form of a three-dimensional histogram, such as histogram 240 on Fig. In a three-dimensional histogram on Fig clearly shows where the highest correlation values. Of course, the application 40 may display the correlation matrix and other graphical means, such as wireframe drawing, contour graph, etc. which will be seen the highest correlation values.

In one of the examples shown on the screen 241 Fig, the application 40 view can display a graph of the correlation, illustrating the deviation between the set point correlation in the desired state of the process and set point correlations in the current or undesirable state of the process. So, on the screen 241 Fig presents the first graph 242A correlation, showing a set of correlated points (marked as X) for the desired process state, and the second graph 242V correlation, which displays the same set of correlated points for the current state of the process, showing, thus, the deviation between the correlation parameters for the desired process state and the current state of the process; this deviation may indicate abnormal (emergency) situations in this process. Here each point correlation, denoted as X, is the value of correlation of at least two different parameters of the SPM or the same block SPM, Lieb is different blocks SPM. Of course, as shown in Fig, for one or both States of the process can be built schedule baseline mean value µ and the base of the standard deviation σ.

Similarly, as shown on the screen 243 on Fig, Annex 40, you may create a correlation matrix with the color coding in which the value of a particular point correlation will be marked a certain color from a set of different colors depending on the magnitude of this value. This chart correlation makes it easier for the user to analyze the correlation between different parameters of the SPM and, therefore, the detection or prediction of a future emergency situation at the enterprise. It is also necessary to understand that the correlation matrix can be defined and depicted in the graph for other types of parameters SPM (not just averages), for mathematical combinations of SPM and also for different types of parameters SPM.

Further, in addition to or instead of the above described forms, the application 40 may represent SPM data in other forms. For example, the application 40 may create visual graphs or charts in the form of three-dimensional graphs of trends over time on the X-axis, the mean value and standard deviation of the SPM block Y, and Z three-dimensional histogram that contains graphics srednjeg the values and their standard deviations in X and Y, and the quantity of each Z-axis, three-dimensional graphs of trends over time on the X-axis, the mean value and the standard deviation of the SPM block Y and Z, and showing the upper and lower control limits and/or limits of the specifications for the mean, standard deviation, or both. Of course, there is a virtually unlimited number of ways to visualize data SPM, and the present description is not limited to the specific described methods.

On Fig shows the screen 244 graph that can be generated by the application 40 view to provide the user with the possibility to compare graphs of different variables, such as parameters SPM or related variables or data, for example, the measured and predicted data. In this case, section 245 of the screen 244 graphics can provide the user with a means of selecting a specific graphics data displayed in the graphics section 246 of the screen. The user may, for example, choose to view a graph of the measured data (for the device selected in the hierarchy on the same screen), the predicted data (for example, data generated by the model), residual data, etc. on the same chart. The user can also initiate a discovery operation offset on the chart and/or viewing threshold measurements in section hat. In the example on Fig the user has chosen to view a graph of the measured data (which can be a SPM data or raw data process variables) in combination with the predicted data for viewing shift or transition between analyzed by the process state and the predicted state of the process. Of course, the application 40 may provide the user with a choice of other variables and data (SPM data and data process variables) to build a joint graph to view other relationships.

In another example, the application 40, you may create a trend graph of two (or more) different parameters of SPM on the same graph to provide the user the ability to view the expected or unexpected behavior of one of the parameters SPM relative to another (other) settings. This graph 250, shown in Fig, characterized in that two parameters SPM the lines 252 (for valve) and 254 (for the transmitter). In this example, the user or engineer can expect regular divergence of the two parameters SPM, followed by the convergence of these two parameters SPM to a certain limit, for example, shown by vertical lines 255 and 256. However, if the discrepancy between these two variables occur until the convergence to the limit, in the example, as shown by vertical lines 257 and 258, the user or engineer will become clear that there is a problem or in the future will be an emergency situation.

It is assumed that the correlation parameters SPM can give some idea about the General condition of enterprises, parts of enterprises, parts of equipment, etc. When a company (or part of the company, part of the equipment etc. is in normal working condition, some variables can be quite correlated with other variables. Over time you can change any of the values of the correlation. Change (deviation) some values of correlation may indicate that the company is not working with the same performance that earlier. Therefore, following are some examples of ways to visualize changes one or more values of correlation for some period of time.

To view the changes in the correlation values for any period of time, the correlation value may be calculated at different times. To generate the correlation values of the data from the entire available range can be used, for example, equation 11 or equation 12. In addition, the data can be divided into segments of a certain length (for example, 30 minutes, 1 hour, 6 hours, 1 day, 7 days a certain number of times, etc), then for each of the th segment can be computed one or more values of the correlation. Thus, if the correlation value from one segment to the next has changed, this can be interpreted as a change of correlation values over a given period of time. In another example, correlation values can be generated based on a rolling window of data, while the sliding window has a certain length (for example, 30 minutes, 1 hour, 6 hours, 1 day, 7 days a certain number of times, and so on).

On Fig approximate graph 260 change one of the correlation values with the passage of time. On Fig approximate graph 262 multiple values of correlation with the passage of time. As seen on Fig, the graph becomes overloaded, because on the same graph is greater than the values of correlation. Therefore, the following describes additional exemplary methods of the visualization data associated with the set of correlation values.

In one example creates a graph of correlation values. For example, you can create a graph of correlation values from the initial values, previous values, basic values, normal values, expected values, etc. In this example, any change can be expressed as the relative change (e.g., interest) or as an absolute change. The base value for this correlation values, as a rule, should be calculated based on the number of ex is breaking the data based on the amount of data variables needed to generate the data underlying the correlation values. For example, the average value can be generated on the basis of the data segment length is only 5 minutes or 1 day. Currently, it is assumed that the correlation value from the average data using at least 30 medium data points provides a statistically reliable sample. (It should be understood that in some embodiments implement a statistically reliable correlation value may be provided less than 30 average data points, or, alternatively, may take more than 30 secondary data points.) In this case, if the average data points are selected at intervals of 5 minutes, the window correlation should be about 3 hours or more.

In some embodiments, the implementation of the generation of secondary data includes the period of preparation before storing the first average value. In this embodiment, the algorithm used to generate the average value includes the attempt to define the base of the middle line for the process. The existence of the base of the middle line can be determined by checking that the mean and standard deviation of two consecutive data sets are within a certain tolerance of each the t other this condition can guarantee that the underlying mean value will be maintained during the period of time when the process is in a stable state, and not be maintained when the process is in a transitional state. After defining the reference average value, the algorithm starts to calculate and transmit the average values, which can be used by other algorithms, processes, etc. These average values can be used to calculate the correlation values. Thus, the process can be in steady state and in the normal operation condition when calculating algorithm of the first average values.

In one example, the first correlation value is calculated once defined the basic middle line is selected as the base correlation. As described above, when calculating the first correlation values, the process can, in many cases, to be in stable condition and in good working condition.

In some cases, however, with constant use of the first correlation values as "normal" values may cause problems. For example, the process may be such that even in the normal state, the correlation coefficient varies in the interval from one block correlations to the next. This applies particularly to the case of the EU and two variables, by definition, have a very low correlation. In addition, if configured too large or too small loop control unit SPM, which generates an average value, or if the process was not in a normal state when "learning" algorithm generating medium, the first correlation value may not be sufficiently adequate assessment of normal value.

Therefore, in some situations as the base correlation values may be optimal use of correlation values, different from the first correlation values. In addition, it may be stated that the base value of the correlation should not get out, or as the base correlation values must be chosen for some absolute value (for example, 0), for example, in cases where correlation values are relatively small and/or irregular.

The following are some approximate methods of deciding whether to use the first correlation value as the base value. In one example can be generated by differences between the first correlation value and one or more subsequent values of the correlation, showing compatible if the first correlation value and subsequent values of the correlation. If the first correlation value is different from the subsequent values of the correlation to a certain extent, may be that is the first correlation value should not be used as the base value. In one specific example, the first correlation value is compared with the second correlation value. If the first correlation value is different from the second correlation values is smaller than a certain degree (for example, 1%, 2%, 3%, 4%, 5%, 6%, 7% etc), then the first correlation value may be selected as the base value of the correlation. If the difference is greater than a specified level, the first correlation value is selected as the base correlation values. To determine whether the first correlation value is used as the base value can be used in other ways.

In one example, the base value can be generated on the basis of the generated correlation values (for example, by calculating an average of the correlation values, use the median correlation values etc). In other examples, the base value may be generated based on one or more of the generated correlation values from other similar process, based on simulations based on models, etc.

After determining the initial values, previous values, basic values, normal values, expected values, etc. for each of the correlation values can be calculated array deviations of the correlation values. Array discard the deposits of the correlation values may include values of the deviation (difference) between each of the correlation values and the corresponding initial value, base value, "normal" value, expected value, etc.

The deviation can be expressed in the form of any relative changes (for example, in the form of percentage or absolute changes. As in the conventional methods for calculating the correlation values are generated correlation values between 0 and 1, the absolute change must also lie between 0 and 1. On the other hand, if the magnitude of the change in percent, this percentage changes can be potentially very large, especially if the correlation with the baseline value close to 0. However, situations may arise when using percentage changes optimally and/or is more preferable than using the absolute value of the change.

On Fig shows the approximate schedule 264 of the correlation values and the base values, depending on time. Schedule 264 allows the user to see the deviation between the correlation value and the baseline value as a function of time. However, if you add on the chart 264 larger number of the correlation values and the basic schedule of values could be overloaded.

On Fig shows a sample screen 266 matrix of deviations of the correlation values from the corresponding baseline values. In this example, the correlation values for which no definition is found, the base value, matrix cells are left blank. Alternatively, these cells of the matrix could be filled by any indicator that indicates that the corresponding correlation values did not have baseline values.

On Fig shows a sample screen 268 of the matrix of variance of the correlation values from the corresponding baseline values. On the screen 268 deviation of the correlation values shown color squares, where the color of the square represents the degree of deviation. For example, if the absolute value of the deviation is less than 0.2, the square has the first color. If the absolute value of the deviation exceeds 0.4, the square assigned to the second color. If the absolute value of the deviation lies between 0.2 and 0.4, the square has a third color.

On the screens 266 and 268 on Fig and 31 displays the deviation of the correlation values for a single time or one segment of time. In other examples, the screens can be modified for presentation to the user the possibility to view the variance of the correlation values for a variety of moments or periods of time. For example, can be implemented UI mechanism (scroll bar, arrow keys, etc. that allow the user to view variances in different periods or segments of time. For example, on the screen 268 on Fig include a panel 269 navigation, prednaznachen the I to display the deviation of the correlation values for different points or periods of time. In addition, the screens 266 and 268 may have a UI mechanism with "animated" a view showing the change of variance over multiple points in time or time segments. Similarly, the screen 264 may also have a similar mechanism of the user interface that allows the user to view different time periods.

Further, multiple values of the deviations of the correlation can be combined to generate a value that reflects the deviation of the multiple values of correlation. This value can be shown on the graph depending on time. Combining several values of the deviations of the correlation can be performed in various ways. For example, the set of values of the deviations of the correlation can be considered as a vector, and the norm of such a vector will reflect variations in the correlation values. Below are three equations for computing the norm of the vector. The norm can be calculated by any of these equations, or another equation.

Rule 1:

Norm 2:

The norm for infinity:

where ΔC - i-e value differences correlation, N is the number{number} value the differences of correlation. The factor 1/N in equation 13 and multiplierin equation 14 if necessary, you can put the ü. Can be used, and other equations.

On Fig shows an exemplary graph 270 according to normal values 2 (equation 14) from time to time, while the normal value 2 corresponds to the set of values of the deviations of the correlation. On Fig shows a sample screen 272 displaying the matrix 273 deviations of the correlation values for a set of deviations of the correlation for a particular segment or time schedule 274 according to normal values 2 sets of deviations of the correlation time. Screen 272 may also include a mechanism in the user interface (for example, a scroll bar, buttons, etc)that allows the user to view the matrix 273 deviations of the correlation values and/or graph 274 for different moments or periods of time. For example, the screen 272 contains a navigation panel 275. In addition, the graph 274 may include an indicator indicating the time or segment time on the chart 274 corresponding to the matrix 273 deviations of the correlation values. Finally, the screen 272 may include a mechanism user interface with support for "animation" matrix 273 to visualize changes in the variance of the correlation matrix 273 for several moments or time segments.

As indicated previously, the correlation value may reflect the degree of linear correlation between two variables. Is correlati which can be defined as the result of the operation of the linear regression on the data set. In the General case of a linear regression determines the line that "best" fits the data set. The results of the approximation linear regression often represent the slope of a line and the intersection of the line with the y-axis the Slope of this line and/or a change in the slope of this line over time may be indicative of the normal control state enterprises, parts of enterprises, process, equipment and/or for detection of an emergency situation. If there are two data sets X and Y, the slope of the line of best approximation can be calculated by the following equation:

where xi- the i-th sample data set X, yi- the i-th sample data set Y, x - average values of samples in the data set X, y - the average values of the samples in the data set Y, N is the number of samples in each of the data sets X and Y.

The correlation value and the corresponding tilt can be shown visually by plotting on a graph in polar coordinates. In this case, the absolute value of the correlation corresponds to the radius, and the angle is defined as follows:

where m is the slope defined by the equation 16 or another equation. The arctangent function has range (-π/2, π/2). Thus, when using this method point correlation will be located only is on one half of the polar plane. If necessary, in order to use the entire plane in polar coordinates, you can use the equation:

In this case, the angle shown on the chart, will not be the exact slope of a line. This may be acceptable if the user is more convenient to view the graph in this form. On Fig shows an example of the image correlation values and the angle corresponding to the inclination of the line of best fit on the graph 276 in polar coordinates.

On Fig shows a sample screen 278 with the correlation values and angles in the graph in polar coordinates. On the screen 278 in the center there is a correlation near zero, on the outer side there is a correlation of about 1. Thus, points in the outer ring are the points with the highest correlation, while the points in the Central circle are the points with the lowest correlation. Rings can have different colors for clarity, showing different levels of correlation. Screen 278 may also have a mechanism of the user interface (e.g., scroll bar, buttons, etc. that allow the user to view the graph for different aspects or segments of time. For example, the screen 278 has a navigation panel 279.

In another example, the graph in polar coordinates can be displayed deviation m is the correlation value and the base value. In this example, the deviation of the correlation values is calculated as the absolute value of the difference between the correlation value and the base value, and the angle represents the angle of the correlation values calculated, for example, using equation 18. Thus, the correlation values that are close to its fundamental value, will give the variance of the correlation, located in the center of the graph. If the correlation value significantly deviates from its reference value, it will give the deviation correlation, located away from the center of the graph. On Fig presents a sample screen 280 with the schedule of deviations of correlation in polar coordinates. Rings on the screen 280 represent different levels of the deviation of the correlation values from its basic value and can be color coded. On the exemplary screen on Fig Central ring 280 is deviation correlation less than 0.2. The middle ring represents the deviation correlation less than 0.4 and equal to or greater than 0.2. The outer ring represents the deviation correlation less than 0.6, greater than or equal to 0.4. In another embodiment, may use a different number of rings and their other radii. Screen 280 can also include a mechanism in the user interface (for example, a scroll bar,buttons, etc), allowing the user to view the graph for different aspects or segments of time. For example, the screen 280 has a navigation panel 281.

In some cases, polar graph, such as Fig and 36 may present a graph of the correlation values or Delta values of correlation for a set of points or segments of time on the same graph. For example, the values of the correlation or deviation correlation for different moments or time segments can be connected by lines (which may, arrows), and the user will be able in a visual form to see the changes of the values of the correlation or variance correlation over time.

Screens, for example, depicted in Fig and 36 can be combined with other screens that helps the user to control the normal state of the process. For example, on Fig shows the display 241, which shows the polar diagrams. Statistical data described above with reference to 11-36 (for example, mean value, standard deviation, change, average change standard deviation, correlation, variance correlation, the reference line and so on), can be generated by various devices on the processing plant, such as field devices, I / o, controllers, processes, working with the ancii, servers, device history data, etc. for Example, average values can be generated by the field devices and the correlation between these average values can be generated on the workstation. In another example, mean values and correlation of average values can be generated by the field devices.

The application of 40, you may provide the user or engineer some or all of the views described above, which allows the user or engineer to detect the presence or suspected occurrence of an emergency in the future at a manufacturing plant in the manual mode; on the other hand, an emergency situation can be detected automatically based on SPM data by application 42 development and execution of the rule engine. One possible embodiment of the application 42 development and execution of the rule engine figure 1 and 2 in more detail is shown in Fig. According pig, Annex 42 development and execution of the rule engine includes a mechanism 290 rules, which can be a expert mechanism of any type on the basis of rules and a set of 292 rules that can be stored in the database (for example, in memory W figure 2) and can access mechanism 290 rules. The mechanism 290 collects rules or monitors statistical data monitoring process (specified in the loc 294), for example, in the 43 data in figure 1 and 2, a field device, the server 89 connection in figure 2, the Manager of archival data, etc. of Course, these data SPM can contain any of the data described previously and obtained by, for example, the application 38, and any other data generated by the manufacturing enterprise, including data SPM data and process variables. In other words, the mechanism 290 rules may receive SPM data and other various data types, including, for example, configuration data process, data management strategies, output control, data variables, historical data, data modeling, data optimization, warnings, alarms, emergency/signaling information management, data management documents, reference data and data instructions, data equipment with rotating parts, laboratory data analysis, specific data production, data management environment, etc.

The mechanism 290 rules applies rules 292 to SPM and other data to determine the presence state that indicates, according to at least one of the rules 292 that the user needs to send a warning or alarm (indicated by block 296). Of course, if necessary mechanism 290 rules can perform other actions in addition to the message is an emergency signal, if the rule indicates that a problem exists. Such actions may be, for example, the termination of one or more components involved in the process, change management to change management process, etc.

Further, the application of development regulations, or subroutine 298 enable the user to develop one or more rules of the expert system (for example, for use as one of the rules 292) on the basis of sample statistics and correlations, and thus, detection of known deviations in the enterprise, module, device, cycle control, etc. Thus, while at least some of the rules 292 used mechanism 290 rules can be pre-installed or pre-configured, application 298 development of the rules gives the user the ability to create other rules-based events the controlled production enterprise. For example, if the user knows that a certain combination of emergency conditions or events SPM indicates a problem in the process, they can use the application 298 development of rules and generate a corresponding detection rule this state and, if necessary, generate an alarm or alert or run the AC the CSO or other action based on the detected presence of this condition.

Of course, in the process of manufacturing enterprise mechanism 290 rules, for which the configured receive SPM data (and any other required data), applies rules 292 for the detection of an event on any of these rules. If based on one or more rules 292 issue has been identified in the process, the operator of the enterprise may be issued a warning; it can also be sent to another appropriate person. Of course, if necessary, various rules for the detection of various emergency conditions at the facility and process management can be part of the mechanism 290 rules real-time expert system that can search for samples, correlations of data and parameters SPM to detect developing emergency status.

Further, some of the data that can be used by the mechanism 290 rules 290 represent the state of the SPM, which can be found on the devices that generated the data SPM. In this case, the mechanism 290 may be a client system or may be part of the client system, which reads the parameters of SPM and condition of the device, for example, through the OPC server. As described above, these parameters SPM can be stored in a database for subsequent use, for example the compilation of g is of Afik dependence of average values and standard deviations of time. In any case, if the average value or standard deviation of the process variable changes by more than a user-defined value, the SPM block can detect an emergency condition, such as a change of the average value, a significant change or low dynamics. While reports of such emergency conditions can be communicated to the client system, for example the mechanism 290 of the rules, together with all statistical monitoring data collected from these field devices.

Then, if the engineer or other user has the information that if a particular combination of changes in process variables in a certain way must be submitted by a specific alarm or certain action is performed, the engineer can use the subroutine 298 define rules and set a rule to detect this situation, knowing that the application of this rule will cause an alarm signal upon the occurrence of a specified set of conditions. In one example, the application 298 rule definitions can create a configuration screen that allows the user to create one or more rules "if-then" or Boolean type, which will be stored in the database 292 these rules. An example of one possible configuration screen 300 shown in Fig. In particular, the screen 300 con is Horatii contains a section 302 name, in which the user specifies the name of the rule, section 304 of the conditions in which the user specifies the condition "if" rules "if-then", and section 306 of steps in which the user specifies the action that will occur if it is discovered that it was a condition "if".

In the specific example on Fig the new rule has a name "Boiler 1 Check". In addition, as shown in Fig, section 304 of conditions includes a set of separate conditional descriptions, each of which includes a display device 310 (which is the SPM block, transmitting the data to the SPM used in the description of the conditions), the name of the block 312 SPM (definition of concrete block SPM within the device that needs to transmit data SPM), type 314 SPM data type definition (data transmitted by the unit SPM), description 316 comparison (definition of mathematical comparison operations for SPM data) and section 318 value (determination threshold or value, with which to compare the incoming data to the SPM by describing 316 comparison). Next, at box 320, the user can select or define the operator Boolean logic, such as the AND operator or the OR operator, which will be used between each set of descriptions of conditions to determine how the logical combining these descriptions of conditions at the job complete "if"condition. On figv as possible options are shown only Boolean operators And and OR, can also be used any other Boolean operator (or other desired type of operator)that will give the user the ability to create more complex rules. Next, there is a set of flags 322 and 324, which allow you to define groups of descriptions of the conditions. For example, the selection flag 322 (before the opening parenthesis) indicates the beginning of a new set of descriptions of the conditions specified inside the parentheses, while the choice of flag 324 (before the closing parenthesis) indicates the end of the set of descriptions of the terms inside the parentheses. As will be made clear hereinafter, descriptions of the terms inside the parentheses are combined by a Boolean operator between them to combine descriptions of the terms (or group of descriptions of conditions within different combinations of parentheses.

Thus, in the example in Fig sets the rule, according to which: (1) if the average value (measured by the SPM block 1 in the device RT-101) is less than or equal to 102, And the standard deviation (measured by the SPM block 3 in the device RT-102) is greater than or equal 1.234, OR (2)if the status setting unit SPM 2 FT-201 is equal to the change of the average value, And the status setting unit SPM 4 FT-201 is equal to the change in the mean, there must be applied the action specified in section 306 steps.

As shown in Fig, section 306 of action includes the section 330 name custom alerts section 332 to identify the severity and section 334 of the description. In section 330 name user warning sets the name associated or assigned to the alert that is generated if the detected condition in section 304 of the conditions in section 332 to identify the severity specifies the severity of the alert (e.g., denial, service, communications or other type of warning), section 334 description sets the description associated with the alert, which may be issued to the user or viewer warnings. Of course, while in section 306 steps on Fig sets generated by the warning in this section 306 steps can be also (or instead) specify other actions to be performed, such as shutting down the device, module, etc. in the enterprise, switching or changing the management company, transfer the new value of the parameter or condition management controller in the enterprise and so on will be clear that when you create and save a set of rules in the database 292 of these rules on Fig can use the expert system 42 development and implementation, which will be automatically to detect deviations in process on the basis of data or emergency conditions reported by SPM blocks at the manufacturing plant in which the process of production of the enterprise. Of course, it will be clear that the system 42 may operate or be in the process of production of the enterprise is continuously or periodically to detect emergency conditions at a manufacturing plant on the basis of rules in the database 292 these rules.

If necessary, the system 42 may present a screen view that provides the user with information about the current configuration and status of the mechanism 290 rules on Fig. An example of such a screen is shown Fig. In particular, the screen 340 Fig contains discovered hierarchy ADB 110 (as described initially on 6 and 8), as well as the summary 115 SPM data, as described on Fig.

In addition, the screen 340 Fig contains a section 342 summary of the rules, which lists and describes some information about the rules that have been defined for the mechanism 290 rules and implemented them. In the example on Fig were determined at least three rules, and in section 342 summary of the rules provides information about the devices used by each of these three rules, and the type or severity of alerts generated by each of these three rules. On Fig also shown that in section 344 summary alarm the display shows any of the warning, which was activated or sent by a mechanism 290-based rules defined in the rules. In example f is g active at the moment two warnings - System 2 Failed and Boiler Needs Service. These warnings were generated by the mechanism 290 rules on Fig on the basis of rules that are not shown explicitly in section 342 summary, but if necessary could be seen in the section 342 summary after scrolling down.

As will be made clear hereinafter, the hierarchy 110 main tree and summary 115 available SPM blocks may be provided by methods described with reference to figure 4. Similarly, each rule in the summary section 342 of rules can be created by the user through a configuration screen similar to the screen on Fig. In addition, a warning appears if any of the conditions in the status section of SPM blocks matches any of the specified rules. Of course, it will be clear that the user, if necessary, can use predefined rules for known deviations, modify existing rules to new conditions or to create an entirely new rules.

On Fig and 41 presents other examples of how to create rules or definition screens. For example, on the screen 350 rule definitions presents a simple ("Simple") media type definition Boolean rules, in which there is a set of descriptions 351 of conditions, each of which has a first element 352, which determines the controlled variable or parameter SPM, condition 354 validation or comparison (which m is can be any mathematical operation or test) and another element 356, which can be any process variable or parameter SPM. Each of these elements if necessary, you can manually enter or select in the drop-down menu. In a similar way, like the screen on Fig, may be determined by a Boolean operator, which brings together all the descriptions 351 conditions, and section 360 of the results, you can specify the name of the alert, its severity and the message that will be issued to the user in the composition alerts if a certain condition is met IF ("if") will be executed.

On Fig shown advanced (Advanced) media type 370 rule definitions, which includes a section 372 IF the content of which you can enter/built by pressing different buttons 374. Button 374 may contain or allow the user to determine the type or specific parameter (for example, ADB, parameter SPM, condition or parameter process variable process variable, PV, and so on), a Boolean operator, numbers, and mathematical expression/equality used to create more complex conditional IF statement in section 372. Section 376, which are section determine the name of the alert section to identify the severity and category of messages that can be used to set the warning or alarm signal generated by the rule. Of course, the application 40 may allow the use shall be any other way to define rules implemented mechanism 290 rules for the monitoring of the appearance or the prediction of emergency situations.

Further, through the screens on Fig, 40 and 41, the user can specify a Boolean rules such as "if-then", but together with them or instead of them can be identified and other types of rules. For example, the screens on Fig, 40, and 41 can be appropriately modified or may provide additional screens that will allow you to define rules table type (for example, rules similar to the rules in the software, spreadsheets, Microsoft Excel®), fuzzy logic rules, the mathematical relationships between the parameters, the generation of correlations, the filtering parameters (e.g., low-pass filters, high-pass filters, bandpass filters, finite impulse response (finite impulse response, FIR)filters infinite impulse response (infinite impulse response, IIR), etc. and etc

In operation, the mechanism 290 rules on Fig can be used many different ways of mapping the state of the SPM blocks with predetermined rules in the database 292 these rules. If the rules in the base 292 of these rules is relatively simple, the mechanism 290 can be simply programmed the corresponding logical handlers. On the other hand, if some of the rules are quite complicated, more preference is sustained fashion may be the ready-to-use tool expert system.

As will be clear after the start of the control process all rules are transmitted to the mechanism 292 rules on any interface. Thereafter, whenever the event state changes SPM, for example, detected by blocks 132 or 134 in figure 4, these conditions are communicated to the mechanism 292 rules. Then the mechanism 292 rules in each interval determines whether the conditions of any of the rules. If any of the rules corresponds to a situation mechanism 292 rules returns to the host a message, causing the user may be issued a warning or performed some other action based on the descriptions of the actions this rule is executed.

On Fig shows a sample screen 380 for parts production enterprises and the screen 382 alarms. The mechanism 290 rules may display a screen 382 alarms in case if one or more of the relevant rules. On the screen 382 alarms can be displayed proposed corrective actions, refer to the procedures on the company links to view performance data/quality etc. On the screen 380 may also be displayed region 383 highlight different areas on the screen, indicating devices, circuits, measurements, etc. associated with the alarm is. The mechanism 290 rules may, for example, send the data to the application 40 of view, which provides the output screen 382 alarms and areas 383 selection.

On Fig shows another exemplary screen 384 for part of the production company, containing information warnings and alarms. In particular, graph-385 on the screen reflects the different statistical parameters associated with the alert/alarm signal. Screen 384 may also contain information window 386 and 387, which displays information associated with the alert. Different levels of importance in the information Windows 386 and 387 may be indicated, for example, color coding. The mechanism 290 rules may initiate the display Windows 385, 386 and 387 in case, if the condition of one or more relevant rules. The mechanism 290 rules may, for example, to send data to the application 40 of view, which provides the output window 385, 386 and 387.

On Fig shows another exemplary screen 390 for part of the production enterprises, the display 390, containing information warnings and alarms. On Fig shows another exemplary screen 395 for part of the production enterprises, the display 395, containing information warnings and alarms.

The above describes a mechanism 292 rules, but the stage is the implementation to him or instead can be used with other types of mechanisms analysis. As examples of the types of analysis that can be used in this case can lead to mathematical computing mechanism (for example, a computer system Mathematica® production Wolfram Research, MATLAB® production MathWorks, etc.), an analytical tool with fuzzy logic, the mechanism of matching samples, the neural network, the mechanism of regression analysis, etc.

The above methods of data collection, visualisation techniques and methods of rule engine can be used to collect, view, and data processing SPM not only in the enterprise configuration in figure 1, but in the other configurations. For example, they can be used in the environment of PC-based (for example, DeltaV, AMS and Ovation), in which the software may be directed to different servers (e.g., servers, OCR, web servers, and so on), to obtain the hierarchy of the enterprise, locate in the company and to identify the device with the support of ADB and SPM. Another use case refers to the location directly at the higher protection devices, such as devices Rosemount 3420, which have a built-in OPC server and have direct access to the field devices. In this case, the application of data collection and rule engine may reside on the device itself and is performed without the need to separate the latform, such as a user workstation. In addition, in this or other cases of visual applications or components described may work or may be implemented in other devices, such as handheld devices, PDAs, etc. that can connect to a standalone device and read the data collected SPM, warnings, etc. for viewing by the user. Similarly, application of data collection and viewing can refer to field devices or other devices through the device for remote viewing. Thus, such software may reside on web servers or to be available through them, as, for example, Asset Portal and AMSweb production of Emerson Process Management. In addition, the OPC server figure 2 is shown as separate from the field device containing the SPM blocks, however, the OPC server or another server may be placed directly in one or more field devices. Further, the application 38 data collection and mechanism 42 of the rules of the system of prevention of emergency situations can be located in the same device that blocks ADB and/or SPM that generate data for SPM, for example, in a field device containing blocks ADB or SPM. In this case, the system 35 prevention of emergency situations can work or run on the same device, and that b is Oka statistical without having to use interface ORS (however, the use of ORS is also possible). If necessary, data SPM or warnings, alarms, etc. generated by the application 38 and 42 can be obtained by any conventional methods access data from a field device, such as a connection controller, handheld device, wireless channel, etc.

On Fig shows another way of implementation of prevention of emergency situations at the manufacturing plant, which does not require the use of distributed controllers, hosts, or other more traditional user interfaces to support SPM blocks and functionality prevention of emergency situations. In the system 400 Fig some or all applications 35 prevention of emergency situations and/or applications 38-42 may be located on a device other than the host workstation or personal computer. For example, the system 400 Fig includes a set of field devices 405 (these devices are shown as Fieldbus field devices, but may be a device other types)associated with the interface device 410, which may represent, for example, the Rosemount 3420. In this case, the interface device 410 that is not a personal computer, VK is ucati some or all functions 35 prevention of emergency situations, described above. In particular, the interface device 410 may include a means 412 view, which allows you to retrieve and organize the data received from the field device 405 (which may be the field devices of different types). If necessary, this tool 412 view or the communications device may include a viewer ORS. The application 38 data collection (or part thereof) may also be located and run on the processor in the interface device 410 and to ensure the collection of data from field devices 405, including SPM data, as described above, for any of the field devices with the SPM blocks. In addition, the interface device 410 may include one or more blocks 414 SPM, providing data collection process variables directly from one or more field devices (such as field devices, no blocks or functions SPM), and the generation parameters SPM, as described previously. Thus, blocks 414 SPM, located and operating on the interface device 410, allow to compensate for the absence of SPM blocks in some field devices 405 and can be used to generate SPM data for field devices that do not have their own support blocks SPM or SPM functions.

Further, the application mechanism 42 of the rules (or part of it, for example, the mechanism 290 rules on Fig) may races is to rogatica and work on the interface device 410; the base 43 of the data can also be positioned in the interface device 410. The interface device 410 may communicate with other devices, such as main workstation 430, through hardware connection, for example, 2-wire, 3-wire, 4-wire connection, etc. and transfer the data to the SPM or data processing, such as warnings, graphics, data, etc. on these devices for viewing by the user. In addition, as shown in Fig, the interface device 410 may be connected via one or more radio-frequency connections to the web browser 440 and to a handheld computing device 450, such as a telephone, a handheld computer (PDA), laptop computer, etc. In this example, one or more applications 40 view can settle down and work on other devices, such as the main workstation 430, the web browser 440 or handheld computing device 450, and these applications can interact with the interface device 410 and to obtain the required data for processing and viewing in any way such as any of the above. If necessary, the device 430, 440 and 450 may include an application 298 define rules on Fig that allows the user to generate rules implemented by the rules engine in the interface device 410. Similarly, the AK is shown in Fig, possible indirect access to the data from the interface device from the host 410 430 via the web browser 460 and submitting them to other users through any desired network connection. Of course, the interface device 410 may include a network server and to communicate with any other device, such as device 430, 440, 450 and 460 through any desired Protocol, such as OPC, Modbus, Ethernet, HTML, XML, etc.

On Fig presents further enterprise configuration 500, in which the interface device 410, which may be similar to the device on Fig or the same device, associated with a set of field devices 510 (forming part of the heat exchanger 515) and the system controller 520 of the process. In this case, the interface device 410, which may include all applications and functions of the device 410 on Fig, can provide access to the data to view for the host 530 and may send alerts or alarms generated by the rule engine, the system controller 520. The system controller 520 can combine these warnings or alarms warnings and alarms controller of another type to view, for example, operator control workstation 540 operator. Of course, if necessary, the main workstation 530 moretime any desired application view, used to view the data collected and provided by the interface device 410 by any desired method, including any of those described herein. Similarly, access to this data may be provided for viewing by other users through a web browser 550. Thus, as will be clear from the various applications described as associated with system 35 prevention of emergency situations, can be distributed on different devices and not be under the control interface device. Instead, the processing and collection of data (such as data SPM) can run on a single device, such as an interface unit 410, after which the data is transmitted for viewing on another device. Similarly, rules can be created on the device user interface, such as host, web browser, PDA, etc. and be transferred to another device, such as an interface unit 410, to be executed by the rule engine.

While in the example of figures 1 and 2 of Annex 38, 40 and 42 associated with the system 35 prevention of emergency situations, is shown as located on the same workstation or computer, some of these applications or other objects may be stored and executed on other workstations or computer the devices 10 or associated with it. In addition, the application system 35 prevention of emergency situations can be separated and run on two or more computers or devices and may be configured to communicate with each other via a wired, wireless and/or temporary communication channels. Further, the described system of prevention of emergency situations may include any of the applications 38, 40 and 42 and may, but need not necessarily, include the described blocks ADB or SPM. Further, while in the described examples use the SPM blocks in the form of blocks SPM standard Fieldbus, the term "block SPM" is used herein as descriptive and includes any other types of blocks, statistical process control, program, etc. that collect process data or variables and perform some statistical operation or control, regardless of whether these blocks or programs correspond to the well-known Fieldbus Protocol.

In addition, in the above description mentioned blocks, such as blocks ADB and the SPM blocks that calculate statistics, but you can use other types of blocks of the data collection processing of the signals that generate the data signal processing of other types. For example, it may be blocks of the data collection signal processing that generate data frequency analysis (e.g. the measures data generated on the basis of Fourier transform or other transform of the process variable data, var, data, wavelets, data generated using a neural network, the data generated using fuzzy logic, etc. that can be used in the system of prevention of emergency situations. Thus, used the term "block of the data collection signal processing" means and includes any type of control units, software, hardware, etc. that perform data collection or process variables and performing some operation signal processing or control, such as the generation of statistical data, a mathematical transformation (e.g., using Fourier transform, discrete Fourier transform, fast Fourier transform, short-time Fourier transform, Z-transform, Hilbert transform, Radon transform, distribution Wepner, wavelet transform etc), process data, retrieving information from the converted data process, filtering, extracting information from process data using fuzzy logic, neural networks, methods of autoregressive etc.

Next, have been described examples in which collected and analyzed data signal processing blocks of the data collection is of Ignatov within a single enterprise; on the other hand, it should be appreciated that these methods can be used in the case of many businesses. For example, the data signals may be collected from multiple companies and transferred to the analysis engine and/or the viewer. Examples have been described using specific protocols and methods of communication, however, when accessing configuration data and data signal processing blocks of the data collection signal processing may use different protocols and methods, including the well-known protocols and methods. For example, in the identification and/or configuration blocks of the data collection, signal processing, data collection, signal processing, etc. can be applied to other protocols and methods in addition to ORS. Other methods may include, for example, Internet protocols, Ethernet, XML, proprietary protocols, etc, in other embodiments, the implementation can be used by web servers and/or their own computing devices, such as controllers, processes, input/output, workstations, field devices, etc. in a Similar way, can be applied to other types of data hierarchy, including its own data.

System to prevent non-standard (emergency) situations and applications described herein as associated with the system prevent the treatment of non-standard situations, preferably realized in the form of software, but they can also be implemented as hardware, firmware, etc. and can operate on the basis of any other processor associated with the process control system. Thus, the described elements may be implemented on the basis of standard multi-purpose CPU, specially designed for this purpose hardware or in firmware, such as an integrated circuit for a specific application (application-specific integrated circuit, ASIC) or other suitable hardware devices. In case of implementation in the form of a software program may be stored on any storage medium read by a computer, such as magnetic disk, optical disc (e.g. DVD) or other storage medium, in a RAM or ROM of a computer or processor, in any database, etc. likewise, this software may be supplied to the user or enterprise via any known or desired delivery method including, for example, the disk read by a computer or other transportable computer storage mechanism or over a communication channel such as a telephone line, The Internet is, etc. (which are considered as the same or interchangeable with the delivery of such software through the mobile data carrier).

Thus, the present description of the invention given with reference to specific examples, which are intended only for illustrative purposes and do not limit it in any way; the specialist in the art it will be clear that in the above descriptions may be amended, supplemented or reduction without derogating from the principles and scope of the invention.

1. The method of visual representation of data associated with an enterprise, comprising the following steps:
collect a set of statistical parameters of the control process, generated by the set of blocks of statistical process control related to a set of devices in the enterprise;
define the correlation matrix for a set of more than two statistical parameters of the control process, where each element in the correlation matrix determines the correlation coefficient between some statistical parameters of the control process;
displays the correlation matrix.

2. The method according to claim 1, characterized in that the display of the correlation matrix includes a display matrix of the correlation matrix of numbers.

3. The method according to claim 1, characterized in that otobrazheni the matrix of correlation involves mapping of the correlation matrix in the form of a three-dimensional histogram.

4. The method according to claim 1, characterized in that the display of the correlation matrix includes the display of the correlation matrix as a wire-frame drawing.

5. The method according to claim 1, characterized in that the display of the correlation matrix includes the display of the correlation matrix in the form of a contour plot.

6. The method according to claim 5, characterized in that the display of the correlation matrix includes a display matrix correlation matrix correlation with color-coded, and the specific value of the correlation coefficient indicate a certain color from a set of different colors depending on the values of the pixel correlation.

7. The system of visual representation of data signal processing associated with the enterprise, which includes the following components:
means for collecting a set of statistical parameters of the control process, generated by the set of blocks of statistical process control related to a set of devices in the enterprise;
renderer to determine the correlation matrix for a set of more than two statistical parameters of the control process, where each element in the correlation matrix determines the correlation coefficient between some statistical parameters of the control process, and display of the correlation matrix.

8. The system according to claim 7, characterized in that when estva visualization displays the correlation matrix in the form of a matrix of numbers.

9. The system according to claim 7, characterized in that the renderer displays the correlation matrix in the form of a three-dimensional histogram.

10. The system according to claim 7, characterized in that the renderer displays the correlation matrix as a wire-frame drawing.

11. The system according to claim 7, characterized in that the renderer displays the correlation matrix in the form of a contour plot.

12. The system according to claim 7, characterized in that the renderer displays the correlation matrix in the form of the correlation matrix, color-coded, and the specific value of the correlation coefficient indicate a certain color from a set of different colors depending on the values of the pixel correlation.

13. The method of visual representation of data associated with an enterprise, comprising the following steps:
collect a set of statistical parameters of the control process, generated by the set of blocks of statistical process control related to a set of devices in the enterprise;
define the correlation matrix for a set of more than two statistical parameters of the control process, where each element in the correlation matrix determines the correlation coefficient between some statistical parameters of the control process;
compute a matrix of deviations of the correlation values, and the variance matrix mn the values of correlation includes the difference between each of the correlation coefficients and the corresponding basic value;
display the matrix of deviations of the correlation values.

14. The method according to item 13, wherein the display matrix of the deviations of the correlation values comprises displaying a matrix of deviations of the values of the correlation matrix of numbers.

15. The method according to item 13, wherein the display matrix of the deviations of the correlation values comprises displaying a matrix of deviations of the values of the correlation matrix of the deviations of the correlation values with color-coded, and the value of a specific deviations indicate a certain color from a set of different colors depending on the degree of deviation.

16. The method according to item 13, characterized in that it further involves the implementation of a user interface that allows the user to view the variations in different periods and time segments.

17. The method according to item 13, characterized in that it further involves the implementation mechanism of the user interface with "animated" a view showing the change of variance over multiple points in time or time segments.

18. The system of visual representation of data signal processing associated with the enterprise, which includes the following components:
means for collecting a set of statistical parameters of the control process, generated by the set of blocks with ateisticheskogo process control, related to the set of devices in the enterprise;
renderer used to determine the correlation matrix for a set of more than two statistical parameters of the control process, where each element in the correlation matrix determines the correlation coefficient between some statistical parameters of the control process to calculate the variance matrix of the correlation values, and the variance matrix of the correlation values comprises a difference between each of the correlation coefficients and the corresponding basic value, and displays the matrix of deviations of the correlation values.

19. System p, characterized in that the renderer displays the matrix of deviations of the values of the correlation matrix of numbers.

20. System p, characterized in that the renderer displays the matrix of deviations of the values of the correlation matrix of the deviations of the correlation values with color-coded, and the value of a specific deviations indicate a certain color from a set of different colors depending on the degree of deviation.

21. System p, characterized in that screen renderer is used to implement the mechanism of the user interface that allows the user to view the variations in different periods and time segments.

22. System p, characterized in that the visualization tools are used to implement the user interface with "animated" a view showing the change of variance over multiple points in time or time segments.



 

Same patents:

FIELD: information technology.

SUBSTANCE: method of constructing a mobile repair unit for communication equipment additionally involves identification (determination) of the type of the communication equipment connected to the repair unit. The diagnosis program is selected depending on the type of the connected communication equipment. The communication equipment is diagnosed. The degree of importance (priority) of the repaired part of the communication equipment is determined and unit repair is carried out.

EFFECT: high efficiency of the repair unit for communication equipment owing to diagnosis and repair of communication equipment depending on the type of the equipment.

1 dwg

FIELD: information technology.

SUBSTANCE: in the method of creating control-diagnostic tests before creating the tests through digital shooting in optical range, pictures of the non-component side of the article with clear identification of types of radio components thereon and their position is obtained. For each combination of input test electrical signals, simultaneously with determination of standard values of parametres of electrical response signals from outputs of a standard sample of the article of the given type which is known to be fault-free, standard digital infrared images of the standard sample of the article of the given type are obtained through digital infrared video shooting, where the said infrared images display the differences between thermal conditions of the radio components in places where they are located on the fault-free standard sample of the article of the given type. The obtained data are entered into the computer data base of the control-diagnostic installation and are used for subsequent monitoring of correct operation and diagnosing faults in articles of the given type.

EFFECT: detection of faulty radio components without violating integrity of moisture-proof coating of the article, high efficiency and reliability of diagnosis.

5 cl, 2 dwg

FIELD: electricity.

SUBSTANCE: in device, comprising object of control, set of software-controlled sources of inlet test signals, set of digital metering parametres of response signals, control computer of device, there is additionally introduced digital video camera of infrared range, installed onto object of control with the help of adapter accessory, outlet of digital video camera of infrared range is connected to additional inlet of control computer of device, at the same time adapter accessory provides for fixation of digital camera lens with provision of full view of electric radio elements installed on object of control, and protection against effect of external radiations at results of diagnostics of controlled object faults.

EFFECT: invention provides for detection of faulty electric radio elements without damage to integrity of moistureproof coating of printed circuit boards, increased efficiency and validity of diagnostics.

3 cl, 4 dwg

FIELD: electricity.

SUBSTANCE: in method based on qualification of operable or faulty condition of controlled sample of radio electronic equipment (REE) depending on match or mismatch of measured values of parametres of electric signals of response and reference values of parametres of the same signals for operable condition of this type of REE, additionally infrared image (thermal image) of REE is produced with the help of digital video camera of infrared range with display of temperature conditions of electric radio elements (ERE) on it, as well as sections of electric circuits, and also their location in composition of REE sample, having detected REE faults by mismatch of measured and reference values of parametres of electric signals of response, produced infrared image of REE reflecting actual condition of ERE is compared with previously produced one, and results of comparison are used to determine location of faulty ERE to be replaced.

EFFECT: invention provides for detection of faulty electric radio elements without damage to integrity of moistureproof coating of printed circuit boards, increased efficiency and validity of REE faults diagnostics.

6 cl, 4 dwg

FIELD: electricity.

SUBSTANCE: device to control main parametres and operability of circuit board of control of electronic control system (ECS), current sensor (CS) and switch, comprises unit of supply, receiving part, central processor (CP) and converter of analog signal into digital one (ADC), control unit, unit of testing equipment, comprising tested CS, ECS circuit board and switch.

EFFECT: increased reliability of operation of electronic control system, current sensor and switch in process of operation.

2 dwg

FIELD: machine building.

SUBSTANCE: control system consists of control valve, of valve controller, of control line, of supporting component, of two pressure gauges, of logic, of processor, and of storage for recording threshold rate of change. The valve controller consists of two pressure gauges and is connected to a pneumatic actuator of the valve and to a solenoid valve for testing the valve actuator and solenoid valve under the on-line mode. To test the solenoid valve the valve controller measures pressure at various ports of the solenoid valve by actuating the solenoid valve for a very short period of time. The valve controller determines complete operability of the solenoid valve by derivative of difference between measured pressure signals, i.e. basing on rate of change of difference of measured pressure signals in time.

EFFECT: raised reliability of system functionality.

15 cl, 4 dwg

FIELD: information technology.

SUBSTANCE: apparatus for predicting system technical state has a shift-storage register, a unit for determining polynomial order, a circuit for connecting finite differences, a multiplier unit, a Newton time coefficient circuit, a delay element, a bus for setting the time point, a control unit, a computing unit designed for calculating a signal prediction on the selected model, a memory unit, an analysis and correction unit designed for calculating and correcting the prediction total error on a given algorithm, a unit for displaying prediction results.

EFFECT: broader functionalities of the apparatus owing to coordinated control and high accuracy of the apparatus owing to accounting for total prediction error.

1 dwg

FIELD: information technology.

SUBSTANCE: flight monitoring system has an onboard subsystem for collecting and transmitting flight information, and a subsystem for receiving and processing flight information in a ground air traffic control post. The flight monitoring subsystem has sensors for the state of separate units of the aircraft, conversation sensors, a unit for collecting and converting information, registers for process and conversation information, a unit for monitoring the state of the human operator, a unit for processing and preparing information and a unit for transmitting information. The subsystem for receiving and processing flight information has a unit for receiving information, an information register and an online information processing unit. The unit for monitoring the state of the human operator has human pulse and body temperature sensors, a pulse generator, a toggle flip-flop, a direct voltage source, setup units, a threshold element, pulse formers, recirculating shift registers, delay and comparator elements, pulse counters, AND and OR elements, a computing unit, an inverter, a digital-to-analogue converter, pulse formers, static flip-flops, control result memory registers, control result displays, an alarm (device for signalling deviation of the controlled parametre from the tolerance limits). Deviation of any of the controlled parametres from tolerance limits can be used to prevent accidents.

EFFECT: wide range of controlled parametres, in particular working of the heart of the human operator in real time, reliable operation of the flight monitoring system.

8 dwg

FIELD: information technology.

SUBSTANCE: equipment has a computer, a signal switch connected to not less than three monitoring units designed for connection to the monitored object. Each monitoring unit consists of a control unit, a multichannel comparator unit, a virtual standard, a load switch, a controlled load, a controlled power supply for output circuits, an output signal former, a switch and a standardisation unit.

EFFECT: broader functionalities of the device owing to possibility of measuring the level of the input power voltage in each channel and monitoring with nominal and maximum load, and high reliability of monitoring results.

2 cl, 1 dwg

FIELD: physics.

SUBSTANCE: method of determining a failed angular velocity sensor in a redundant system is based on periodic checking the relationship of measured motion parametres which characterises non-faulty operation of sensors, detecting the moment the relationship is violated, turning the system by a given angle φ0 from that moment, integration of measured parametres during turning, comparing signals received after integration with given signals after turning and determining the failed sensor from the comparison results.

EFFECT: broader functional capabilities of automatic control.

1 dwg

FIELD: automatic control, applicable in systems with excessive quality of transducers, for example, accelerometers, a failure of one of which should not result in a failure of the control system.

SUBSTANCE: the method is based on a periodic check-up of relation between the measured parameters of motion characterizing the correct operation of the transducers, fixation of the moment of failure of the relation, comparison of the readings of the transducers at this moment and at the moment preceding the moment of disturbance of the relation, and determination of the failed transducer by the results of the comparison.

EFFECT: expanded functional potentialities due to possibility of determination of the failed transducer in any excess system.

1 dwg

FIELD: measuring and monitoring technique, possibly monitoring of different objects.

SUBSTANCE: system includes control unit, unit for calling testing programs, coupling unit, measuring unit, test stimulation unit, power sources, unit for distributing signals, memory unit, N matching units, N testing program units. Each testing-program unit has evaluation circuit and two memory devices.

EFFECT: lowered volume of equipment, simplified organization of monitoring process and development of software.

1 dwg

FIELD: electric measurements, applicable in check-up of tram and trolleybus electric apparatuses in the process of manufacture and in service.

SUBSTANCE: current in the current source is fed to the current winding of the current relay from the rectifier via a key, choke, shunt. The device uses a pulse-width modulator that controls the keys, slowly varying voltage is applied to the modulating input of the pulse-width modulator that is preliminarily modulated by the rectifier ripple voltage. Besides, use is made of a sample-release circuit of operate (release) currents and voltages. The signals from these circuits are fed to indicators via analog-to-digital converters.

EFFECT: reduced error of determination of operate and release current and voltage relays, enhanced capacity of check-up in the device due to reduced ripples of the source of smoothly varying current.

2 cl, 4 dwg

FIELD: mechanical engineering.

SUBSTANCE: method comprises determining variations of the parameter during acceleration and deceleration of the actuator. The device comprises generator and OR-NOT unit, the inputs of which are connected with the outputs of the relay. The output of the relay is connected with the input of the generator.

EFFECT: enhanced accuracy of the method and simplified device.

3 dwg

FIELD: instrumentation engineering; serviceability check of multichannel communication systems.

SUBSTANCE: proposed equipment includes personal computer, multiplexing switch, circuit checkup unit, control unit, multichannel comparison unit, virtual standard, switching unit, output signal shaper, multiplexer, and normalizing unit that has voltage meter and circuit meter.

EFFECT: enlarged functional capabilities of device.

3 cl, 1 dwg

FIELD: measuring equipment.

SUBSTANCE: as a source of standard signal not separate generator of test signal according to known code structure is used, but a component of modem, to provide for substantial simplification of process under unfavorable conditions.

EFFECT: higher efficiency.

1 dwg

FIELD: automated control and diagnostics systems.

SUBSTANCE: first variant of complex includes control computer, mating block, commutator, local data exchange main, tests forming block, logical analyzer, signature analyzer, synchronization block, digital oscillographs block, special form signals programmed generators block, programmed power-sources block. Second variant of complex additionally includes block for forming high-frequency test signals and block for measuring high-frequency signals.

EFFECT: broader functional capabilities, higher efficiency, higher reliability.

2 cl, 2 dwg

FIELD: automatic control.

SUBSTANCE: device has first and second analog-digital converters, first and second coefficients forming blocks, first and second multiplication blocks, counter, first and second integrator, control effect forming device, division block, buffer and registering block, while coefficients forming blocks are made in form of digital filters and all remaining blocks of device are made digital.

EFFECT: higher precision, higher resistance to interference.

1 dwg

FIELD: measuring equipment.

SUBSTANCE: device has block for forming control and stimulation signals, block for forming standard signals, multiplication blocks, frequency transformer, phase rotator, commutator, frequencies grid generator, integrators, blocks for square involution, adder, normalization block, key, analog-digital converter, comparison circuits, memory blocks, registers, information output block, interval estimation block (for setting lower and upper limits of trust range for each measured value of mutual difference coefficient of distorted and standard signals) and block for analysis of number of support values of mutual difference coefficient (to exclude from further processing results of measurements, for which within limits of trust interval number of support values of coefficient exceeds allowed limit).

EFFECT: higher precision.

2 cl, 2 dwg

FIELD: technical diagnostics.

SUBSTANCE: method includes, for each set of input test signals, forming of prior matching response signals for intermediate points of controlled device. Received response signals at outputs of product are compared to parameters of standard response signals and level of their match is determined, in case of mismatches broken branch of functional circuit is determined and diagnostics is repeated by substituting all formed combinations of input signals, after that diagnostics of erratic portions is started.

EFFECT: simplified method.

3 dwg

Up!