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Method of searching for faulty unit in continuous dynamic system. RU patent 2513504.

Method of searching for faulty unit in continuous dynamic system. RU patent 2513504.
IPC classes for russian patent Method of searching for faulty unit in continuous dynamic system. RU patent 2513504. (RU 2513504):

G05B23/00 - Testing or monitoring of control systems or parts thereof (monitoring of programme-control systems G05B0019048000, G05B0019406000)
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FIELD: radio engineering, communication.

SUBSTANCE: method involves recording the reaction of a fault-free and controlled system, setting the minimum value of the square of standard deviation of signals of the controlled and fault-free system, generating a signal as a square of the standard deviation of reactions of the controlled system and the fault-free system, setting the minimum of current values, generating a signal as the minimum of current values of coefficients of discrimination of all pairs of defects, determining integral estimates of output signals, transmitting output signals of multiplier units to inputs of integrating units, recording the obtained estimates of output signals, determining the number of structural defects of units, determining integral estimates of model signals, recording estimates of output signals obtained from integration, determining deviation of integral estimates of model signals, determining standardised values of deviation of integral estimates of model signals, replacing the system with nominal characteristics of the controlled system, transmitting an analogue test signal to the input of the system, determining integral estimates of signals of the controlled system, determining deviation of integral estimates of signals, determining standardised values of deviation of integral estimates of signals, determining diagnostic features, determining the serial number of the defective unit from the minimum value of the diagnostic feature.

EFFECT: improved noise-immunity of diagnosing control systems by improving distinguishability of defects.

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The invention relates to the field of control and diagnostics of automatic control systems and their elements.

There is a method of diagnostics of dynamic links management systems (the Patent for the invention № 2439648 from 10.01.2012 on the application number 2010142159/08(060530), MKI 6 G05B 23/02, 2012), based on the multiple integration of the output unit with weights

e - α l t

, & alpha l is a real constant, l - number of constants.

The disadvantage of this method is that it provides detection of defects with low visibility, that is has low immunity.

The closest technical solution (prototype) is a way of finding a bad block in a dynamic system (Patent for invention №2435189 from 27.11.2011 on the application number 2009123999/08(033242), MKI 6 G05B 23/02, 2011).

The disadvantage of this method is that it provides detection of defects with low visibility, that is has low immunity.

Technical task, which directed the invention, is the improvement of noise immunity of the way of diagnosing continuous automatic control systems by improving the distinctiveness of defects. This is achieved by applying an adaptive algorithm for diagnosis.

The task is achieved by registering reaction known good system f j (t), j=1,...,k, and the reaction of the controlled system f j (t) j=1,...,k in the interval t belongs to[0,T K ] k checkpoints, set the minimum value of the square of the norm deviations h signals controlled and obviously a working system, the h value is determined experimentally for a particular controlled system, form the signal H as the square of the norm deviations reactions of the controlled system and the known good system for the given set of control points:

H ( t ) = ∑ j = 1 k Δ f j 2 ( t )

where F j (t)=f j (t)-f j (t), j=1,...,k, set a minimum of current values at the time t of the coefficients of the distinctiveness of all pairs of defects g, the value of g is determined experimentally in the range [0, 1] for specific controlled system, form the signal G as the minimum of the current values of the coefficients of the distinctiveness of all pairs of defects:

G ( t ) = min { ' j i ( α , t ) } where ' j i ( α , t ) = 1 - ( ∑ m = 1 k Δ P m i ( α , t ) x Δ P m i ( α , t ) ) 2 ∑ m = 1 k Δ P m i 2 ( α , t ) x ∑ m = 1 k Δ P m i 2 ( α , t )

cosine squared angle between normalized vectors integral estimates of the deviations of signals from the nominal values during test deviations for defects with numbers i and j, define the integral evaluation of the output signal F j (a), j=1,...,k system, which at the time of filing of the test signal at the input of the system with nominal characteristics simultaneously begin the integration of control signals in each of the k checkpoints weights e-αt where

α = 5 T To

by filing on the input k blocks multiplication control signals, on the second input block multiplication serves exponential signal e-αt , output signals k blocks multiplication served on inputs k blocks of integration, provided that N(t) is greater than the specified values of h and G(t) is greater than the selected minimum distinction g, the integration was completed at the moment of time T to , the resulting integrated assessment of the output signal F j (a), j=1,...,k, register, record the number m of the considered structural defects blocks, determine integral evaluation of signals models for each of the k control points obtained in the course of the trial of deviations for m single defects blocks, which in turn in each block of the dynamic system is administered trial deviation parameter transfer function and are integral evaluation of output signal system for the setting of integration ascension and test signal x(t)for time values, in which H(t) is greater than the specified norm of h and G(t) is greater than the selected minimum distinction g, obtained by integrating the assessment of the output signals for each of the k control points, and each of the m trial deviations P ji (a), j=1,...,k; i=1,...,m register, determine the deviation of the integral estimates of signals to the model, the resulting trial variance combinations of different structural units ΔP ji (a)=P ji (a)-F j (a), j=1,...,k; i=1,...,m, determine the normalized value deviations integral estimates of signals to the model, the resulting trial of deviations for structural defects ratio

Δ P ^ j i ( α ) = Δ P j i ( α ) ∑ r = 1 k Δ P r i 2 ( α ) , ( 1 )

replace the system with a nominal characteristics controlled, at the entrance of the system serves a similar test signal x(t), define the integral evaluation of signals controlled system for k checkpoint F j (a), j=1,...,k option of integrating alpha, for values time, in which H(t) is greater than the specified norm of h and G(t) is greater than the selected minimum distinction g, determine the deviation of the integral estimates of signals controlled system for k checkpoint from the nominal values F j (a)=F j (a)-F j (a), j=1,...,k, determine the normalized value deviations integral estimates of signals controlled system of ratios

Δ F ^ j ( α ) = Δ F j ( α ) ∑ r = 1 k Δ F r 2 ( α ) , ( 2 )

determine the diagnostic signs of ratio

J i = 1 - [ ∑ j = 1 k Δ P ^ j i ( α ) x Δ F ^ j ( α ) ] 2 , i = 1 , ... ,m , (3)

the minimum value of the diagnostic sign determine the serial number of the defective unit.

The essence of the proposed method consists in the following.

The method is based on using a trial deviations of the parameters of the model of continuous dynamical systems.

To improve the reliability of diagnosis it is desirable to increase the distance between diagnostic signs. Under other equal conditions, it allows to increase the immunity of the algorithm of diagnosis. Increase the distance between diagnostic signs by targeted selection diagnostic information in the process of formation of diagnostic features. For these purposes serves the adaptive algorithm of searching a single structural defects, functional diagram of which is shown in figure 1.

Here: ENTRANCE - input;

1 - diagnostic model;

2 - diagnostic object;

3 - model trial deviation;

4th block the multiplication of the signals;

5 - the set of characteristics;

6 - block select the minimum of the current values of the coefficients of the distinctiveness of all pairs of defects;

7 - block addition signals through control points;

8 - key scheme with two control signals;

The OUTPUT is the vector of values of diagnostic features.

Adaptive algorithm search single defects blocks consists in the following:

- function blocks 1, 2 and 3 is the same input signal INPUT;

- a signal is generated at the output of 7 (H(t)) as the square of the norm deviations time characteristics for the given set of control points

H ( t ) = ∑ j = 1 k Δ f j 2 ( t ) ;

- a signal is generated at the output of 5 (G(t)) as the minimum of the current values of the coefficients of the distinctiveness of all pairs of defects

G ( t ) = min { ' j n ( α , t ) } ;

- instantaneous values of deviations of signals nominal model and object F(t), as well as deviations signals nominal models and models with a trial deviations ΔP i (t) transmitted through the key figure 8 on the set of signs, provided that H(t) exceeds the given value of the square of the norm of h and G(t) is greater than the selected minimum the distinctiveness g;

- is formed the current value of diagnostic character at this point of time at the output of 8;

- after the time monitoring of the received vector diagnostic signs OUTPUT the values of the elements of which is determined defective unit.

Thus, the proposed method of search for bad blocks is reduced to the following operations:

1. As dynamic systems consider a system consisting of randomly United dynamic blocks, number of single defects blocks m.

2. Pre-determine the time control T To & GE; T PP , where T PP - transition time system. The transition process to assess the nominal values of the parameters of the dynamic system.

3. Determine the parameter integral transforms of signals from the relation

α = 5 T To .

4. Record the number of control points k.

5. To calculate the current instant of coefficients of distinctiveness at the moment of time t pre-determine the normalized vectors

Δ P ^ i ( α , t )

deviations integral estimates of signals to the model, the resulting trial of deviations of the i-th number of each of the m single defects blocks and defined above option, integral transforms alpha, why do paragraphs 6-10.

6. Serves test signal x(t) (single step, linearly increasing, rectangular pulse etc.) to the input of a control system with a nominal characteristics. Major restrictions on the type of input test the impact of the proposed method does not.

8. Determine the current integrated assessment signal model for each of the k control points obtained in the course of the trial deviations of each of the m single defects blocks, which in turn for settings transfer functions of each structural unit dynamic the system is administered trial deviation of these parameters and perform paragraphs 6 and 7 with respect to reactions of models and mock deviations for the same test signal x(t). The resulting integration of current estimates of the output signals for each of the k control points, and each of the m trial deviations P ji (alpha,t), j=1,...,k; i=1,...,m are registering.

9. Determine the deviation of the current integral estimates of signals to the model, the resulting trial deviations structural blocks ΔP ji (alpha,t)=P ji (alpha,t)-F j (alpha,t), j=,...,k; i=1,...,m.

10. Determine the normalized values of current deviations integral estimates of signals models received in the course of the trial deviations of one of the blocks at time t by the formula:

Δ P ^ j i ( α , t ) = Δ P j i ( α , t ) ∑ r = 1 k Δ P r i 2 ( α , t ) .

11. Set the minimum value of the square of the norm deviations h signals controlled and a known good system.

12. Form the signal H(t) as the square of the norm deviations signals controlled and a known good system for the given set of control points:

H ( t ) = ∑ j = 1 k Δ f j 2 ( t ) .

13. Specify the minimum of the current values at the time t of the coefficients of the distinctiveness of all pairs of defects g.

14. Form the signal G(t) as the minimum of the selected instantaneous values at time t of the coefficients of the distinctiveness of pairs of defects:

G ( t ) = min { ' j i ( α , t ) } where ' j i ( α , t ) = 1 - ∑ m = 1 k ( Δ P ^ m i ( α , t ) x Δ P ^ m i ( α , t ) ) 2 .

15. To calculate diagnostic signs determine the normalized vectors

Δ P ^ i ( α )

deviations integral estimates of signals to the model, the resulting trial of deviations of the i-th number of each of the m single defects blocks and defined above option, integral transforms alpha, why do paragraphs 16-19.

16. Define the integral evaluation of the output signal F j (a), j=1,...,k system to system reactions f j (t), j=1,...,k in the interval t belongs to[0,T ] in k checkpoints. To do this, at the time of filing of the test signal at the input of a control system with a nominal characteristics simultaneously begin integration of control signals in each of the k checkpoint with weights e-αt where,

α = 5 T To

what signals control system serves on the input k blocks multiplication, on the second input block multiplication serves exponential signal e-αt , output signals k blocks multiplication served on inputs k blocks of integration, provided that H(t) exceeds the specified value of the square of the norm of h and G(t) is greater than the selected minimum distinction g, the integration was completed at the moment of time T To , the resulting integrated assessment of the output signal F j (a), j=1,...,k register.

17. Define integral evaluation of signals models for each of the k control points obtained in the course of the trial deviations of each of the m single defects blocks, which in turn, for each structural unit of the dynamic system is administered trial deviation of parameters the transfer function and perform paragraph 16 with respect to the output signals of models and mock deviations for the same test signal x(t). The resulting integrated assessment of the output signals for each of the k control points, and each of the m trial deviations P ji (a), j=1,...,k; i=1,...,m are registering.

18. Determine the deviation of the integral estimates of signals to the model, the resulting trial of deviations of the parameters of the structural unit ΔP ji (a)=P ji (a)-F j (a), j=1,...,k; i=1,...,m.

19. Determine the normalized deviation values of integral estimates of signals to the model, the resulting trial of deviations of the parameters of the structural unit under the formula:

Δ P ^ j i ( α ) = Δ P j i ( α ) ∑ r = 1 k Δ P r i 2 ( α ) .

20. Define integral evaluation of signals controlled system for k checkpoint F j (a), j=1,...,k, carrying out the operations described in paragraph 16 in relation to the controlled system.

21. Determine the deviation of the integral estimates of signals controlled system for k checkpoint from the nominal values F j (a)=F j (a)-F j (a), j=1,...,k.

22. Calculate the normalized deviation values of integral estimates of signals controlled system by the formula:

Δ F ^ j ( α ) = Δ F j ( α ) ∑ r = 1 k Δ F r 2 ( α ) .

23. Calculate diagnostic signs faulty structural unit according to the formula (3).

24. The minimum value of the diagnostic sign determine defective unit.

Consider the implementation of the proposed method of finding a single defect for a system of structural scheme is presented in figure 2.

Transfer function blocks:

rated: T 1 =5; 1 k =1; k 2 =1; T 2 =1; k 3 =1; T 3 =5 C. for single defect in the form of deviations time constant T 1 =4 in unit 1 by filing a speed test input single amplitude and integral transforms signals for the parameter?=0.5 and T =10 s the obtained values of diagnostic features according to the formula (3) with the three control points located on the outputs of the blocks, and method (prototype) without adaptation: J 1 =0; J 2 =0.78; J 3 =0.074. The minimum value of the sign J 1 clearly indicates the existence of a defect in block 1, and the difference between the third and the first signs can quantitatively characterize the actual appearance of this defect. The appearance of the defect ΔJ=J 3-J 1 =0.074. The same defect is found by application of adaptation options adaptation h=0.041 and g=0.046, gives the following values diagnostic signs: J 1 =0.1697; J 2 =0.818; J 3 =0.8164 the Appearance of the defect: ΔJ=J 3-J 1 =0.6467. Analysis of values diagnostic indicators shows that the values of the second and third grounds of obtained using adaptive algorithm, more than without adaptation (prototype). This leads to the conclusion that the actual appearance of the defect unit 1 above when using the proposed method. The distinctiveness of defects blocks 2 and 3 when searching for them using adaptive algorithm also not worse than using prototype.

Modeling of processes of search of defects in unit 2 (in the form of reduction of the parameter T 2 20%) for this object diagnosis using adaptive algorithm, parameters adaptation h=0.041 and g=0.046 and with the same input signal gives the following values diagnostic features:

J 1 =0.9497, J 2 =0.06825, J 3 =0.4042.

The appearance of the defect ΔJ=J 3-J 2 =0.33595.

For comparison, see diagnostic signs of faulty unit without using adaptive algorithm (prototype): J 1 =0.7827, J 2 =0, J 3 =0.7455. The appearance of the defect: ΔJ=J 3-J 2 =0.7455.

Modeling of processes of search of defects in block 3 (in the form of reduction of the parameter T 3 20%) for this object diagnosis if the adaptive algorithm with parameters adaptation h=0.041 and g=0.046 gives the following values:

J 1 =0.7272, J 2 =0.64, J 3 =0.0004069.

The appearance of the defect: ΔJ=J 2 J 3 =0.6395931.

For comparison, see diagnostic signs of faulty unit without using adaptive algorithm (prototype):

J 1 =0.074, J 2 =0.7478, J 3 =0.

The appearance of the defect ΔJ=J 1-J 3 =0.074.

The results show that the actual appearance of finding defects with the use of adaptation is more balanced for different defects units and, as a rule, is much higher, therefore, higher and robustness of the algorithm.

The minimum value of diagnostic character in all cases correctly indicates a defective unit.

 

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