Method for adaptive forecasting of residual operating life of complex objects, and device for its implementation

FIELD: measurement equipment.

SUBSTANCE: method involves force action on the surface of a test object, recording of a set of electrical signals of input information with the information sensors installed on the test object; signals of the information sensors are determined with changes in force action on the surface of the test object. A use is made of information sensors installed on the test object and receiving changes in parameters of the test object, electronic filters to increase signal-to-noise ratio, which are connected to the outputs of the sensors and connected to the inputs of an electronic analogue circuit implementing a neural network model of reliable operation of the test object; with that, to another group of inputs of the electronic analogue circuit there connected is a unit for supply of a signal for retraining of a model with wears, and to its output there in-series connected is approximation unit of time dependences of sets of electrical signals, a shaping unit of time series of forecasts of reliable operation of the test object, an extrapolation unit of the value of the set of electrical signals at the output of the electronic analogue circuit till a limit value and determination of residual life, to the input of which an extrapolation model setting unit is connected.

EFFECT: improving reliability of forecast results; improving flexibility of the method and enlarging its application scope.

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The invention relates to the field of measurement technology, in particular to methods and devices for diagnostics of technical condition of complex technical objects NDT methods.

The invention can be used to control the reliability of complex systems and materials in the production process and during operation: spatial mesh structures, complex building structures, for example, a complex of municipalities, compartments spacecraft, rocket engines, pipelines, pressurized vessels, complex parts, such as blades of gas-turbine units), etc. Particularly effective application of the claimed invention during testing and operation of potentially dangerous and expensive in manufacturing of constructions and products, to which, on the one hand, there are high requirements for reliability, but on the other hand, they are quite expensive and time-consuming to manufacture to ensure that a sufficiently large number of designs can be tested by the methods of destructive testing, i.e., to destroy. In some cases the destruction of such products is simply not allowed. This requires to identify potentially dangerous areas (components), which can break down (due to the presence of defects, lowered the th strength or other reasons) when it loads, what can cause a crash, and which may need to be strengthened, as well as to determine the reliability of the residual operating life of the designs or products with a certain probability.

The level of technology

The aging of assets, lack of preventive work to increase reliability, the fall production discipline and a number of other reasons have led to a sharp increase in the country of accidents and disasters (the increase in the number of accidents in the system construction, utilities, Sayano-Shushenskaya hydroelectric power station and others).

For example, according to statistics from 366000,0 km of heating mains in the Russian Federation 18,4% need immediate replacement. The number of accidents, failures and incidents on the sources of heat, steam and heat networks was 107539 cases per year, i.e. approximately 3.6 accidents/km a year.

If you consider that the city of Moscow has approximately 4000 km of heating mains, we can say that in Moscow is approximately 15000,0 accidents per year.

Therefore, the development and implementation of a reliable and simple in operation of the method and apparatus of technical diagnostics of technical condition and assess the reliability of such objects with an estimate of their residual life is quite relevant.

Such examples can be privest is quite a lot in different areas of technology and industry.

Recently conducted studies of the main ways of solving this problem. For example, the direction associated with the restoration of the basic funds, showed that it will take about 1 trillion. $ what is not acceptable. Similar results were obtained in some other areas.

One of the most promising ways to solve the problem of increasing the reliability and safety of complex technical systems adopted monitoring (constant in time) an objective assessment of the reliability of their operation on the basis of methods and means of nondestructive testing and technical diagnostics and, in the future, carrying out repair work on the monitoring results.

There are a number of analogues of the methods for predicting resource of technical devices. For example, the method (see the description of the invention to the patent of Russian Federation №2292028 "method for determining the residual life of steel structures", IPC G01N 3/00. Published 2007.01.20) is determined by the number of loading cycles of the studied metal element, and the number of loading cycles for the period of operation between measurements establishes the dependence of the coercive force in the locations of the defects on the basis of which is calculated residual life of steel structures. This takes into account only the Dean indicator - the number of cycles of loading element metal, such indicators as the limiting condition due to the joint reduction of the wall thickness at wear or corrosion, are not taken into account. It is not intended to evaluate the strength of the replaced worn-out elements, accuracy, reliability and completeness of the results of diagnostics of technical devices, which is a significant disadvantage of known methods and means of forecasting resource of technical devices.

Known methods of predicting secondary and assigned resource (see, for example, [RD 26.260.005-91. Methodical instructions. Chemical equipment. The range of indicators and methods for assessing the reliability]), in which the resource is predicted by the mean time to failure overhaul using probabilistic models in reliability theory. The methods find wide practical application due to the lack of reliability of the resource estimation technical devices, because the resource is based on the nomenclature of reliability established for individual sample sites, and does not take into account the results of the resource and strength of research and technical diagnosis, when the known data on the wear of the walls of the replaceable elements, mechanical stresses, defects arising during the operation, and the amount of technical d is of being diagnosed with.

According to the method of predicting residual life of metal parts (see the description of the invention to the patent of Russian Federation №2215280 "a Method for evaluating the remaining life of parts", IPC 7 G01N 3/00. Published 2003.10.27) operational rate of change of residual stresses for the selected zones details define as the ratio of the difference of residual stresses on the second and first control stages to the time between the control stage, and a maximum operating speed of change of residual stresses residual life determined after the operating time between two control stages is not less than 0.05 from the project resource details. Therefore, the known method is of limited use when assessing resource of technical devices, as it is only one indicator - the rate of change of residual stresses in the material studied items.

A known method for determining the robustness of the loaded material (see description of the invention to the patent of Russian Federation №2167421 "method for determining the safety factor of the loaded material, IPC 7 G01N 29/14, G01N 19/04. Published 2001.05.20) with the loading of the material researched two different size loads, and researched material under these loads of acoustic emission method record the acoustic pulses e is issia, measure their quantitative characteristics and of the counting rate of acoustic emission to determine a margin of safety of the investigated material. However, acoustic emission method does not forecast well before destruction; not possible to determine the resource of new products without defects, it is not possible to determine the resource is unloaded products without external influences, such as the bottoms of vertical cylindrical tanks, reduces the residual resource of technical devices in the testing process through the development of cracks in excess of the load [see, for example, in the book. Non-destructive testing and diagnostics. The Handbook. M: Engineering, 1995]. The disadvantage of this method is its limited use in assessing resource of technical devices, as it is only one indicator - safety factor of the investigated material with defects.

There is a method of determining the degree of wear (see the description of the invention to the patent of Russian Federation №2390746 "Method is capable of diagnosing the connecting rod bearings of internal combustion engine", IPC 7 G01M 15/00, G01M 13/04 was investigated, F16C 17/24. Published 2010.05.27) by measuring the amplitude of pulsations of pressure values. The disadvantage of this method is its limited use in the assessment of resource parts, components and elements of technical devices that do not contain pairs mehanicheskij the friction because there is only one indicator of the degree of wear of the largest gaps in friction pairs of machines.

There is a method of determining the degree of wear (see the description of the invention to the patent of Russian Federation №2006811 "Method is capable of diagnosing the degree of wear of the bearings of internal combustion engine", IPC 5 G01M 15/00, G01M 13/04 was investigated. Published: 30.01.1994), which determine the ratio of the difference between values of the first and second pressures to the magnitude of the second pressure and compare the value of this ratio with the reference dependence of the degree of wear of the bearings, and the growth of this ratio corresponds to an increase in the degree of wear.

There is a method of determining reliability of non-destructive testing of defects in determining the quality of manufacturing, reliability and safe operation of the product (see the description of the invention to the patent of Russian Federation №2243565 "Method of determining reliability of non-destructive testing (NDT) of defects in determining the quality of manufacturing, reliability and safe operation of the product, IPC 7 G01N 35/00, G01N 3/00. Published 2004.12.27), test sample with defects located randomly, they control this test sample selected by a method of non-destructive testing, characteristics of the defects set of non-destructive control, compared with the characteristics laid the Def is tov and judge the validity of this method of nondestructive testing. At the same time, the defects in real structures may differ significantly in form, location relative to the coordinate axes of the elements of technical devices from defects in the test sample, so the use of the test specimen with defects is mainly exploratory in nature to assess the reliability of non-destructive testing methods and reliability of non-destructive testing of defects in determining the quality of manufacturing, reliability and safe operation of the product. The known method is of limited use in forecasting resource replaceable elements as having an indicator of the reliability of non-destructive testing of defects in the studied material parts is not possible to estimate the resource of technical devices.

A known method for predicting the residual life of thin-walled shells of technical devices (see, for example, the description of the invention to the patent of Russian Federation №2234079 "Method and device for determination of residual life of thin-walled shells of tank and pipe steels", IPC G01N 27/72. Published 2004.08.10) with determination of residual wall thickness, changing due to corrosion, low cycle fatigue and aging technical devices, sampling the studied thin-walled shells, calculating the current values of coefficients the rate of stock toughness and the safety factor of the thickness of the investigated thin-walled shell, and compare them with previously not negrosensis the sample is representative. The array of remaining average service lives of plots determine 95% of the residual life of one of the most typical site technical devices. In the known method is not considered replaceable worn elements, completeness and quality of the diagnosis, the probability parameter of reliability of the estimates of the reserves of strength, the degree of responsibility (group or hazard class technical devices), describing the likely degree of risk in case of failure or destruction, the rate of corrosion and the corrosion resistance of materials and operating speed reducing factor of safety. Therefore, the known method is of limited use for predicting resource of technical devices.

By a known method ((see, for example, Makhutov N. A. and Pimshtein P. G.) determination of the useful life and residual life of the equipment ("problems of safety in emergency situations. Vol. 5." M, 1995) residual life determined on the basis of their design life and probabilistic resource in excess of the wall thickness and the speed of its corrosive and erosive wear of the technical device. In conditions of cyclic load with the allowable number of cycles and the period of loading, creep, depending on the strain rate creep, groupc the th destruction normative service life is assumed to be equal to 20 years for most of the technical devices. Maximum permissible operating time is determined from the set of those terms of service depending on your volume control at diagnosis. Residual life determine the difference between the calculated service life and operation time, but excludes the impact of stock strength of the replaceable elements for a resource that is not taken into account the degree of responsibility (group or hazard class technical devices), describing the likely degree of risk in case of failure or destruction. Not considered operational speed reduction factor of safety of the replaceable elements, indicators of corrosion and the corrosion resistance of materials that do not provide sufficient accuracy assessment resource technical devices.

The known method for determination of residual resource of technical devices [for example, in the book. "Vessels and pressure piping: a Handbook / by A. M. Kuznetsov, V. I. Livshits and other" Ed. 2nd, supplementary Irkutsk: Publishing SE "Irkutsk regional printing house No. 1, 1999. 600 S.] subject to a reserve margin of safety against the admitted loads (calculated, for example, according to the normative documents) to the actual loads from the ratio of the effective thickness of the walls of the net gain to the calculated wall thickness. By a known method in the calculation of the resource is not taken into account the impact strength reserve replaceable ale is now on the resource, not taken into account the likely degree of responsibility (group or class of danger) technical devices, characterizing the likely degree of risk in case of failure or destruction. Not considered operational speed reduction factor of safety of the replaceable elements, the rate of corrosion and the corrosion resistance of materials that are not accurate enough prediction of the source, renewable and residual resource of technical devices.

A known method for predicting the residual life [Makhutov N. A. Structural strength, resource and technogenic safety: 2 h / H.A. Makhutov. - Novosibirsk: Nauka, 2005. - PM 2: Rationale for resource and security. - 610 S.] by defining margins for different criteria limit state of relations limit load, strain and number of loading cycles to operational stress, strain, number of loading cycles. However, in the known method is not considered a quantitative measure of the fullness and volume of technical diagnostics, probabilistic parameter of reliability of the estimates of the reserves of strength replaceable elements, the degree of responsibility (group or class of danger), characterizing the likely degree of risk and responsibility in case of failure or damage, which does not provide a prediction of the resource with the replacement otnoshenija, parts and elements of technical devices.

In a known method of determining the remaining life of technical devices [see, for example, EP 03-421-01. Guidance on carrying out technical inspection and determination of the residual service life of vessels and apparatuses. Series 03. Issue 17 / Coll. auth. - M.: State unitary enterprise "Scientific-technical center for industrial safety of Gosgortekhnadzor of Russia, 2002. - 136 C.] the resource determined by the rate of corrosion for the period of operation, but excludes the impact of stock strength of the replaceable elements for a resource that is not taken into account the degree of responsibility (group or class of danger), characterizing the likely degree of risk and responsibility in case of failure or damage, which does not provide sufficient accuracy assessment resource, reliability and safety of operation of technical devices.

There is a method of evaluating the effectiveness of diagnosis of vessels, tanks and piping [see, for example, Cherepanov, A. P., Poroshin Y. C. evaluation of the effectiveness of diagnostics of vessels, tanks and piping. // Safety in the industry. 2004. No. 10. S. 43-46.], using a quantitative indicator of the effectiveness of diagnosis taking into account the degree of responsibility (group or class of danger), characterizing the probable article the stump risk in case of failure or destruction, the confidence score methods, completeness and volume control performed when the diagnosis. Not taking into account the impact of the margins of the replaceable elements, the rate of corrosion and the corrosion resistance of materials and an operating speed of inventory reduction strength is also not possible with sufficient accuracy to estimate the resource of technical devices.

The method for estimation of residual resource of technical devices (see the description of the invention to the patent of Russian Federation №2253096 "Method of evaluation of the technical state of the equipment, IPC G01M 15/00, F15B 19/00, Published 2005.05.27), which hold the input control until the beginning of the operation, and determine the conformity of normative-technical documentation and operating conditions and control of operational parameters, measure the parameters of the technical condition of at least one common node, develop mitigation measures to eliminate detected nonconformities, determining the magnitude of the residual resource and/or value of performance options that allows safe operation of technical devices, and develop an expert opinion. However, without considering the margins of the replaceable elements, completeness and quality of diagnosis, level of responsibility (group or classiobesity), characterising the likely degree of risk in case of failure or destruction, an operating speed lower margins, rates of corrosion and the corrosion resistance of materials is impossible forecasting resource replaceable elements of technical devices using a known method.

There is a method of technical diagnostics and residual life assessment in patent No. 2428682 "Method of thermal nondestructive testing of thermal state long, geometrically-complex and hard-to-reach objects. He is in continuous recording and analysis of the temperature along the extended object, and when a sudden change in the temperature of the local area it is concluded that the reduction of the level of technical security.

The analysis of the level of technology has allowed to establish that the analogues, characterized by a set of characteristics is identical for all features of the proposed technical solutions are not available. None of the closest analogues and prototypes does not provide a prediction of the original resource of complex technical objects (complex of constructions, complex production facilities and other) indicators of the actual technical condition, which is defined by the technical control during manufacturing, renewable and residual life at replacement of osennih elements based on the measure of the corrosion resistance of materials volume and efficiency of diagnosis, probable risk failure, characterizing the responsible group or hazard class technical devices that meet the criteria of "novelty and usefulness".

Search results known technical solutions in this and related areas of technology showed that the distinguishing features of the claimed method and its implementation does not follow explicitly from the prior art presents analogues and prototypes. The prior art also revealed no known significant features provided in the claimed invention, and the achievement of the technical result. Therefore, the claimed invention meets the condition of patentability "inventive step".

The disadvantage of known methods and their corresponding devices is as follows: all known methods of residual life assessment solve the problem in relation to a particular purely individual objects. This should be preceded by a very thorough study of the object: modeling behavior under various defects, experimental testing of mathematical models, to assess the adequacy, etc., This reduces the versatility of the method, dramatically increases the cost and reduces the performance diagnostics. The emergence of a new diagnosed objective is and requires a full range of research. In addition, the reliability of the results obtained by known methods, in a sufficiently large degree determined by the subjective qualities, which reduces the versatility of the method and reliability of results obtained.

In other words, to evaluate the residual life of the methods described in the above analogues and the prototype requires most reliable a priori information about the behavior of the investigated object with the supplied external and internal loads.

This is not always possible, i.e., it is not always possible to obtain the necessary a priori information. This is, for example, refers to the problems of diagnosis:

energy and industrial safety of the municipalities, with the simultaneous operation of a large number of complex engineering objects

- complex engineering and construction projects,

- the physical condition of athletes, etc.

The closest analogue is the way to control mechanical stresses, as described in I. Einav, B. Artemiev, E. Azizov, A. Azizov. "Non-destructive testing in construction" / tutorial under the General editorship of academician centuries Klyuyev. M.: Publishing house of the Spectrum, 2012, S. 283-286.

This way and realizing his device are in a location on the object control information of the sensors (e.g. the, load cells), which measure the stress-strain state of the structure. Information is collected on tantostanze. From these data, the operator assesses the current state of the design.

The disadvantage of this method repeats the disadvantages of the previously described methods.

Therefore, today there is urgent need to create a method and device for adaptive prediction of the residual resource, allowing for time-varying conditions of operation and changing the wear rate of the nodes and blocks and based on the periodically updated information about the current state of the object and the automatic self-learning prediction.

Fundamentally, the approach to solving this problem has become possible with the advent of methods and their corresponding means of neural network analysis.

The invention

There are repeated attempts to solve the problem through testing different methods - ultrasonic, radiofrequency, etc., But this did not lead to the desired results. This is due mainly to the following: all known methods of residual life assessment solve the problem in relation to a particular purely individual objects. This should be preceded by a very thorough study of the object: modeling behavior under various defects, experimental the Naya development of mathematical models, the assessment of the adequacy, etc. in Addition, the reliability of the results obtained by known methods, in a fairly large extent determined by subjective qualities, which reduces the versatility of the method and reliability of results obtained.

Technical result achieved when using the claimed group of inventions is to improve the accuracy of the prediction results, increasing the versatility of the method, the extension of field of use, reducing the cost forecasting.

The technical result is achieved due to the fact that in the method, including the impact on the object and recording the resulting changes, perform the following steps after registration of the array signals input {UI} with the information of sensors mounted on the test object: pass an array of electrical signals input UIthrough electronic filters Tofto improve the signal-to-noise receiving array output electric signals after processingUinsxfconduct initial training of the neural network model the reliability of the test object using arrays of electrical signals Uinsxfas follows: use of electronic analog circuit which algorithm is equivalent to a neural network model of reliability of the test object with parameters Fiwhere i is the number of the option scheme, pass arrays of electrical signalsUinsxfthrough the scheme receiving the array of electrical signals(Uinsxfwith a)icompare the arrays of signals(Uinsxfwith a)iwith signals Ubeforecorresponding to the loss of reliability of Pbeforeoperation of the controlled object: if the absolute value of the difference

ABS((Uinsxfwith a)i-U/mi> ppedεi=N,

where N is the parameter of the neural network model corresponding to the point of loss of operational reliability, ε - accuracy comparison, Ubefore- limit value of the signal corresponding to the point of loss of reliability/destruction of the controlled object,

approximate the time dependence of the arrays of electrical signalsUinsxfwith a=Uinsxfwith a(t)based on a previously trained neural network model with a specified frequency, for example, by the method of least squares, where t is the time, make the extrapolation values ofUinsxfwith a(t)=P(t)to the threshold value, and determine the Pbeforeand, accordingly, the amount of time the controlled object to its failure tbeforeusing the selected model extrapolation, where P is the load value, P - the value of the limit load.

In case of failure to achieve the required accuracy of the forecasting exercise retraining neural network model based on the newly received data.

The technical result in part of the device is achieved due to the fact that the device for adaptive prediction of the residual resource of interest (1) includes information sensors (2)installed on the test object (1) and perceiving changes parameters of an object control, electronic filters (3) to improve the signal-to-noise ratio associated with the outputs of the sensors and are connected to the inputs of analog electronic circuits (4)that implements a neural network model of the reliability of the test object (1), while the other group of inputs of the analog electronic circuit (4) connected to the power supply signal on the basis of the model (5), and its output is connected serially concatenated block approximation of the time-dependent (6) arrays of electrical signalsUinsxfwith athe shaping unit of the time series (7) of the predictions of the reliability of the operation object control unit extrapolation (8)Uinsxfwith amo stretchy="false"> (t)to limit values and determining the residual life, to the input of which is connected to the unit job extrapolation model (9), and the second output unit signal on the basis of model (5) is connected to the input unit of job extrapolation model (9).

Unit assignments extrapolation model (9) is a block of memory (standard schemes ROM is permanent memory device).

These units perform the following functions.

The block approximation of the time-dependent arrays of electrical signalsUinsxfwith aperforms the approximation of the arrays of electrical signalsUinsxfwith aa power-law function of the 3rd degree for the implementation of extrapolation.

The block forming the time series of predictions of reliability of the test object generates a time series of values of reliability.

Block extrapolation value ofUinsxfwith a(t) to the limits and definition of the residual resource provides extrapolating this value until the prediction of the destruction of the object of control (the condition not exceeding the current rate the reliability of valid values).

Unit assignments extrapolation model (9) allows the representation of the optimal model at the "request" block 5.

Brief description of drawings

Fig.1 - structure of artificial neural network (Ann) direct distribution.

Fig.2 is a functional diagram of the device adaptive prediction of the residual life of complex objects.

Fig.3 is a schematic diagram of the processing unit of the time series (7).

In the above figures, the following notation:

1 - the object of control

2 - information sensors

3 - electronic filters

4 - analog circuit that implements a neural network model of the reliability of the test object

5 - unit signal on the basis of the model

6 is a block approximation of the time-dependent arrays of electrical signals

7 - forming unit of time series

8 is a block extrapolation

9 is a block job extrapolation model

10 is a block accumulates the results of the control.

11 - analog-to-digital Converter: 8-bit ADC CPU

12, 14 - storage device: 8-bit P IS KRE

13 is a Microprocessor-based unit: 8-bit IPC CP

15 - power supply

Xm- input data

Wm2- array of weights

n is the number of neuron

kn2- array of weights

p is the number of neuron

thep- array of weights

The preferred embodiment of the invention

All the electronic components are built using standard microprocessor circuits and microprocessor Assembly with programmable storage devices. The block approximation of the time-dependent arrays of electrical signalsUinsxfwith aunit forming a time series of predictions of reliability of the test object and block extrapolation value ofUinsxfwith a(t)to the limits and definition of the residual resource implemented in standard logic circuits (for example, in accordance with the book: Ugryumov E. P. Digital circuitry: educational. manual for schools. - 3rd ed. revised and enlarged extra - SPb.: - BHV - Petersburg, 2010) or other similar ed the of).

A concrete implementation of the blocks of the circuits shown, for example, in books:

- Reference. Large integrated circuit memory devices. Under. Ed. by A. Y. Gordeev, Y. N. Janeway. - M.: Radio and communication.

- C. C. Yakubovich and other Digital and analog integrated circuits. The Handbook. - M.: Radio and communication.

And in other similar books.

For example, in Fig.3 shows a schematic diagram of the processing unit of the time series.

The device (Fig.1)that implements the proposed method works as follows.

From the information of the sensors (2) of a control object (1) through electronic filters (3) for the analog circuits (4) receives information about the state of the control object.

The algorithm of functioning of the scheme (4) is equivalent neural network model the reliability of the test object with parameters Fi, where i is the number of the parameter schema.

The functioning of the scheme (4) is as follows.

Pass arrays of electrical signalsUinsxfwith athrough the scheme receiving the array of electrical signals(Uinsxfwith a)i . Compare arrays of signals(Uinsxfwith a)iwith signals Ubeforecorresponding to the loss of reliability of Pbeforeoperation of the controlled object as follows:

if the absolute value of the difference

ABS((Uinsxfwith a)i-Uppedεi=N,

where N is the parameter of the neural network model corresponding to the point of loss of operational reliability, ε - accuracy comparison, Ubefore- limit value of the signal corresponding to the point of loss of reliability/destruction of the controlled object.

If the scheme (4) is unable to perform its function, then produces additional training scheme (4) by blocks (5) and (9), defines a new model.

Next, using the block (6) approximate the time dependence of the arrays of electrical signalsUinsxmi> fwith a=Uinsxfwith a(t)based on a previously trained neural network model (section 4) with a given frequency, for example, by the method of least squares, where t is the time unit (7) generates a time series of values of signals in the block (8) is produced by extrapolation of the values ofUinsxfwith a(t)=P(t)to the threshold value, and determine the Pbeforeand, accordingly, the amount of time the controlled object to its failure tbeforeusing the selected model extrapolation, where P is the load value, Pbefore- the value of the limit load. In block (10) are formed in the survey results.

Using this proposed method, neural networks provides a high degree of adaptability to the method of prediction, because, firstly, allows you to configure the model wear on a specific object, secondly, allows increasing the operating time to correct extrapolation of the change model parameters the RA to the limit value, responding flexibly to the change in the wear rate of the pair.

Consider theoretical justification for some node of the execution steps of the proposed method.

1. Pass electrical signals data (UIthrough electronic filters (Kfto enhance the credibility of their informative features and reduce their noise component (increasing the signal/noise) - output signals after processing(Uinsxfwith a).

In the developed method, the evolutionary search with clustering characteristics of limit States of structures of complex objects, their defects and damages, leading to pre-emergency situation, it is proposed to group similar characteristics using clustering techniques, which allow us to split the sample into groups of densely placed signs in the instance space (cluster, factor groups) and to allocate in each cluster, one of the most typical attribute.

Evolutionary search with clustering of signs to highlight the most significant set of features from a given sample limit States of structures of complex objects, their defects and damages, leading to the pre is Etoile < X, Y>, it is proposed to perform the following sequence of steps.

Step 1. To group the signs of the original sample data in the cluster.

Step 1.1. For each element Xi to calculate the Euclidean distance from it to all other characteristics in the sample. The Euclidean distance between signs of Xa and Xb is calculated by the formula:

dE(Xa,Xb)=Σp=1m(xpa-xpb)2,

where m is the number of instances in the sample; Xpa and Xpb - values of the a-th and b-th characteristics of p-instance respectively.

Step 1.2. On the basis of the previously calculated distances between instances using the methods of cluster analysis, for example, a method with the addition of clusters, the method of removing clusters, combined method or the method of fuzzy C-means, to form a group of signs compactly located in the instance space. To identify features that are centers of clusters.

Step 1.3. For each element Xi to calculate the probability of inclusion in the chromosome.

Step 1.3.1. To calculate the value of the individual is Noah estimating the information content of I icharacteristic Xifor example, on the basis of the coefficient of pair correlation, correlation coefficient signs of the correlation coefficient Fechner, dispersion relation, the coupling coefficient information criterion, entropy characteristic criterion, based on a probabilistic approach, or criterion, based on a statistical approach.

Step 1.3.2. To determine the probability Pithe inclusion of the i-th characteristic:

Pi=Ii=dE(Xi,XC,i)dEmax,C(Ii-IC)

where dE(XiXC,i- the distance from the sign of Xito the center of its cluster; dEmax,C- the maximum distance in the cluster, which is the i-th feature; IC- informative sign, which is the center of the cluster, which is the sign of Xi.

Step 2. Set the iteration count (of time): t=0.

Step 3. To initialize the start.

Step 3.1. Set the counter of the formed signs: j=1.

Step 3.2. To form the j-th characteristic Hj.

Step 3.2.1. Set the th counter certain characteristics: i=1.

Step 3.2.2. To generate a random number r=rand[0; 1], where rand[a; b] is a randomly generated number in the interval [a; b].

Step 3.2.3. If Pi>r, then the i-th characteristic of the j-th parameter to assign a value: hij=1, otherwise: hij=0.

Step 3.2.4. If the j-parameter is fully formed (i=L), then

go to step 3.3.

Step 3.2.5. Set: i=i+1.

Step 3.2.6. Go to step 3.2.2.

Step 3.3. If you have set up all the parameters (j=N), then go to step 4.

Step 3.4. To install: j=j+1.

Step 3.5. Go to step 3.2.

Step 4. To calculate the value of the fitness function f(Hj) by the formula:

f(Hj)=I(Hj)Σi=1Lhij(1+Σi=1LIihij)(1+Σi=1LPihij)

where J(Hj- the value of the criterion, taking into account the size and the information content of the set of indicators corresponding parameter Hj.

Step 5. To validate Crete the holders of the stop (the maximum acceptable time for the operation method, the number of iterations, the values of the fitness function). If the criteria of the search is finished, satisfied, then skip to step 11.

Step 6. Increase the counter of iterations: t=t+1.

Step 7. Select the parameter by using one of the existing methods of selection (proportional selection, the selection using roulette wheel, tournament selection, threshold selection, selection by ranking).

Step 8. To apply a uniform crossing. The mask crossing to establish a single value, which correspond to the characteristics with the probability of detection, above average, the rest set to zero.

Step 9. Stop.

Thus, in the proposed method, the evolutionary search with clustering characteristics of limit States of structures of complex objects, their defects and damages, leading to pre-emergency situation is taken into account the proximity of the signs in the instance space that allows you to create new solutions from signs located, as a rule, in different groups, increasing the likelihood of finding a combination of characteristics, with maximum information content.

2. Produce preliminary training of the neural network model the reliability of operation of the controlled object using arrays of electrical signals(U insxf)as follows.

Built and trained a neural network to approximate the table given function yi=f(xi)=[n1n2, ...nN], i=1, 2, ... N

Used the function newff(.) the creation of the "classic" multi-layer neural network trained by the method of back-propagation errors.

P=zeros(1,);

for i=1:N% create an array

P(i)=i*0.1; % input (argument)

end

T=[n1n2, ..., nN], % input (the function value)

net=newff([-12.09], [51], {'tansig' 'purelin'}); %create a neural network

net.trainParam.epochs=100; %set number of learning epochs

net=train(net, P, T); %training the network

y=sim(net, P); %the survey trained network

figure (1);

hold on;

xlabel ('P');

ylabel ('T');

plot(P, T, P, y, 'o'),grid; %drawing the graph of the original data and functions generated by the neural network

The result of the neural network

3. Make the extrapolation values ofUinsxfwith a(t)=P(t)to limit values and identification (Pbeforeand, accordingly, the amount of time remaining share (tbeforewith selected using the extrapolation model, where P is the load value.

Neural networks allow to extrapolate the data. Used a three-layer Ann direct distribution with activation function sigmoid (Fig.2). Directly to determine the residual life ofLOSTCPGused neuron of the third layer of the network.

Training is performed using a sliding window according to the method of back-propagation errors. At each step of the learning taking k values (according to the window size) and to train the network, presenting as a reference at the output of the Ann (k+1) the value of a number. Then the window is moved one step to the right, and the cycle resumes. This process is repeated, starting with the first window position, until, until you have obtained acceptable results in accuracy. In extrapolating the number of more than one step using the data obtained in the previous stages of extrapolation. For the experiment we used the Ann with 10 neurons in the input layer and 5 hidden.

To confirm the above conducted the following experiment: trained neural network on 20 measurements and determined the residual resource of complex technical object.

The sliding window size was k=90, the stop criterion was acceptable is the value standard deviation (MSWD). The training of the Ann on a single processor AMD AthlonMP 1800+was 280, and the Squaw 3.01.

In the Ann extrapolates the number with some errors, but fairly well predicts the trend.

The presented method has the following advantages:

- provides operational control in real operating conditions of controlled structures in the process of loading different types of loads,

- allows to increase the reliability of the results of monitoring and forecast, approximately two times,

- allows to increase the reliability and safety of operation of controlled structures (especially those working at the limit of the residual resource),

- provides the versatility of hardware and software through a flexible adaptation of the method of control to a variety of objects of control,

- reduces the probability of accidents by identifying real technical characteristics of structures and estimation of the real residual resource.

1. Method of adaptive prediction of the residual resource of technical objects, including a force on the surface of the inspection object, check the array of electrical signals input UIset on the object control information sensors, and the information signals of the sensors due to the betrayal of the s forces acting on the surface of the inspection object, characterized in that
pass an array of electrical signals input UIthrough electronic filters Tofto improve the signal-to-noise receiving array output electric signals after processing,
conduct initial training of the neural network model the reliability of the test object using arrays of electrical signalsas follows:
use electronic analog circuit which algorithm is equivalent to a neural network model of reliability of the test object with parameters Fiwhere i is the number of the option scheme,
pass arrays of electrical signalsthrough the scheme receiving the array of electrical signals,
compare arrays of signalswith signals Ubeforecorresponding to the loss of reliability of Pbeforeoperation of the controlled object:
ifi=N
where N is the parameter of the neural network model corresponding to the point of loss of operational reliability, ε - accuracy comparison, Ubefore- limit value of the signal corresponding to the point of loss of reliability/destruction of the controlled object,
approxima the comfort of the time dependence of the arrays of electrical signals based on a previously trained neural network model with a specified frequency, for example, by the method of least squares, where t is time,
make extrapolationto the threshold value, and determine the Pbeforeand, accordingly, the amount of time the controlled object to its failure tbeforeusing the selected model extrapolation, for example, the sample of maximum similarity, where P is the load value.

2. The method according to p. 1, characterized in that in case of failure to achieve the required accuracy of the forecasting exercise retraining neural network model based on the received data.

3. Device for adaptive prediction of the residual resource of technical objects, including information sensors installed on the test object and the perceiver changes parameters of an object control, electronic filters to improve signal-to-noise ratio associated with the outputs of the sensors and are connected to the inputs of analog electronic circuit that implements a neural network model of the reliability of the test object, while the other group of inputs of analog electronic circuits connected to the power supply signal to retrain the model wears, and its output is connected serially concatenated block approximation temporary C is voimasta arrays of electrical signals unit forming a time series of predictions of reliability of the operation object control unit extrapolationto limit values and determining the residual life, to the input of which is connected to the unit job extrapolation model, where- an array of electrical signals at the output of analog electronic circuits.



 

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