CN111308327A - Analog circuit fault location and fault element parameter identification method - Google Patents

Analog circuit fault location and fault element parameter identification method Download PDF

Info

Publication number
CN111308327A
CN111308327A CN201911213502.1A CN201911213502A CN111308327A CN 111308327 A CN111308327 A CN 111308327A CN 201911213502 A CN201911213502 A CN 201911213502A CN 111308327 A CN111308327 A CN 111308327A
Authority
CN
China
Prior art keywords
fault
parameter
population
analog circuit
representing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201911213502.1A
Other languages
Chinese (zh)
Other versions
CN111308327B (en
Inventor
杨成林
田书林
黄建国
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN201911213502.1A priority Critical patent/CN111308327B/en
Publication of CN111308327A publication Critical patent/CN111308327A/en
Application granted granted Critical
Publication of CN111308327B publication Critical patent/CN111308327B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/28—Testing of electronic circuits, e.g. by signal tracer
    • G01R31/316—Testing of analog circuits

Landscapes

  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Tests Of Electronic Circuits (AREA)

Abstract

The invention discloses a method for fault location and fault element parameter identification of an analog circuit, which comprises the steps of obtaining a transmission function of a measuring point, analyzing fuzzy group information of the analog circuit, determining representative fault elements of each fuzzy group, obtaining a characteristic matrix of each representative fault element based on the transmission function, constructing an overdetermined equation group of polynomial fitting, calculating to obtain coefficient vectors corresponding to each representative fault element, monitoring the state of the measuring point when the analog circuit fails, locating faults based on monitoring data, and identifying the parameters of the fault elements by utilizing a genetic algorithm according to the located faults. The method can effectively realize the fault diagnosis and the fault element parameter identification of the analog circuit, has the advantages of quick positioning of simulation before test and accurate parameter identification of simulation after test, and does not need to store all parameter drift faults in advance.

Description

Analog circuit fault location and fault element parameter identification method
Technical Field
The invention belongs to the technical field of analog circuit fault diagnosis, and particularly relates to a method for analog circuit fault location and fault element parameter identification.
Background
With the rapid development of integrated circuits, digital and analog components are integrated on the same chip to improve product performance and reduce chip area and cost. It is reported that although the analog part only occupies 5% of the chip area, the failure diagnosis cost thereof occupies 95% of the total diagnosis cost, and the analog circuit failure diagnosis has been a bottleneck problem in the integrated circuit industry. At the present stage, some developed and relatively perfect analog circuit fault diagnosis theories are applied to practice, for example: a fault dictionary method in a pre-test analog diagnosis method, and an element parameter identification method and a fault verification method in a post-test analog diagnosis method. However, the fault dictionary method can only process discrete parameter faults and hard faults, cannot exhaustively simulate all continuous parameter faults and parameter combinations of elements, and therefore can hardly be used for parameter fault diagnosis. The simulation method after the test has the defects of blindness, long simulation time and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for positioning faults and identifying parameters of fault elements of an analog circuit.
In order to achieve the above object, the analog circuit fault location and fault element parameter identification method of the present invention comprises the following steps:
s1: acquiring a transmission function of the analog circuit at a measuring point t;
s2: analyzing to obtain fuzzy group information for fault diagnosis of the analog circuit through the output voltage of the measuring point t, recording the number of the obtained fuzzy groups as N, and selecting one element as a representative fault element in each fuzzy group;
s3: for each representative fault element, the following method is adopted to obtain a characteristic matrix representing the fault element respectively:
let i the parameter value p of the failed elementiIn the possible value range [ p ]imin,pimax]In which M values, p, are randomly takenimin、pimaxRespectively represents piI ═ 1,2, …, N, and the restThe parameter value of the fault element is taken as a value within a tolerance range, and a transmission function value corresponding to the mth value of the ith representative fault element under the preset excitation signal is obtained according to the transmission function
Figure BDA0002298817860000021
M is 1,2, …, M; according to M transmission function values of each representative fault element
Figure BDA0002298817860000022
Constructing a characteristic matrix H with the size of Mx 2 and representing fault elementsi：
Figure BDA0002298817860000023
S4: in each of the characteristic matrices H representing the faulty componentiInserting a column of M-dimensional unit column vectors into the last column of the array to obtain an extended feature matrix A with the size of Mx 3i：
Figure BDA0002298817860000024
If A isiRank r (A)i) Let the ith coefficient vector K representing the failed component be 3i：
Ki＝[ki,1,ki,2,ki,3]T
Let i' th constant term matrix b representing faulty elementi：
Figure BDA0002298817860000025
Solving overdetermined equation set AiKi＝biCoefficient vector K ofiLeast squares solution of (c):
Ki＝(Ai TAi)(-1)Ai Tbi
if the matrix A isiRank r (A)i) If 2, the following matrix a is constructedi′：
Figure BDA0002298817860000026
Let i ' th coefficient vector K ' representing faulty element 'i：
K′i＝[ki,2,ki,3]T
Let i' th constant term matrix b representing faulty elementi：
Figure BDA00022988178600000315
Solving overdetermined equation set A'iK′i＝biCoefficient vector K'iLeast squares solution of (c):
Figure BDA00022988178600000316
obtaining a coefficient vector Ki＝[0,ki,2,ki,3]T；
S5: when the analog circuit fails, the output voltage at the measuring point t is monitored for state under the same excitation signal as that in step S3, and D output voltages are measured
Figure BDA0002298817860000031
Wherein D is 1,2, …, D > 3; is calculated to obtain
Figure BDA0002298817860000032
Representing the voltage of the excitation signal, forming a test matrix of size M x 3
Figure BDA0002298817860000033
And a matrix of constant terms of size mx 2
Figure BDA0002298817860000034
Figure BDA0002298817860000035
Figure BDA0002298817860000036
Calculating evaluation parameters
Figure BDA0002298817860000037
Selecting N evaluation parameters WiCoefficient vector K corresponding to minimum valueiThe corresponding representative fault element is the fault diagnosis result;
s6: the following method is adopted to identify the parameters of the fault element:
s6.1: to be provided with
Figure BDA0002298817860000038
As individuals in genetic algorithms, where xcA parameter value representing the faulty component c,
Figure BDA0002298817860000039
representing output voltage
Figure BDA00022988178600000310
Corresponding fault element
Figure BDA00022988178600000311
Parameter value of (1), let xcRandomly taking a value within the tolerance of the faulty element c,
Figure BDA00022988178600000312
in the fault element
Figure BDA00022988178600000313
Randomly taking values in the fault range to generate G individuals to form an initial population P;
s6.2: judging whether an iteration end condition of the genetic algorithm is reached, if so, entering a step S6.7, otherwise, entering a step S6.3;
s6.3: performing cross and variation operation on the population P to obtain a sub-population Q;
s6.4: combining the population P and the population Q to form a population S;
s6.5: each individual is connected with
Figure BDA00022988178600000314
Splitting the parameter vector into D fault elements according to the following modes:
Figure BDA0002298817860000041
obtaining output phasors corresponding to parameter vectors of D fault elements under preset excitation signals according to the transmission function
Figure BDA0002298817860000042
Forming an output vector
Figure BDA0002298817860000043
D output voltages obtained by actual measurement under the fault state of the analog circuit
Figure BDA0002298817860000044
Constructing fault output vectors
Figure BDA0002298817860000045
Then calculating an output vector
Figure BDA0002298817860000046
And fault output vector
Figure BDA0002298817860000047
The Euclidean distance of (a), which is taken as an individual fitness value;
s6.6: g individuals with smaller Euclidean distance are screened out from the population S and used as a next generation population P ', and then the population P is made equal to P', and the step S6.2 is returned;
s6.7: selecting individuals with the minimum Euclidean distance from the current population, wherein the fault elements in the individuals
Figure BDA0002298817860000048
D parameter values of (a) are faultsAnd identifying the result of the parameter.
The invention relates to a method for fault location and fault element parameter identification of an analog circuit, which comprises the steps of obtaining a transmission function of a measuring point, analyzing fuzzy group information of the analog circuit, determining a representative fault element of each fuzzy group, obtaining a characteristic matrix of each representative fault element based on the transmission function, constructing a polynomial fitting over-determined equation set, calculating to obtain a coefficient vector corresponding to each representative fault element, monitoring the state of the measuring point when the analog circuit fails, locating the fault based on monitoring data, and identifying the fault element parameter by utilizing a genetic algorithm according to the located fault. The method can effectively realize the fault diagnosis and the fault element parameter identification of the analog circuit, has the advantages of quick positioning of simulation before test and accurate parameter identification of simulation after test, and does not need to store all parameter drift faults in advance.
Drawings
FIG. 1 is a flowchart of an embodiment of a method for analog circuit fault location and identification of a faulty component parameter according to the present invention;
FIG. 2 is a flow chart of a method for identifying parameters of a faulty component based on a genetic algorithm according to the present invention;
fig. 3 is a structural diagram of the second-order thomas analog filter circuit in the present embodiment.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
To better explain the technical solution of the present invention, first, the technical principle of the present invention will be explained.
As is well known, the transfer function of an arbitrary measurement point of an analog circuit with respect to an input point is expressed as:
Figure BDA0002298817860000051
wherein the content of the first and second substances,
Figure BDA0002298817860000052
respectively representing the input and output of an analog circuit, s ═ j ω, j representing an imaginary unit, ω representing an angular frequency, an、bmAre functions that take the element parameter values as variables. If the excitation frequency is not changed, the voltage is output
Figure BDA0002298817860000053
Is the element parameter X ═ X1,x2,…,xC) Function of xcThe parameter value of the c-th element is indicated. Recording nominal value of element parameter of analog circuit
Figure BDA0002298817860000054
The nominal value of the parameter for the c-th element is indicated. The fault diagnosis can be converted into an optimization problem when a circuit has a single fault, namely a certain element is out of tolerance
Figure BDA0002298817860000055
α denotes a tolerance parameter, typically 0.05.
With reference to the description in the patent with the reference number "201410336727.7" entitled "a fuzzy set identification method for analog circuits", for a linear analog circuit, when the amplitude and frequency of an input sinusoidal signal are determined for input
Figure BDA0002298817860000056
For reference phasors, outputting voltage phasors
Figure BDA0002298817860000057
The real and imaginary parts of (c) satisfy the following circular equations:
(Uor-a)2+(Uoj-b)2＝r2(2)
where a, b, r are parameters determined by the location of the failed element (network structure) and the value of the non-failed element, independent of the value of the failed element. Different elements have different values of a, b, r and can therefore be characterized as faults. The method can completely model and simulate all parameter drift faults of the element, and is a fault diagnosis model with unified hardness.
Rewrite the equation (2) for a circle as:
k1Uor+k2Uoj+k3＝c (3)
wherein k is1＝2a，k2＝2b，k3＝r2-a2-b2，
Figure BDA0002298817860000058
If the characteristic circle degenerates to a straight line, it can be expressed as:
k1+k2Uoj+k3＝0(4)
according to the three-point circle, three different fault amplitudes are set, simulation is carried out, and according to three different voltage values
Figure BDA0002298817860000059
Three parameters k can be obtained1、k2And k3And is not recorded as a feature vector K ═ K1,k2,k3]Each representative fault has a different feature vector. Therefore, as long as the fault characteristic parameter K ═ K is determined1,k2,k3]Then the fault source signature is uniquely determined.
FIG. 1 is a flowchart of an embodiment of a method for analog circuit fault location and identification of a faulty component parameter according to the present invention. As shown in fig. 1, the method for locating a fault and identifying parameters of a faulty component in an analog circuit of the present invention comprises the following steps:
s101: acquiring a transmission function:
and acquiring the transmission function of the analog circuit at the measuring point t.
S102: fuzzy group analysis:
and analyzing to obtain fuzzy group information for fault diagnosis of the voltage output by the analog circuit through the measuring point t, recording the number of the obtained fuzzy groups as N, and selecting one element as a representative fault element in each fuzzy group.
S103: acquiring a characteristic matrix representing a fault element:
for each representative fault element, the following method is adopted to obtain a characteristic matrix representing the fault element respectively:
let i the parameter value p of the failed elementiIn the possible value range [ p ]imin,pimax]In which M values, p, are randomly takenimin、pimaxRespectively represents piThe possible value range includes a normal value and a fault value, which is generally the value range of the element parameter values in the actual operation process of the circuit, taking a resistor as an example, a short circuit or an open circuit may occur, and then the possible value range is [0, + ∞ ]. Making the parameter values of other fault elements take values within a tolerance range, and obtaining a transmission function value corresponding to the mth value of the ith representative fault element under a preset excitation signal according to the transmission function
Figure BDA0002298817860000061
M is 1,2, …, M. According to M transmission function values of each representative fault element
Figure BDA0002298817860000062
Constructing a characteristic matrix H with the size of Mx 2 and representing fault elementsi：
Figure BDA0002298817860000063
S104: determining a characteristic vector representing a fault element:
in each of the characteristic matrices H representing the faulty componentiInserting a column of M-dimensional unit column vectors into the last column of the array to obtain an extended feature matrix A with the size of Mx 3i：
Figure BDA0002298817860000064
Then equations (3) and (4) can be uniformly expressed as an overdetermined system of equations for polynomial fitting:
AiKi＝bi(7)
wherein, KiRepresenting the ith coefficient vector representing the faulty element, biThe ith matrix of constant terms representing the failed element is represented.
If A isiRank r (A)i) Let i' th coefficient vector K representing the faulty component be 3 (corresponding to the equation of the circle)i：
Ki＝[ki,1,ki,2,ki,3]T(8)
Let i' th constant term matrix b representing faulty elementi：
Figure BDA0002298817860000071
Solving overdetermined equation set AiKi＝biCoefficient vector K ofiLeast squares solution of (c):
Ki＝(Ai TAi)(-1)Ai Tbi(10)
if the matrix A isiRank r (A)i) 2 (corresponding to the linear equation), the following matrix a is constructedi′：
Figure BDA0002298817860000072
Let the ith coefficient vector K representing the faulty component at the same timei′：
K′i＝[ki,2,ki,3]T(12)
Let i' th constant term matrix b representing faulty elementi：
Figure BDA0002298817860000073
Solving overdetermined equation set A'iK′i＝biCoefficient vector K'iLeast squares solution of (c):
Figure BDA0002298817860000074
obtaining a coefficient vector Ki＝[0,ki,2,ki,3]T。
Each coefficient vector KiI.e. the corresponding eigenvector representing the faulty component.
S105: fault diagnosis based on state monitoring:
when the analog circuit fails, the output voltage at the measuring point t is monitored in state under the same preset excitation signal as that in step S103, and D output voltages are obtained through measurement
Figure BDA0002298817860000081
Where D is 1,2, …, D needs to be larger than the dimension of the coefficient vector, i.e. D > 3, the larger the value, the better, it can be set according to the actual requirement. Is calculated to obtain
Figure BDA0002298817860000082
Representing the voltage of the excitation signal, forming a test matrix of size M x 3
Figure BDA0002298817860000083
And a matrix of constant terms of size mx 2
Figure BDA0002298817860000084
Figure BDA0002298817860000085
Figure BDA0002298817860000086
From all N eigenvectors K representing faulty elementsiIn (1), find and test matrix
Figure BDA0002298817860000087
The most conforming one, the evaluation of the test matrix
Figure BDA0002298817860000088
The characteristic vector with least square distance with the equation determined by the characteristic vector is specifically as follows: calculating evaluation parameters
Figure BDA0002298817860000089
Selecting N evaluation parameters WiCoefficient vector K corresponding to minimum valueiThe corresponding representative fault component is the fault diagnosis result, that is, the serial number of the representative fault component obtained by diagnosis
Figure BDA00022988178600000810
Can be expressed by the following formula:
Figure BDA00022988178600000811
s106: identifying parameters of the fault element:
let us remember
Figure BDA00022988178600000812
The corresponding serial number of each representative fault element in all fault elements is
Figure BDA00022988178600000813
Suppose D output voltages
Figure BDA00022988178600000814
Respectively corresponding to the fault elements
Figure BDA00022988178600000815
D number of parameter values of
Figure BDA00022988178600000816
And the values of other fault elements are within the tolerance range, the goal of fault parameter identification is to find out element parameter combinations, so that the output voltage of the element parameter combinations is the closest to the output voltage in a fault state.
FIG. 2 is a flow chart of the method for identifying parameters of a faulty element based on genetic algorithm in the present invention. As shown in fig. 2, the method for identifying parameters of a faulty component based on a genetic algorithm in the present invention comprises the following steps:
s201: initializing a genetic algorithm population:
to be provided with
Figure BDA00022988178600000817
As individuals in genetic algorithms, where xcA parameter value representing the faulty component c,
Figure BDA00022988178600000818
representing output voltage
Figure BDA00022988178600000819
Corresponding fault element
Figure BDA00022988178600000820
Parameter value of (1), let xcRandomly taking a value within the tolerance of the faulty element c,
Figure BDA00022988178600000821
in the fault element
Figure BDA00022988178600000822
Randomly taking values in the fault range to generate G individuals to form an initial population P.
S202: and judging whether an iteration end condition of the genetic algorithm is reached, if so, entering step S207, and otherwise, entering step S203. The iteration ending conditions of the genetic algorithm are generally two, namely, the maximum iteration times are reached, and the objective function value reaches a preset threshold value and can be set according to actual needs.
S203: generating a sub-population:
and performing cross and variation operation on the population P to obtain a sub-population Q. In this embodiment, the individual cross is a simulated binary cross (SBX), and the mutation operation is polynomial mutation (POL).
S204: merging the populations:
the population P and the population Q are combined to form a population S, i.e., S ═ P ∪ Q, and it is obvious that the number of individuals in the combined population is 2G.
S205: calculating an individual fitness value:
next, it is required to calculate a fitness value for each individual in the population S, and for the present invention, a euclidean distance between an output voltage obtained by each individual under a preset excitation signal and an output voltage of a current analog circuit is used as the fitness value, and the specific calculation method is as follows:
each individual is connected with
Figure BDA0002298817860000091
Splitting the parameter vector into D fault elements according to the following modes:
Figure BDA0002298817860000092
obtaining output voltages corresponding to parameter vectors of D fault elements under preset excitation signals according to the transmission function
Figure BDA0002298817860000093
Forming an output vector
Figure BDA0002298817860000094
D output voltages obtained by actual measurement under the fault state of the analog circuit
Figure BDA0002298817860000095
Constructing fault output vectors
Figure BDA0002298817860000096
Then calculating an output vector
Figure BDA0002298817860000097
And fault output vector
Figure BDA0002298817860000098
The euclidean distance of (a) is used as an individual fitness value. The calculation formula of the euclidean distance is as follows:
Figure BDA0002298817860000099
obviously, for fault diagnosis, it should be noted that the smaller the euclidean distance, the closer the output voltage is to the current analog circuit and the output voltage, and the better the individual.
S206: and (3) generating a next generation population:
g individuals with a small euclidean distance are selected from the population S as a next generation population P ', and the next generation population P' is returned to step S202. In this embodiment, individual optimization is performed by the tournament method of the alternative.
S207: determining a fault parameter identification result:
selecting individuals with the minimum Euclidean distance from the current population, wherein the fault elements in the individuals
Figure BDA0002298817860000103
The D parameter values are the identification result of the fault parameters. According to the D parameter values, a parameter degradation curve of the fault element when the analog circuit fails can be obtained, and parameter prediction of the fault element can be further carried out.
Examples
In order to better illustrate the technical effects of the present invention, the present invention is illustrated by taking a second-order thomas analog filter circuit as an example. Fig. 3 is a structural diagram of the second-order thomas analog filter circuit in the present embodiment. As shown in FIG. 3, the second order Thomas analog filter circuit of the present embodiment uses VoutAs a measurement point, the fuzzy group condition under the measurement point is as follows: { R1}、{R2}、{R3,C1}、{R4,R5,R6,C2}. The fuzzy group is determined by the circuit structure, is independent of the excitation signal and is only relevant to the measuring point selection. Under the condition of nominal condition, the direct current power supply is 5V, and under the excitation of 1V and 1kHZ sinusoidal signals, the method is adopted to obtain the characteristic vector representing the fault element. Table 1 is a feature vector representing a faulty element in this embodiment.
Fuzzy set k1 k2 k3
{R1} K1 0 0.9632 0
{R2} K2 1.6523 0 0
{R3,C1} K3 0 -1.5915 0
{R4,R5,R6,C2} K4 1 -0.6283 0
TABLE 1
As can be seen from Table 1, the fuzzy set { R1}、{R3,C1The corresponding model is a straight line, fuzzy set { R }2}、{R4,R5,R6,C2}。
In the present embodiment, the element R is assumed2Parameter drift failures occur (getting larger) and other components vary randomly within tolerances. Let element R2The parameter values of the elements in the degradation process are 11554 Ω, 11847 Ω, 12342 Ω, 13025 Ω, 13175 Ω, 13304 Ω, 13506 Ω, 13945 Ω, 14488 Ω and 14807 Ω, respectively, and the parameter values of the remaining elements are: r1＝10074Ω、R3＝10012Ω、R4＝9710Ω、R5＝10470Ω、R6＝10218Ω、C1＝10.03nF、C210.48 nF. Constructing 10 element parameter vectors according to the formula (18), obtaining 10 output voltages and forming an output vector
Figure BDA0002298817860000101
Figure BDA0002298817860000102
Figure BDA0002298817860000111
Obtaining a test matrix according to the expansion of the real part and the imaginary part
Figure BDA0002298817860000112
Sum constant term matrix
Figure BDA0002298817860000113
Substituting into formula (17) to obtain R2So that the evaluation parameter has a minimum value, and thus the fault is localized as R2。
Then, the population number of the genetic algorithm is set to be 200, and the iteration number is set to be 300. As a result of fault localization, R2Therefore, the range of values of the second to 11 th genes in the population of genetic algorithm is the fault range (0,9500 Ω) ∪ (10500 Ω, ∞), and other elements vary within the tolerance range.
After 300 iterations, the optimal individuals of [10245 Ω 11327 Ω,11612 Ω,12108 Ω,12772 Ω,12901 Ω,13044 Ω,13221 Ω,13664 Ω,14201 Ω,14526 Ω,10185 Ω,10244 Ω,09732 Ω,09500 Ω,10.23nF and 9.58nF were obtained]The corresponding simulation obtains a fault response of
Figure BDA0002298817860000114
Figure BDA0002298817860000115
Therefore, the method has high precision in fault diagnosis and fault element parameter identification, can realize fault diagnosis, can be used for parameter identification and prediction, and is suitable for continuous change of all parameters.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (1)

1. A method for positioning the fault of an analog circuit and identifying the parameter of a fault element is characterized by comprising the following steps:
s1: acquiring a transmission function of the analog circuit at a measuring point t;
s2: analyzing to obtain fuzzy group information for fault diagnosis of the analog circuit through the output voltage of the measuring point t, recording the number of the obtained fuzzy groups as N, and selecting one element as a representative fault element in each fuzzy group;
s3: for each representative fault element, the following method is adopted to obtain a characteristic matrix representing the fault element respectively:
let i the parameter value p of the failed elementiIn the possible value range [ p ]imin,pimax]In which M values, p, are randomly takenimin、pimaxRespectively represents piI is 1,2, …, N, the parameter values of the other fault elements take values within the tolerance range, and the transmission function value corresponding to the mth value of the ith representative fault element under the preset excitation signal is obtained according to the transmission function
Figure FDA0002298817850000011
According to M transmission function values of each representative fault element
Figure FDA0002298817850000012
Constructing a characteristic matrix H with the size of Mx 2 and representing fault elementsi：
Figure FDA0002298817850000013
S4: in each of the characteristic matrices H representing the faulty componentiInserting a column of M-dimensional unit column vectors into the last column of the array to obtain an extended feature matrix A with the size of Mx 3i：
Figure FDA0002298817850000014
If A isiRank r (A)i) Let the ith coefficient vector K representing the failed component be 3i：
Ki＝[ki,1,ki,2,ki,3]T
Let i' th constant term matrix b representing faulty elementi：
Figure FDA0002298817850000015
Solving overdetermined equation set AiKi＝biCoefficient vector K ofiIs smallest inA solution of two multiplications:
Ki＝(Ai TAi)(-1)Ai Tbi
if the matrix A isiRank r (A)i) 2, then the following matrix A 'is constructed'i：
Figure FDA0002298817850000021
Let i ' th coefficient vector K ' representing faulty element 'i：
K′i＝[ki,2,ki,3]T
Let i' th constant term matrix b representing faulty elementi：
Figure FDA0002298817850000022
Solving overdetermined equation set A'iK′i＝biCoefficient vector K'iLeast squares solution of (c):
Figure FDA0002298817850000023
obtaining a coefficient vector Ki＝[0,ki,2,ki,3]T；
S5: when the analog circuit fails, the output voltage at the measuring point t is monitored for state under the same excitation signal as that in step S3, and D output voltages are measured
Figure FDA0002298817850000024
Wherein D is 1,2, …, D > 3; is calculated to obtain
Figure FDA0002298817850000025
Figure FDA0002298817850000026
To representExciting signal voltages to form a test matrix of size Mx 3
Figure FDA0002298817850000027
And a matrix of constant terms of size mx 2
Figure FDA0002298817850000028
Figure FDA0002298817850000029
Figure FDA00022988178500000210
Calculating evaluation parameters
Figure FDA00022988178500000211
Selecting N evaluation parameters WiCoefficient vector K corresponding to minimum valueiThe corresponding representative fault element is the fault diagnosis result;
s6: the following method is adopted to identify the parameters of the fault element:
s6.1: to be provided with
Figure FDA00022988178500000212
As individuals in genetic algorithms, where xcA parameter value representing the faulty component c,
Figure FDA0002298817850000031
Figure FDA0002298817850000032
representing output voltage
Figure FDA0002298817850000033
Corresponding fault element
Figure FDA0002298817850000034
Parameter value of (1), let xcRandomly taking a value within the tolerance of the faulty element c,
Figure FDA0002298817850000035
in the fault element
Figure FDA0002298817850000036
Randomly taking values in the fault range to generate G individuals to form an initial population P;
s6.2: judging whether an iteration end condition of the genetic algorithm is reached, if so, entering a step S6.7, otherwise, entering a step S6.3;
s6.3: performing cross and variation operation on the population P to obtain a sub-population Q;
s6.4: combining the population P and the population Q to form a population S;
s6.5: each individual is connected with
Figure FDA0002298817850000037
Splitting the parameter vector into D fault elements according to the following modes:
Figure FDA0002298817850000038
obtaining output phasors corresponding to parameter vectors of D fault elements under the condition of obtaining preset excitation signals according to the transmission function
Figure FDA0002298817850000039
Forming an output vector
Figure FDA00022988178500000310
D output voltages obtained by actual measurement under the fault state of the analog circuit
Figure FDA00022988178500000311
Constructing fault output vectors
Figure FDA00022988178500000312
Then calculating an output vector
Figure FDA00022988178500000313
And fault output vector
Figure FDA00022988178500000314
The Euclidean distance of (a), which is taken as an individual fitness value;
s6.6: g individuals with smaller Euclidean distance are screened out from the population S and used as a next generation population P ', and then the population P is made equal to P', and the step S6.2 is returned;
s6.7: and selecting an individual with the minimum Euclidean distance from the current population, wherein D parameter values of the fault element c in the individual are the fault parameter identification result.
CN201911213502.1A 2019-12-02 2019-12-02 Analog circuit fault location and fault element parameter identification method Active CN111308327B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911213502.1A CN111308327B (en) 2019-12-02 2019-12-02 Analog circuit fault location and fault element parameter identification method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911213502.1A CN111308327B (en) 2019-12-02 2019-12-02 Analog circuit fault location and fault element parameter identification method

Publications (2)

Publication Number Publication Date
CN111308327A true CN111308327A (en) 2020-06-19
CN111308327B CN111308327B (en) 2021-01-26

Family

ID=71156172

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911213502.1A Active CN111308327B (en) 2019-12-02 2019-12-02 Analog circuit fault location and fault element parameter identification method

Country Status (1)

Country Link
CN (1) CN111308327B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112464551A (en) * 2020-09-27 2021-03-09 电子科技大学 Analog circuit fault parameter range determining method based on non-dominance
CN114115166A (en) * 2020-08-26 2022-03-01 南京依维柯汽车有限公司 Error-proofing and tracing management method applying commercial vehicle electric control system

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4808988A (en) * 1984-04-13 1989-02-28 Megatek Corporation Digital vector generator for a graphic display system
CN101246200A (en) * 2008-03-10 2008-08-20 湖南大学 Analog PCB intelligent test system based on neural network
CN101576604A (en) * 2009-01-04 2009-11-11 湖南大学 Method for diagnosing failures of analog circuit based on heterogeneous information fusion
WO2013157299A1 (en) * 2012-04-19 2013-10-24 国立大学法人東京工業大学 Signal processing device, signal processing method and program
US20140128713A1 (en) * 2012-11-06 2014-05-08 Perminova Inc. System for electrophysiology that includes software module and body-worn monitor
CN104090228A (en) * 2014-07-16 2014-10-08 电子科技大学 Analog circuit fuzzy group identification method
CN104198922A (en) * 2014-08-15 2014-12-10 电子科技大学 Frequency selection method in early fault diagnosis of analog circuit
US20150150957A1 (en) * 2010-11-12 2015-06-04 The Trustees Of The University Of Pennsylvania Consensus Prostate Antigens, Nucleic Acid Molecule Encoding The Same And Vaccine And Uses Comprising The Same
CN105973594A (en) * 2016-04-25 2016-09-28 西北工业大学 Rolling bearing fault prediction method based on continuous deep belief network
CN107480386A (en) * 2017-08-22 2017-12-15 哈尔滨工业大学 A kind of test and excitation method for optimizing based on response aliasing measurement with genetic algorithm
US20180025489A1 (en) * 2016-07-25 2018-01-25 Case Western Reserve University Quantifying mass effect deformation with structural radiomics in brain tumor patients
US20180038909A1 (en) * 2015-08-07 2018-02-08 Hefei University Of Technology Analog circuit fault diagnosis method using single testable node

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4808988A (en) * 1984-04-13 1989-02-28 Megatek Corporation Digital vector generator for a graphic display system
CN101246200A (en) * 2008-03-10 2008-08-20 湖南大学 Analog PCB intelligent test system based on neural network
CN101576604A (en) * 2009-01-04 2009-11-11 湖南大学 Method for diagnosing failures of analog circuit based on heterogeneous information fusion
US20150150957A1 (en) * 2010-11-12 2015-06-04 The Trustees Of The University Of Pennsylvania Consensus Prostate Antigens, Nucleic Acid Molecule Encoding The Same And Vaccine And Uses Comprising The Same
WO2013157299A1 (en) * 2012-04-19 2013-10-24 国立大学法人東京工業大学 Signal processing device, signal processing method and program
US20140128713A1 (en) * 2012-11-06 2014-05-08 Perminova Inc. System for electrophysiology that includes software module and body-worn monitor
CN104090228A (en) * 2014-07-16 2014-10-08 电子科技大学 Analog circuit fuzzy group identification method
CN104198922A (en) * 2014-08-15 2014-12-10 电子科技大学 Frequency selection method in early fault diagnosis of analog circuit
US20180038909A1 (en) * 2015-08-07 2018-02-08 Hefei University Of Technology Analog circuit fault diagnosis method using single testable node
CN105973594A (en) * 2016-04-25 2016-09-28 西北工业大学 Rolling bearing fault prediction method based on continuous deep belief network
US20180025489A1 (en) * 2016-07-25 2018-01-25 Case Western Reserve University Quantifying mass effect deformation with structural radiomics in brain tumor patients
CN107480386A (en) * 2017-08-22 2017-12-15 哈尔滨工业大学 A kind of test and excitation method for optimizing based on response aliasing measurement with genetic algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
S. COLLINS等: "An analogue radial basis function circuit using a compact Euclidean distance calculator", 《PROCEEDINGS OF IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS - ISCAS "94》 *
梁生欣等: "基于物理设计约束的模拟电路尺寸设计的优化算法GMSGA", 《复旦学报（自然科学版）》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114115166A (en) * 2020-08-26 2022-03-01 南京依维柯汽车有限公司 Error-proofing and tracing management method applying commercial vehicle electric control system
CN114115166B (en) * 2020-08-26 2024-04-02 南京依维柯汽车有限公司 Error-proofing and traceability management method for electronic control system of commercial vehicle
CN112464551A (en) * 2020-09-27 2021-03-09 电子科技大学 Analog circuit fault parameter range determining method based on non-dominance
CN112464551B (en) * 2020-09-27 2023-06-02 电子科技大学 Non-dominant analog circuit fault parameter range determining method

Also Published As

Publication number Publication date
CN111308327B (en) 2021-01-26

Similar Documents

Publication Publication Date Title
CN109581203B (en) Post-test simulation fault diagnosis method based on genetic algorithm
CN111260063B (en) Analog circuit fault positioning and parameter identification method based on genetic algorithm
US7406451B2 (en) Method for training neural networks
US6606580B1 (en) Fault diagnosis
CN109839583B (en) Analog circuit multi-fault diagnosis method based on improved genetic algorithm
CN110907810B (en) Analog circuit single fault diagnosis method based on particle swarm algorithm
CN111308327B (en) Analog circuit fault location and fault element parameter identification method
CN112485652B (en) Analog circuit single fault diagnosis method based on improved sine and cosine algorithm
CN110470980B (en) Analog circuit fault characteristic range determining method based on genetic algorithm
CN110470979B (en) Analog circuit fault diagnosis method based on fault characteristic region
CN113218537B (en) Training method, training device, training equipment and training storage medium for temperature anomaly detection model
CN110308384B (en) Analog circuit fault diagnosis method based on circle model and neural network
CN111814342B (en) Complex equipment reliability hybrid model and construction method thereof
CN110673017B (en) Analog circuit fault element parameter identification method based on genetic algorithm
CN109964182A (en) Method and system for vehicle analysis
CN112505533A (en) Analog circuit double-fault diagnosis method based on improved particle swarm optimization
CN113761755B (en) Accelerated life analysis method under temperature and humidity dual stress by considering cognitive uncertainty
CN114970157A (en) Method for predicting test life of small sample of electronic product under voltage stress
CN112505532A (en) Analog circuit single fault diagnosis method based on improved particle swarm optimization
CN110850358A (en) Electric energy meter comprehensive verification method and system based on stepwise regression algorithm
CN114511025A (en) Fan fault diagnosis method and device based on weighted multi-sensor fusion filtering
CN109948267B (en) Linear analog circuit fault diagnosis method based on circular model parameters
CN115408949B (en) Load model parameter identification method System, apparatus and medium
CN110308386B (en) Analog circuit fault diagnosis method based on polynomial fitting and state monitoring
CN112287628B (en) Constraint optimization-based analog circuit fault parameter range determination method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant