CN109581203A - Survey post-simulation method for diagnosing faults based on genetic algorithm - Google Patents
Survey post-simulation method for diagnosing faults based on genetic algorithm Download PDFInfo
- Publication number
- CN109581203A CN109581203A CN201811321491.4A CN201811321491A CN109581203A CN 109581203 A CN109581203 A CN 109581203A CN 201811321491 A CN201811321491 A CN 201811321491A CN 109581203 A CN109581203 A CN 109581203A
- Authority
- CN
- China
- Prior art keywords
- population
- fault
- individual
- representative
- output voltage
- 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
Links
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)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a kind of survey post-simulation method for diagnosing faults based on genetic algorithm, measurement obtains output voltage of the analog circuit under different frequency pumping signal at measuring point first, then analysis obtains the transfer function and ambiguity group of analog circuit, each representative fault element of fuzzy group selection one, each representativeness fault element corresponding point of population when initialization population, representative fault element parameter value value in fault coverage in the corresponding individual for dividing population, other fault elements value in range of tolerable variance, each point of population is intersected respectively first when each iteration, variation generates sub- population, after merging with father population, next-generation population is preferably obtained further according to target function value, the representative fault element that parameter value is located in fault coverage in optimum individual in last generation population is fault diagnosis result.The present invention can effectively improve fault diagnosis accuracy rate.
Description
Technical field
The invention belongs to Analog Circuit Fault Diagnosis Technology fields, more specifically, are related to a kind of based on genetic algorithm
Survey post-simulation method for diagnosing faults.
Background technique
Currently, mainly having emulation (such as failure dictionary method) and survey post-simulation before survey in analog circuit fault diagnosing field
Method.Emulation is to be emulated before testing according to the possible breakdown to circuit such as circuit diagram and parameter, and failure is rung before surveying
It should store, after circuit malfunctions, with the identical excitation used when constructing dictionary before, measure failure response.Then
It goes to search most similar response therewith from dictionary, to find failure.The advantages of this method is fault diagnosis fast speed, but
Disadvantage is equally obvious, that is, when constructing dictionary, needs exhaustive institute faulty.
Summary of the invention
The survey post-simulation failure based on genetic algorithm that it is an object of the invention to overcome the deficiencies of the prior art and provide a kind of
Diagnostic method, using genetic algorithm find with the immediate output voltage of output voltage when analog circuit fault, thus obtain therefore
Hinder diagnostic result.
It for achieving the above object, include following step the present invention is based on the survey post-simulation method for diagnosing faults of genetic algorithm
It is rapid:
S1: the transfer function of analog circuit is obtained；
S2: carrying out fuzzy group analysis to analog circuit, represents for each representative fault element of fuzzy group selection one, note
Property fault element quantity be N, remember that the quantity of other non-representative fault elements is M；
S3: when analog circuit breaks down, measurement obtains the output at measuring point t under K different frequency pumping signal
Voltage Respectively indicate output voltageReal and imaginary parts, j is imaginary unit, k=1,
2,…,K；
S4: with X={ x1,…,xN,x′1..., x 'MAs the individual in genetic algorithm, wherein xnIndicate n-th of representativeness
The parameter value of fault element, n=1,2 ..., N, x 'mIndicate the parameter value of m-th of non-representative fault element, m=1,2 ...,
M；1 point of population p is generated respectively to each representative fault elementn, dividing population pnEach of in individual, n-th is representative
The parameter value x of fault elementnThe parameter value of the value in the fault coverage of the representativeness fault element, other fault elements is holding
Value in poor range；Then by N number of point of population pnMerge, constitute population P, remembers that individual amount is G in population P；
S5: judge whether otherwise the iteration termination condition for reaching genetic algorithm enters step if so, entering step S10
S6；
S6: respectively to each point of population pnIntersection and mutation operation are carried out, sub- population q is obtainedn, N number of sub- population qnConstitute kind
Group Q；When intersect with mutation operation, need to guarantee sub- population qnIn n-th of representative fault element parameter value xnDo not surpass
The fault coverage of the representativeness fault element is crossed, the parameter value of other fault elements is no more than its range of tolerable variance；
S7: population P and population Q are merged, and constitute population S；
S8: each of population S individual is substituted into transfer function respectively, obtains surveying under K different frequency pumping signal
Output voltage U at point tk,g=αk,g+jβk,g, αk,g、βk,gRespectively indicate output voltage Uk,gReal and imaginary parts, g=1,
Then 2 ..., 2G are calculated using the following equation the Europe between g-th of individual output voltage and the output voltage of present day analog circuit
Formula distance Dg, calculation formula is as follows:
S9: according to Euclidean distance DgPreferably then G individual divides as next-generation population P and obtains each point from population S
Population pn, return step S5, the method for dividing population dividing is as follows:
For each individual in current population P, successively judge whether the parameter value of each representative fault element is located at
In range of tolerable variance, if it is, next representative fault element is judged, if it is not, then illustrating the representativeness fault element
Parameter value be located in fault coverage, divide population p for what the individual was divided to corresponding representative fault elementnIn；
S10: the smallest individual of Euclidean distance is selected from current population P, parameter value is located in fault coverage in the individual
Representative fault element be fault diagnosis result.
The present invention is based on the survey post-simulation method for diagnosing faults of genetic algorithm, measurement first obtains analog circuit in different frequencies
Output voltage under rate pumping signal at measuring point, then analysis obtains the transfer function and ambiguity group of analog circuit, each fuzzy
The representative fault element of group selection one, when initialization population each representative corresponding point of population of fault element, it is representative
Fault element parameter value value, other fault elements in fault coverage in the corresponding individual for dividing population take in range of tolerable variance
Value, when each iteration, first intersects each point of population respectively, making a variation generates sub- population, will be every after merging with father population
Individual between the output voltage obtained under different frequency pumping signal and the output voltage of present day analog circuit it is European away from
From as target function value, next-generation population is preferably obtained, parameter value is located at failure model in optimum individual in last generation population
Representative fault element in enclosing is fault diagnosis result.The present invention is defeated when being found using genetic algorithm with analog circuit fault
The immediate output voltage of voltage out, to obtain fault diagnosis result.It can be found using the present invention and not deposited in advance
The source of trouble of storage improves fault diagnosis accuracy rate.
Detailed description of the invention
Fig. 1 is the specific embodiment flow chart of the survey post-simulation method for diagnosing faults the present invention is based on genetic algorithm；
Fig. 2 is the topological diagram of second order Thomas analogue filter circuit in the present embodiment.
Specific embodiment
A specific embodiment of the invention is described with reference to the accompanying drawing, preferably so as to those skilled in the art
Understand the present invention.Requiring particular attention is that in the following description, when known function and the detailed description of design perhaps
When can desalinate main contents of the invention, these descriptions will be ignored herein.
Fig. 1 is the specific embodiment flow chart of the survey post-simulation method for diagnosing faults the present invention is based on genetic algorithm.Such as
Shown in Fig. 1, the specific steps the present invention is based on the survey post-simulation method for diagnosing faults of genetic algorithm include:
S101: transfer function is obtained:
Obtain the transfer function of analog circuit.
S102: fuzzy group analysis:
Fuzzy group analysis is carried out to analog circuit, it is representative for each representative fault element of fuzzy group selection one, note
The quantity of fault element is N, it is clear that N also illustrates that ambiguity group quantity, remembers that the quantity of other non-representative fault elements is M.
S103: determine that analog circuit currently exports:
When analog circuit breaks down, measurement obtains the output voltage at measuring point t under K different frequency pumping signal Respectively indicate output voltageReal and imaginary parts, j is imaginary unit, k=1,2 ..., K.
In order to keep the output voltage under malfunction more accurate, can repeatedly measure at corresponding frequencies carry out after output voltage it is flat
, to obtain the output voltage under each frequency.
S104: initialization Population in Genetic Algorithms:
With X={ x1,…,xN,x′1..., x 'MAs the individual in genetic algorithm, wherein xnIndicate n-th of representative event
Hinder the parameter value of element, n=1,2 ..., N, x 'mIndicate the parameter value of m-th of non-representative fault element, m=1,2 ..., M.
1 point of population p is generated respectively to each representative fault elementn, dividing population pnEach of in individual, n-th it is representative therefore
Hinder the parameter value x of elementnThe value in the fault coverage of the representativeness fault element, other fault element (i.e. other N-1 generations
Table fault element and M non-representative fault elements) parameter value in range of tolerable variance value.Then by N number of point of population pn
Merge, constitute population P, remembers that individual amount is G in population P.In general, in order to make corresponding to each representative fault element
Diagnosis probability of the failure when starting is equal, each point of population p in initialization populationnIn individual amount it is identical.
S105: judge whether otherwise the iteration termination condition for reaching genetic algorithm enters if so, entering step S110
Step S106.There are two types of the iteration termination condition of genetic algorithm is general, first is that reaching maximum number of iterations, first is that target function value
Reach preset threshold, can be configured according to actual needs.
S106: sub- population is generated:
Respectively to each point of population pnIntersection and mutation operation are carried out, sub- population q is obtainedn, N number of sub- population qnConstitute population
Q.When intersect with mutation operation, need to guarantee sub- population qnIn n-th of representative fault element parameter value xnIt is no more than
The parameter value of the fault coverage of the representativeness fault element, other fault elements is no more than its range of tolerable variance.
S107: merge population:
Population P and population Q are merged, population S, i.e. S=P ∪ Q are constituted, it is clear that merging individual amount in population is
2G。
S108: individual goal functional value is calculated:
Next it needs to calculate separately target function value to each individual in population S, is to use for the present invention
Each individual is European between the output voltage obtained under different frequency pumping signal and the output voltage of present day analog circuit
Distance is as objective function, therefore circular is as follows:
Each of population S individual is substituted into transfer function respectively, is obtained under K different frequency pumping signal in measuring point t
The output voltage U at placek,g=αk,g+jβk,g, αk,g、βk,gRespectively indicate output voltage Uk,gReal and imaginary parts, g=1,2 ...,
2G, be then calculated using the following equation between g-th of individual output voltage and the output voltage of present day analog circuit it is European away from
From Dg, calculation formula is as follows:
Obviously for fault diagnosis, it may be that apart from smaller, expression output voltage and present day analog circuit and output electricity
Press it is closer, individual it is more excellent.
S109: next-generation population is generated:
According to Euclidean distance DgPreferably then G individual divides as next-generation population P and obtains each point of kind from population S
Group pn, return step S105.The method for dividing population dividing is as follows:
For each individual in current population P, successively judge whether the parameter value of each representative fault element is located at
In range of tolerable variance, if it is, next representative fault element is judged, if it is not, then illustrating the representativeness fault element
Parameter value be located in fault coverage, divide population p for what the individual was divided to corresponding representative fault elementnIn.According to population
It initializes and intersects, the specific method of mutation operation is it is found that only have the parameter of a representative fault element in each individual
Value is located in fault coverage, and the parameter value of other representative fault elements and non-representative fault element is respectively positioned on range of tolerable variance
It is interior, it can divide to obtain a point population accordingly.
S110: fault diagnosis result is determined:
The smallest individual of Euclidean distance is selected from current population, parameter value is located at the representative in fault coverage in the individual
Property fault element is fault diagnosis result.
Embodiment
Technical solution in order to better illustrate the present invention, by taking second order Thomas's analogue filter circuit as an example to the present invention into
Row is described in detail.Fig. 2 is the topological diagram of the present embodiment second order Thomas's analogue filter circuit.As shown in Fig. 2, the two of the present embodiment
It include 3 amplifiers, 6 resistance and 2 capacitors in rank Thomas's analogue filter circuit, with resistance R1Input as whole
The input of a circuit, the output using the output of the 3rd amplifier as entire circuit, as measuring point t.The biography of circuit shown in Fig. 2
Defeated function is shown below:
Wherein, ω indicates angular frequency.
According to Symbolic Analysis Method and transfer function it is found that the ambiguity group situation of the circuit are as follows: { R1, { R2, { R4,R5,R6,
C2, { R3,C1}.The failure undistinguishable of ambiguity group internal element can be distinguished on the failure theory between ambiguity group.This reality
The representative fault element for applying 4 ambiguity groups in example is respectively R1,R2,R3,R4。
It is randomly provided a failure, such as R2=12000 Ω, other elements random value in range of tolerable variance, R1=10303
Ω, R3=10037 Ω, R4=10138 Ω, R5=9813 Ω, R5=10105 Ω, R4=10138 Ω, C1=10.018nF, C2=
10.207nF.Emulate to obtain transfer function in 500Hz, 1000Hz { R using PSPICE3,C1And 1500Hz excitation under output
{R3,C1Voltage difference it is as follows:
Initialization population P, population P are total to contain 160 individuals, and a total of 4 points of populations each divide population to contain 40
Individual, each individual vector X={ R1,R2,R3,R4,R5,R6,C1,C2}.With representative fault element R1Corresponding divides population p1
For, the value range of each fault element parameter value is as follows in individual: R1∈[10Ω,9500Ω)∪(10500Ω,107
Ω], R2∈ [9500 Ω, 10500 Ω], R3∈ [9500 Ω, 10500 Ω], R4∈ [9500 Ω, 10500 Ω], R5∈[9500
Ω, 10500 Ω], R6∈ [9500 Ω, 10500 Ω], C1∈ [9.5nF, 10.5nF], C2∈ [9.5nF, 10.5nF], that is, represent
Property fault element R1The value in fault coverage, other fault elements value in range of tolerable variance.
4 points of populations are intersected and made a variation respectively, 4 sub- population q are generated1、q2、q3、q4, generation is corresponded in sub- population
The parameter value of table fault element also should in fault coverage value, the parameter value of other fault elements value in range of tolerable variance
It is then combined with P, q1、q2、q3、q4, obtain population S.It brings the individual parameter value in population S into transfer function, calculates all individuals
Output voltage under three frequencies [500Hz, 1000Hz, 1500Hz], and calculate the output voltage of itself and present day analog circuit
Euclidean distance as fitness function.Using championship alternative method, next-generation population P is selected, and is therefrom isolated down
4 points of populations of a generation, into next iteration.
In the present embodiment be arranged maximum number of iterations be 500, by 500 instead of after, select from current population P to current
The nearest individual of the output voltage of analog circuit is as optimal solution.Table 1 is fault diagnosis contrast table in the present embodiment.
| Element | Element nominal value | Parameter is set | Genetic algorithm optimal solution |
| R1 | 10000Ω | 10303Ω | 10121Ω |
| R2 | 10000Ω | 12000Ω | 12080Ω |
| R3 | 10000Ω | 10037Ω | 9859Ω |
| R4 | 10000Ω | 10138Ω | 10233Ω |
| R5 | 10000Ω | 9813Ω | 9947Ω |
| R6 | 10000Ω | 10105Ω | 9729Ω |
| C1 | 10nF | 10.018nF | 9.9222nF |
| C2 | 10nF | 10.207nF | 9.8781nF |
Table 1
As shown in table 1, the 2nd be classified as each element nominal value, whens the 3rd broomrape front simulation circuit malfunctions each component parameters
Value, it is seen that resistance R2Parameter exceeds nominal value 20%, is fault element, and other elements are normal.The optimal solution that genetic algorithm obtains
Display element R in individual2Parameter value exceed nominal value 20.8%, other device parameter values in range of tolerable variance, therefore, it is determined that
Element R2Failure, it is seen that fault diagnosis is correct.
Table 2 is the output electricity of the corresponding output voltage of genetic algorithm optimal solution individual and present day analog circuit in the present embodiment
Press contrast table.
| 500Hz | 1000Hz | 1500Hz | |
| Current output voltage | -0.9964+0.2923j | -0.9146+0.8037j | -0.1563+1.2095j |
| Optimal solution output voltage | -0.9958+0.2931j | -0.9118+0.8042j | -0.1547+1.2055j |
Table 2
To the output electricity of genetic algorithm optimal solution individual corresponding output voltage and present day analog circuit listed in table 2
Pressure is compared, it is known that the two is very close, this is it is also seen that diagnosis of the invention is effective.
Although the illustrative specific embodiment of the present invention is described above, in order to the technology of the art
Personnel understand the present invention, it should be apparent that the present invention is not limited to the range of specific embodiment, to the common skill of the art
For art personnel, if various change the attached claims limit and determine the spirit and scope of the present invention in, these
Variation is it will be apparent that all utilize the innovation and creation of present inventive concept in the column of protection.
Claims (3)
1. a kind of survey post-simulation method for diagnosing faults based on genetic algorithm, which comprises the following steps:
S1: the transfer function of analog circuit is obtained；
S2: carrying out fuzzy group analysis to analog circuit, for each representative fault element of fuzzy group selection one, remembers representative event
The quantity for hindering element is N, remembers that the quantity of other non-representative fault elements is M；
S31: when analog circuit breaks down, measurement obtains the output voltage at measuring point t under K different frequency pumping signal Respectively indicate output voltageReal and imaginary parts, j is imaginary unit, k=1,2 ..., K；
S4: with X={ x1,…,xN,x′1..., x 'MAs the individual in genetic algorithm, wherein xnIndicate n-th of representative failure
The parameter value of element, n=1,2 ..., N, x 'mIndicate the parameter value of m-th of non-representative fault element, m=1,2 ..., M；It is right
Each representativeness fault element generates 1 point of population p respectivelyn, dividing population pnEach of in individual, n-th of representative failure
The parameter value x of elementnThe value in the fault coverage of the representativeness fault element, the parameter value of other fault elements is in tolerance model
Enclose interior value；Then by N number of point of population pnMerge, constitute population P, remembers that individual amount is G in population P；
S5: judge whether otherwise the iteration termination condition for reaching genetic algorithm enters step S6 if so, entering step S10；
S6: respectively to each point of population pnIntersection and mutation operation are carried out, sub- population q is obtainedn, N number of sub- population qnConstitute population Q；
When intersect with mutation operation, need to guarantee sub- population qnIn n-th of representative fault element parameter value xnNo more than this
The parameter value of the fault coverage of representative fault element, other fault elements is no more than its range of tolerable variance；
S7: population P and population Q are merged, and constitute population S；
S8: each of population S individual is substituted into transfer function respectively, is obtained under K different frequency pumping signal at measuring point t
Output voltage Uk,g=αk,g+jβk,g, αk,g、βk,gRespectively indicate output voltage Uk,gReal and imaginary parts, g=1,2 ..., 2G,
Then the Euclidean distance being calculated using the following equation between g-th of individual output voltage and the output voltage of present day analog circuit
Dg, calculation formula is as follows:
S9: according to Euclidean distance DgPreferably then G individual divides as next-generation population P and obtains each point of population from population S
pn, return step S5, the method for dividing population dividing is as follows:
For each individual in current population P, successively judge whether the parameter value of each representative fault element is located at tolerance
In range, if it is, next representative fault element is judged, if it is not, then illustrating the ginseng of the representativeness fault element
Numerical value is located in fault coverage, divides population p for what the individual was divided to corresponding representative fault elementnIn；
S10: the smallest individual of Euclidean distance is selected from current population P, parameter value is located at the generation in fault coverage in the individual
Table fault element is fault diagnosis result.
2. method for diagnosing faults after survey according to claim 1, which is characterized in that output voltage in the step S3For
The average output voltage repeatedly averagely obtained after measurement output voltage at corresponding frequencies.
3. method for diagnosing faults after survey according to claim 1, which is characterized in that in the step S4 when initialization population
Each point of population pnIn individual amount it is identical.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811321491.4A CN109581203B (en) | 2018-11-07 | 2018-11-07 | Post-test simulation fault diagnosis method based on genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811321491.4A CN109581203B (en) | 2018-11-07 | 2018-11-07 | Post-test simulation fault diagnosis method based on genetic algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109581203A true CN109581203A (en) | 2019-04-05 |
| CN109581203B CN109581203B (en) | 2020-10-16 |
Family
ID=65921715
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811321491.4A Active CN109581203B (en) | 2018-11-07 | 2018-11-07 | Post-test simulation fault diagnosis method based on genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109581203B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110109005A (en) * | 2019-05-24 | 2019-08-09 | 电子科技大学 | A kind of analog circuit fault test method based on sequential test |
| CN110907810A (en) * | 2019-12-02 | 2020-03-24 | 电子科技大学 | Analog circuit single fault diagnosis method based on particle swarm algorithm |
| CN111260063A (en) * | 2020-01-10 | 2020-06-09 | 电子科技大学 | Analog circuit fault positioning and parameter identification method based on genetic algorithm |
| CN112444737A (en) * | 2020-09-21 | 2021-03-05 | 电子科技大学 | Method for determining fault parameter range of analog circuit |
| CN112615623A (en) * | 2020-12-23 | 2021-04-06 | 电子科技大学 | Single fault diagnosis method of inverted T-shaped DAC (digital-to-analog converter) conversion circuit |
| CN113050547A (en) * | 2021-03-04 | 2021-06-29 | 合肥宏晶微电子科技股份有限公司 | Test stimulus generation method, test method, electronic device, and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6782515B2 (en) * | 2002-01-02 | 2004-08-24 | Cadence Design Systems, Inc. | Method for identifying test points to optimize the testing of integrated circuits using a genetic algorithm |
| JP2011070336A (en) * | 2009-09-25 | 2011-04-07 | Fujitsu Ltd | Automatic circuit-designing pareto data generation program, method and device, and automatic circuit design program, method and device |
| CN102087337A (en) * | 2009-12-04 | 2011-06-08 | 哈尔滨理工大学 | Annealing genetic optimization method for diagnosing excitation of nonlinear analog circuit |
| CN103926526A (en) * | 2014-05-05 | 2014-07-16 | 重庆大学 | Analog circuit fault diagnosis method based on improved RBF neural network |
| CN104698365A (en) * | 2015-03-23 | 2015-06-10 | 电子科技大学 | Unreliability test optimizing method based on grouping genetic algorithm |
| CN105187051A (en) * | 2015-07-14 | 2015-12-23 | 北京航空航天大学 | Power and area optimization method of incomplete certain Reed-Muller circuit based on NSGA-II |
| GB2546324A (en) * | 2016-01-18 | 2017-07-19 | Nat Chung Shan Inst Of Science And Tech | Method and device for correcting antenna phase |
| CN107169514A (en) * | 2017-05-05 | 2017-09-15 | 清华大学 | The method for building up of diagnosing fault of power transformer model |
| CN108233716A (en) * | 2016-12-21 | 2018-06-29 | 电子科技大学 | A kind of method for optimally designing parameters modeled based on genetic algorithm and DC-DC converter |
-
2018
- 2018-11-07 CN CN201811321491.4A patent/CN109581203B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6782515B2 (en) * | 2002-01-02 | 2004-08-24 | Cadence Design Systems, Inc. | Method for identifying test points to optimize the testing of integrated circuits using a genetic algorithm |
| JP2011070336A (en) * | 2009-09-25 | 2011-04-07 | Fujitsu Ltd | Automatic circuit-designing pareto data generation program, method and device, and automatic circuit design program, method and device |
| CN102087337A (en) * | 2009-12-04 | 2011-06-08 | 哈尔滨理工大学 | Annealing genetic optimization method for diagnosing excitation of nonlinear analog circuit |
| CN103926526A (en) * | 2014-05-05 | 2014-07-16 | 重庆大学 | Analog circuit fault diagnosis method based on improved RBF neural network |
| CN104698365A (en) * | 2015-03-23 | 2015-06-10 | 电子科技大学 | Unreliability test optimizing method based on grouping genetic algorithm |
| CN105187051A (en) * | 2015-07-14 | 2015-12-23 | 北京航空航天大学 | Power and area optimization method of incomplete certain Reed-Muller circuit based on NSGA-II |
| GB2546324A (en) * | 2016-01-18 | 2017-07-19 | Nat Chung Shan Inst Of Science And Tech | Method and device for correcting antenna phase |
| CN108233716A (en) * | 2016-12-21 | 2018-06-29 | 电子科技大学 | A kind of method for optimally designing parameters modeled based on genetic algorithm and DC-DC converter |
| CN107169514A (en) * | 2017-05-05 | 2017-09-15 | 清华大学 | The method for building up of diagnosing fault of power transformer model |
Non-Patent Citations (2)
| Title |
|---|
| YANGHONG TAN等: "A Novel Method for Analog Fault Diagnosis Based on Neural Networks and Genetic Algorithms", 《IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT》 * |
| 王硕: "动态自适应遗传算法在模拟电路中的应用", 《东莞理工学院学报》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110109005A (en) * | 2019-05-24 | 2019-08-09 | 电子科技大学 | A kind of analog circuit fault test method based on sequential test |
| CN110907810A (en) * | 2019-12-02 | 2020-03-24 | 电子科技大学 | Analog circuit single fault diagnosis method based on particle swarm algorithm |
| CN110907810B (en) * | 2019-12-02 | 2021-01-26 | 电子科技大学 | Analog circuit single fault diagnosis method based on particle swarm algorithm |
| CN111260063A (en) * | 2020-01-10 | 2020-06-09 | 电子科技大学 | Analog circuit fault positioning and parameter identification method based on genetic algorithm |
| CN111260063B (en) * | 2020-01-10 | 2023-05-30 | 电子科技大学 | Analog circuit fault positioning and parameter identification method based on genetic algorithm |
| CN112444737A (en) * | 2020-09-21 | 2021-03-05 | 电子科技大学 | Method for determining fault parameter range of analog circuit |
| CN112444737B (en) * | 2020-09-21 | 2021-10-22 | 电子科技大学 | Method for determining fault parameter range of analog circuit |
| CN112615623A (en) * | 2020-12-23 | 2021-04-06 | 电子科技大学 | Single fault diagnosis method of inverted T-shaped DAC (digital-to-analog converter) conversion circuit |
| CN113050547A (en) * | 2021-03-04 | 2021-06-29 | 合肥宏晶微电子科技股份有限公司 | Test stimulus generation method, test method, electronic device, and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109581203B (en) | 2020-10-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109581203A (en) | Survey post-simulation method for diagnosing faults based on genetic algorithm | |
| CN102721941B (en) | Method for fusing and diagnosing fault information of circuit of electric meter on basis of SOM (self-organized mapping) and D-S (Dempster-Shafer) theories | |
| CN109839583A (en) | Analog circuit Multiple faults diagnosis approach based on improved adaptive GA-IAGA | |
| CN107607851B (en) | Voltage regulation system and method | |
| AU2015293548B2 (en) | Method for detecting anomalies in a distribution network, in particular for drinking water | |
| CN107703418B (en) | Shelf depreciation location error compensation method based on more radial base neural nets | |
| CN111260063B (en) | Analog circuit fault positioning and parameter identification method based on genetic algorithm | |
| CN107525526A (en) | The apparatus and method of probabilistic measurement result on transmission function are provided | |
| CN107066710B (en) | Heat supply pipe network resistance characteristic identification method and system based on measurement data | |
| CN110907810B (en) | Analog circuit single fault diagnosis method based on particle swarm algorithm | |
| WO2012045498A1 (en) | Detection of loss or malfunctions in electrical distribution networks | |
| JP6432890B2 (en) | Monitoring device, target device monitoring method, and program | |
| CN107133476B (en) | Test excitation and test point collaborative optimization method based on response aliasing measurement | |
| CN110470979B (en) | Analog circuit fault diagnosis method based on fault characteristic region | |
| AU2015315838A1 (en) | Apparatus and method for ensembles of kernel regression models | |
| CN106685749B (en) | The method of inspection and device of network flow | |
| CN110470980A (en) | Method is determined based on the analog circuit fault characteristic range of genetic algorithm | |
| CN110308384A (en) | Analog-circuit fault diagnosis method based on circle model and neural network | |
| US10571314B2 (en) | Signal processing apparatus and signal processing method | |
| CN105372071B (en) | A kind of aerial engine air passage unit failure detection method | |
| CN102445650B (en) | Blind signal separation algorithm-based circuit fault diagnosis method | |
| CN107861082B (en) | Calibration interval determining method and device of electronic measuring equipment | |
| CN110632521B (en) | Fusion estimation method for lithium ion battery capacity | |
| WO2016136391A1 (en) | Fault point locating device and method, electric power system monitoring system, and facility planning support system | |
| CN104090228A (en) | Analog circuit fuzzy group identification 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 |