CN110457776B - Test strategy rapid generation method based on fault decision network - Google Patents

Test strategy rapid generation method based on fault decision network Download PDF

Info

Publication number
CN110457776B
CN110457776B CN201910661767.1A CN201910661767A CN110457776B CN 110457776 B CN110457776 B CN 110457776B CN 201910661767 A CN201910661767 A CN 201910661767A CN 110457776 B CN110457776 B CN 110457776B
Authority
CN
China
Prior art keywords
fault
test
point
optimal
cost
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.)
Active
Application number
CN201910661767.1A
Other languages
Chinese (zh)
Other versions
CN110457776A (en
Inventor
刘震
梅文娟
杜立
程玉华
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 CN201910661767.1A priority Critical patent/CN110457776B/en
Publication of CN110457776A publication Critical patent/CN110457776A/en
Application granted granted Critical
Publication of CN110457776B publication Critical patent/CN110457776B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Abstract

The invention discloses a test strategy rapid generation method based on a fault decision network, which comprises the steps of constructing a fault dependency matrix and the fault decision network of a system to be tested, generating an optimal test point through a heuristic search algorithm, and further obtaining an optimal test strategy, so that faults in the network are isolated through an optimal search path; therefore, by combining the influence of the dependence information on the generation of the optimal strategy, the heuristic function value is accurately estimated, the deviation of the heuristic function estimation and the search strategy decision in application is reduced, and meanwhile, the optimal solution upper limit is set for each search through the setting of opportunity cost, so that the search process is controlled, and the aim of improving the efficiency is fulfilled.

Description

Test strategy rapid generation method based on fault decision network
Technical Field
The invention belongs to the technical field of fault diagnosis, and particularly relates to a test strategy rapid generation method based on a fault decision network.
Background
With the increasing development of electronic information technology, the circuit design inside the complex equipment becomes complex, the system state is determined timely and accurately, internal faults are isolated, and the development, test and setting time of the equipment system can be effectively shortened. How to design an efficient fault testing scheme has become one of the research hotspots in the field of complex equipment system design.
In the existing design method of the fault test scheme, a test sequence test method is provided based on a signal flow diagram given in preliminary design and a circuit relation described by a correlation model in sequential test, so that the cost generated by the test is reduced, and the efficiency of later-stage design and verification evaluation can be effectively improved.
In recent years, the AO algorithm improves the search efficiency of the optimal solution by adopting a mode of combining heuristic search and or graph search, and becomes a widely used diagnostic strategy generation method. The method generates a decision tree model for isolating each fault based on the logical relationship of the fault fuzzy set and the measuring point information, thereby greatly improving the efficiency of fault diagnosis. However, in the AO algorithm and the related improved method thereof, only probability information of a fault and cost information of each measuring point are considered in heuristic cost evaluation, and influence of a fault dependency relationship on heuristic search is ignored, so that a strategy of the heuristic search is limited, and test optimization efficiency of the algorithm is influenced.
Patent 201910438846.0 combines heuristic search with dynamic programming, and improves the solving efficiency by reducing the number of repeated search of the optimal solution, however, the setting of the heuristic function of the patent only considers the relation between the test cost and the failure probability, and ignores the influence caused by information dependence in the heuristic search, so that the estimation of the heuristic function may have great deviation from the real test cost, and further influences the solving efficiency of the optimal test strategy. Different from patent 201910438846.0, a method for rapidly generating a test strategy based on a fault decision network is provided, which generates an optimal test strategy by using a heuristic evaluation value through a dependency relationship between a fault and a test point, thereby isolating the fault in the network through an optimal search path.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a test strategy rapid generation method based on a fault decision network.
In order to achieve the above object, the present invention provides a method for rapidly generating a test policy based on a fault decision network, which is characterized by comprising the following steps:
(1) and constructing a fault dependence matrix H ═ { S, P, T, C, D } of the system to be tested, wherein S ═ S { (S) }1,s2,…,si,…,sNIs a set of fault types, siRepresenting the ith fault type, and N representing the total number of the fault types; p ═ P1,p2,…,pi,…,pNIs a set formed by prior fault probabilities corresponding to fault types, piIs as siCorresponding fault prior probability; t ═ T1,t2,…,tj,…,tMIs the set corresponding to the test point, tjRepresenting the jth test point, and M representing the total number of the test points; c ═ C1,c2,…,cj,…,cMIs a set of test costs corresponding to the test points, cjIs tjCorresponding test cost; d ═ DijIs a dependency information matrix of dimension N x M, d ij1 denotes siQuilt tjMeasure, otherwise dij＝0；
(2) And constructing a fault decision network G ═ G of the system to be testedikIn which g isikRepresentation for isolating siAnd skK ≠ i, k ═ 1,2 …, N;
optimal measuring point gikThe calculation formula of (2) is as follows:
gik＝argmin{cj|dijΔdkj＝1}
wherein Δ represents an exclusive or operation;
(3) generating an optimal test strategy through a heuristic search algorithm
(3.1) setting a fault set S ═ S of the initial search node, a usable test set T ═ T, and an opportunity cost value C ═ Cmax，CmaxIs the set maximum value of the test cost; setting an optimal measuring point topt；
(3.2) calculating a heuristic function evaluation value based on a fault decision network;
Figure GDA0003586358590000021
wherein the content of the first and second substances,
Figure GDA0003586358590000022
represents the measured point tjAt fault siThe evaluation value of the heuristic function in the following,
Figure GDA0003586358590000023
and
Figure GDA0003586358590000024
the evaluation function values of the two separate sub-fault sets are respectively;
(3.3) finding out a measuring point t to be searchedo；
Figure GDA0003586358590000025
(3.4) if the point t is measuredoIf the test optimal solution has been found or the corresponding heuristic function value is greater than the opportunity cost value C, the test point t is measuredoMarked as optimal measurement point toptAnd jumping to the step (4), otherwise, directly entering the step (3.5);
(3.5) according to the measuring point toIncluding dependency information, separating fault subsets
Figure GDA00035863585900000321
And
Figure GDA00035863585900000322
Figure GDA0003586358590000031
Figure GDA0003586358590000032
(3.6) if
Figure GDA0003586358590000033
Containing only a single fault, indicating that the fault has been isolated, and recording
Figure GDA0003586358590000034
Number of isolation faults F 01 and a test Cost 00; otherwise, it will
Figure GDA0003586358590000035
Removing T as new fault setRemoving measuring point toThen as a new measuring point set, calculating
Figure GDA0003586358590000036
At the opportunity cost of
Figure GDA0003586358590000037
And returning to the step (3.2) until the step is found
Figure GDA0003586358590000038
Number of isolation faults of F0And a test Cost0；
(3.7) if
Figure GDA0003586358590000039
Containing only a single fault, which has been isolated, recorded
Figure GDA00035863585900000310
Number of isolation faults of F1＝1，Cost 10; otherwise, it will
Figure GDA00035863585900000311
As a new fault set, removing T from the measuring point ToThen as a new measuring point set, calculating
Figure GDA00035863585900000312
At the opportunity cost of
Figure GDA00035863585900000313
And returning to the step (3.2) until the step is found
Figure GDA00035863585900000314
Number of isolation faults F1And a test Cost1；
(3.8) updating the measuring point toA corresponding heuristic function value;
Figure GDA00035863585900000315
(3.9) calculating the current optimal fault isolation number;
Figure GDA00035863585900000316
(3.10) update opportunity cost value C of
Figure GDA00035863585900000317
(3.11) updating the measuring point to be searched
Figure GDA00035863585900000318
And measure the point
Figure GDA00035863585900000319
Marked as optimal measurement point toptAnd entering the step (4);
Figure GDA00035863585900000320
(4) generating a test strategy
(4.1) initializing a fault set of the root node as S' ═ S;
(4.2) finding out the optimal measuring point t corresponding to SoptAnd separating out the fault subset;
Figure GDA0003586358590000041
Figure GDA0003586358590000042
wherein the content of the first and second substances,
Figure GDA0003586358590000043
denotes siQuilt toptMeasure otherwise, otherwise
Figure GDA0003586358590000044
(4.3) if S'0If only a single fault is contained, the fault is isolated, and the algorithm is ended; otherwise, will S'0And returning to the step (4.2) as a new fault set S 'until S'0Only contains a single fault, and the algorithm is ended;
(4.4) if S'1If only a single fault is contained, the fault is isolated, and the algorithm is ended; otherwise, will S'1And returning to the step (4.2) as a new fault set S 'until S'1Contains only a single fault and the algorithm ends.
The invention aims to realize the following steps:
the invention relates to a test strategy rapid generation method based on a fault decision network, which comprises the steps of constructing a fault dependence matrix and the fault decision network of a system to be tested, generating an optimal measuring point through a heuristic search algorithm, and further obtaining an optimal test strategy, so that faults in the network are isolated through an optimal search path; therefore, the heuristic function value is accurately estimated by combining the influence of the dependence information on the generation of the optimal strategy, the deviation of the heuristic function estimation and the search strategy decision in the application is reduced, and meanwhile, the optimal solution upper limit is set for each search through the setting of opportunity cost, so that the search process is controlled, and the aim of improving the efficiency is fulfilled.
Drawings
FIG. 1 is a flow chart of a method for rapidly generating a test strategy based on a fault decision network according to the present invention;
FIG. 2 is a flow chart of a heuristic search to generate an optimal solution;
FIG. 3 is a comparison graph of heuristic function value field actual test costs;
FIG. 4 is a graph of the effect of upper optimization of left sub-fault sets at a root node on isolation of a solution to be searched;
FIG. 5 is a diagram of the effect of right sub-fault set optimization upper bound on the isolation of the solution to be searched at the root node;
FIG. 6 is a generated diagnostic tree.
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.
Examples
FIG. 1 is a flow chart of a test strategy rapid generation method based on a fault decision network according to the present invention.
In this embodiment, as shown in fig. 1, the method for quickly generating a test strategy based on a fault decision network according to the present invention establishes a more accurate heuristic evaluation mechanism to improve the efficiency of optimal solution search by establishing a graph decision network, and simultaneously establishes an opportunity cost further optimization solution process, and specifically includes the following steps:
s1, constructing a fault dependence matrix H ═ { S, P, T, C, D } of the system to be tested, wherein S ═ S { (S) }1,s2,…,si,…,sNIs a set of fault types, siRepresenting the ith fault type, and N representing the total number of the fault types; p ═ P1,p2,…,pi,…,pNIs a set formed by prior fault probabilities corresponding to fault types, piIs s isiCorresponding fault prior probability; t ═ T1,t2,…,tj,…,tMIs the set corresponding to the test point, tjRepresenting the jth test point, and M representing the total number of the test points; c ═ C1,c2,…,cj,…,cMIs a set of test costs corresponding to the test points, cjIs tjCorresponding test cost; d ═ DijIs a dependency information matrix of dimension N x M, d ij1 denotes siQuilt tjMeasure, otherwise dij＝0；
S2, constructing a fault decision network G-G of the system to be testedikIn which g isikRepresentation for isolating siAnd skK is not equal to i, k is 1,2 …, N;
optimal measuring point gikThe calculation formula of (c) is:
gik＝argmin{cj|dijΔdkj＝1}
wherein Δ represents an exclusive or operation;
s3, as shown in figure 2, generating the optimal test point by a heuristic search algorithm
S3.1, setting a fault set S ═ S of the initial search node, setting a usable test set T ═ T, and setting an opportunity cost value C ═ Cmax，CmaxIs the set maximum value of the test cost; setting an optimal measuring point topt；
S3.2, calculating a heuristic function evaluation value based on a fault decision network;
Figure GDA0003586358590000051
wherein the content of the first and second substances,
Figure GDA0003586358590000052
represents the measured point tjAt fault siThe evaluation value of the heuristic function in the following,
Figure GDA0003586358590000053
and
Figure GDA0003586358590000054
the evaluation function values of the left and right sub-fault sets are separated respectively;
wherein the function value is evaluated
Figure GDA0003586358590000055
And
Figure GDA0003586358590000056
the calculation method comprises the following steps:
Figure GDA0003586358590000057
wherein L is0+L1＝N，
Figure GDA0003586358590000061
Represents the isolation siAnd skThe cost of the measuring point corresponding to the optimal measuring point;
s3.3, finding out a measuring point t to be searchedo；
Figure GDA0003586358590000062
S3.4, if measure point toIf the test optimal solution has been found or the corresponding heuristic function value is greater than the opportunity cost value C, the test point t is measuredoMarked as optimal measurement point toptAnd jumping to step S4, otherwise, directly entering step S3.5;
s3.5, according to the measuring point toIncluding dependency information, separating left and right subsets of faults
Figure GDA0003586358590000063
And
Figure GDA0003586358590000064
Figure GDA0003586358590000065
Figure GDA0003586358590000066
s3.6, if
Figure GDA0003586358590000067
Containing only a single fault, indicating that the fault has been isolated, and recording
Figure GDA0003586358590000068
Number of isolation faults of F 01 and a test Cost 00; otherwise, it will
Figure GDA0003586358590000069
As a new fault set, removing T from the measuring point ToThen as a new measuring point set, calculating
Figure GDA00035863585900000610
At the opportunity cost of
Figure GDA00035863585900000611
And then returns to step S3.2 until the search is found
Figure GDA00035863585900000612
Number of isolation faults F0And a test Cost0；
S3.7, if
Figure GDA00035863585900000613
Containing only a single fault, which has been isolated, recorded
Figure GDA00035863585900000614
Number of isolation faults F1＝1，Cost 10; otherwise, it will
Figure GDA00035863585900000615
As a new fault set, removing T from the measuring point ToThen as a new measuring point set, calculating
Figure GDA00035863585900000616
At the opportunity cost of
Figure GDA00035863585900000617
And then returns to step S3.2 until the search is found
Figure GDA00035863585900000618
Number of isolation faults F1And a test Cost1；
S3.8, updating the measuring point toA corresponding heuristic function value;
Figure GDA00035863585900000619
s3.9, calculating the current optimal fault isolation number;
Figure GDA00035863585900000620
s3.10, update opportunity cost value C is
Figure GDA00035863585900000621
S3.11, updating measuring points to be searched
Figure GDA00035863585900000622
And measure the point
Figure GDA00035863585900000623
Marked as optimal measurement point toptAnd proceeds to step S4;
Figure GDA00035863585900000624
s4, generating test strategy
S4.1, initializing a fault set of the root node as S' ═ S;
s4.2, finding out the optimal measuring point t corresponding to SoptAnd separating out the fault subsets;
Figure GDA0003586358590000071
Figure GDA0003586358590000072
wherein the content of the first and second substances,
Figure GDA0003586358590000073
denotes siQuilt toptMeasure otherwise
Figure GDA0003586358590000074
S4.3, if S'0If only a single fault is contained, the fault is isolated, a test strategy is derived, and the algorithm is ended; otherwise, will S'0And returning to the step S4.2 as a new fault set S 'until S'0Only contains a single fault, and the algorithm is ended;
s4.4, if S'1If only a single fault is contained, the fault is isolated, a test strategy is derived, and the algorithm is ended; otherwise, will S'1And returning to the step S4.2 as a new fault set S 'until S'1Contains only a single fault and the algorithm ends.
Examples of the invention
In order to illustrate the technical effect of the invention, the invention is verified by taking an antitank system as an example.
The anti-tank system is a complete weapon system, consists of hydraulic, fuel and environment control subsystems and is used for striking and destroying heavy armored vehicles, the system has 13 system states and 12 available measuring points, and a fault dependence matrix, the prior probability corresponding to each system state and the test cost of each measuring point are shown in table 1. In order to verify the effect of the algorithm provided by the invention, an antitank system is selected as an example, and meanwhile, the conventional AO algorithm is used as a comparison algorithm to calculate the example together.
Table 1 is the fault-dependency matrix of the anti-tank system;
Figure GDA0003586358590000075
Figure GDA0003586358590000081
TABLE 1
The left and right subtree heuristic function evaluation values of the fault complete set under each measuring point, the overall heuristic function evaluation value and the actual test optimal cost which can be achieved by each measuring point are shown in FIG. 2. As can be seen from FIG. 2, the algorithm effectively estimates the test cost at each test point, and the estimation is used for guiding the search flow of test optimization.
Fig. 3 is a comparison graph of the heuristic function value and the actual test cost, and it can be seen from fig. 3 that the heuristic function evaluation value obtained through the graph network is close to the actual optimal solution test cost, so that the algorithm can perform accurate evaluation of the heuristic function by means of the graph network, and the solution efficiency is improved.
Fig. 4 and 5 are scatter diagrams between the upper search bound and the heuristic evaluation cost of the left and right subtrees at each measurement point respectively, the influence of the upper bound value on heuristic search can be seen through fig. 4 and 5, and when the upper cost bound of the optimal solution is smaller than the heuristic function evaluation value, that is, the scatter point is below the boundary, the program stops the test strategy search on the sub-fault set at the measurement point, thereby simplifying the search process.
Through the above optimization, the optimal troubleshooting tree generated by the present invention is shown in fig. 6, and the performance of the present invention on the antitank system is shown in table 2. As can be seen from table 2, the present invention has significant advantages in test optimization time.
Algorithm Optimizing time Number of searches Fault isolation rate Testing costs
AO* 32.678s 168249 100％ 4.752
DPAO* 0.711s 131 100％ 4.752
AOL 25.61s 31101 100％ 4.752
The invention 0.22s 178 100％ 4.752
TABLE 2
In summary, it can be seen from fig. 6 that the optimal fault tree generated by the present invention can accurately separate all faults in the system, and by comparing performance of the algorithms, the average cost of the fault trees generated by the two algorithms is the same, and the dynamic planning method is adopted in time to improve algorithm efficiency to a certain extent.
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 test strategy rapid generation method based on a fault decision network is characterized by comprising the following steps:
(1) and constructing a fault dependence matrix H ═ { S, P, T, C, D } of the system to be tested, wherein S ═ S { (S) }1,s2,…,si,…,sNIs a set of fault types, siRepresenting the ith fault type, and N representing the total number of the fault types; p ═ P1,p2,…,pi,…,pNIs a set formed by prior fault probabilities corresponding to fault types, piIs s isiA corresponding fault prior probability; t ═ T1,t2,…,tj,…,tMIs the set corresponding to the test point, tjRepresenting the jth test point, and M representing the total number of the test points; c ═ C1,c2,…,cj,…,cMIs a set of test costs corresponding to the test points, cjIs tjCorresponding test cost; d ═ DijIs a dependency information matrix of dimension N x M, dij1 denotes siQuilt tjMeasure, otherwise dij＝0；
(2) And constructing a fault decision network G ═ G of the system to be testedikIn which g isikRepresentation for isolating siAnd skK ≠ i, k ═ 1,2 …, N;
optimal measuring point gikThe calculation formula of (c) is:
gik＝argmin{cj|dijΔdkj＝1}
wherein Δ represents an exclusive or operation;
(3) generating the optimal test point by a heuristic search algorithm
(3.1) setting a fault set S ═ S of the initial search node, a usable test set T ═ T, and an opportunity cost value C ═ Cmax，CmaxIs the set maximum value of the test cost; setting an optimal measuring point topt；
(3.2) calculating a heuristic function evaluation value based on a fault decision network;
Figure FDA0003586358580000011
wherein the content of the first and second substances,
Figure FDA0003586358580000012
represents the measured point tjAt fault siThe value of the lower heuristic function evaluated,
Figure FDA0003586358580000013
and
Figure FDA0003586358580000014
respectively two separate sub-fault sets
Figure FDA0003586358580000015
And
Figure FDA0003586358580000016
the evaluation function value of (1);
(3.3) finding out a measuring point t to be searchedo；
Figure FDA0003586358580000017
(3.4) if the point t is measuredoIf the test optimal solution has been found or the corresponding heuristic function value is greater than the opportunity cost value C, the test point t is measuredoMarked as optimal measurement point toptAnd jumping to the step (4), otherwise, directly entering the step (3.5);
(3.5) according to the measuring point toIncluding dependency information, separating fault subsets
Figure FDA0003586358580000021
And
Figure FDA0003586358580000022
Figure FDA0003586358580000023
Figure FDA0003586358580000024
(3.6) if
Figure FDA0003586358580000025
Containing only a single fault, indicating that the fault has been isolated, and recording
Figure FDA0003586358580000026
Number of isolation faults F01 and a test Cost00; otherwise, it will
Figure FDA0003586358580000027
As a new fault set, remove T from the measure point ToThen as a new measuring point set, calculating
Figure FDA0003586358580000028
At the opportunity cost of
Figure FDA0003586358580000029
And returning to the step (3.2) until the step is found
Figure FDA00035863585800000210
Number of isolation faults F0And a test Cost0；
(3.7) if
Figure FDA00035863585800000211
Containing only a single fault, which has been isolated, recorded
Figure FDA00035863585800000212
Number of isolation faults F1＝1，Cost10; otherwise, it will
Figure FDA00035863585800000213
As a new fault set, remove T from the measure point ToThen as a new measuring point set, calculating
Figure FDA00035863585800000214
At the opportunity cost of
Figure FDA00035863585800000215
And returning to the step (3.2) until the step is found
Figure FDA00035863585800000216
Number of isolation faults F1And a test Cost1；
(3.8) updating the measuring point toA corresponding heuristic function value;
Figure FDA00035863585800000217
(3.9) calculating the current optimal fault isolation number;
Figure FDA00035863585800000218
(3.10) update opportunity cost value C of
Figure FDA00035863585800000219
(3.11) updating the measuring point to be searched
Figure FDA00035863585800000220
And measure the point
Figure FDA00035863585800000221
Marked as optimal measurement point toptAnd entering the step (4);
Figure FDA00035863585800000222
(4) generating a test strategy
(4.1) initializing a fault set of the root node as S' ═ S;
(4.2) finding out the optimal measuring point t corresponding to SoptAnd separating out the fault subset;
Figure FDA00035863585800000223
Figure FDA00035863585800000224
wherein the content of the first and second substances,
Figure FDA0003586358580000031
denotes siQuilt toptMeasure otherwise
Figure FDA0003586358580000032
(4.3) if S'0If only a single fault is contained, the fault is isolated, and the algorithm is ended; otherwise, will S'0And returning to the step (4.2) as a new fault set S 'until S'0Only contains a single fault, and the calculation is finished;
(4.4) if S'1If only a single fault is contained, the fault is isolated, and the algorithm is ended; otherwise, will S'1As a new fault set S', returning to the step (4.2) until S1' only a single fault is contained, and the algorithm ends;
wherein the evaluation function value
Figure FDA0003586358580000033
And
Figure FDA0003586358580000034
the calculation method comprises the following steps:
Figure FDA0003586358580000035
wherein L is0+L1＝N，
Figure FDA0003586358580000036
Represents the isolation siAnd skAnd (4) measuring point cost corresponding to the optimal measuring point.
CN201910661767.1A 2019-07-22 2019-07-22 Test strategy rapid generation method based on fault decision network Active CN110457776B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910661767.1A CN110457776B (en) 2019-07-22 2019-07-22 Test strategy rapid generation method based on fault decision network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910661767.1A CN110457776B (en) 2019-07-22 2019-07-22 Test strategy rapid generation method based on fault decision network

Publications (2)

Publication Number Publication Date
CN110457776A CN110457776A (en) 2019-11-15
CN110457776B true CN110457776B (en) 2022-06-14

Family

ID=68481669

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910661767.1A Active CN110457776B (en) 2019-07-22 2019-07-22 Test strategy rapid generation method based on fault decision network

Country Status (1)

Country Link
CN (1) CN110457776B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111274540A (en) * 2020-02-24 2020-06-12 电子科技大学 Fault diagnosis tree generation method based on information entropy and dynamic programming
CN113221496B (en) * 2021-05-06 2022-06-14 电子科技大学 Fault diagnosis method based on three-dimensional testability analysis model
CN114528948B (en) * 2022-03-10 2023-07-11 电子科技大学 Sequential test sequence generation method of complex system

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101126929A (en) * 2007-09-05 2008-02-20 东北大学 Continuous miner remote real-time failure forecast and diagnosis method and device

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8745440B1 (en) * 2010-09-21 2014-06-03 F5 Networks, Inc. Computer-implemented system and method for providing software fault tolerance
US8577663B2 (en) * 2011-05-23 2013-11-05 GM Global Technology Operations LLC System and methods for fault-isolation and fault-mitigation based on network modeling
CN102520342B (en) * 2011-12-07 2013-11-06 南京航空航天大学 Analog circuit test node selecting method based on dynamic feedback neural network modeling
US9866161B1 (en) * 2014-05-21 2018-01-09 Williams RDM, Inc. Universal monitor and fault detector in fielded generators and method
CN105699882B (en) * 2016-01-22 2018-05-29 电子科技大学 Analog-circuit fault diagnosis method based on vibration test technology
CN105629156B (en) * 2016-03-10 2018-04-13 电子科技大学 Analog circuit fault based on genetic planning tests optimal sequential search method
CN106095608B (en) * 2016-06-17 2019-03-15 电子科技大学 Sequential test dynamic adjusting method based on AO* algorithm
FR3059432B1 (en) * 2016-11-25 2020-02-28 Commissariat A L'energie Atomique Et Aux Energies Alternatives METHOD AND DEVICE FOR ANALYZING DEFECTS IN A NETWORK OF TRANSMISSION LINES
CN109581190B (en) * 2018-12-05 2020-08-11 电子科技大学 Optimal diagnosis tree generation method for circuit fault diagnosis
CN109581194B (en) * 2018-12-18 2020-08-11 电子科技大学 Dynamic generation method for electronic system fault test strategy

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101126929A (en) * 2007-09-05 2008-02-20 东北大学 Continuous miner remote real-time failure forecast and diagnosis method and device

Also Published As

Publication number Publication date
CN110457776A (en) 2019-11-15

Similar Documents

Publication Publication Date Title
CN110457776B (en) Test strategy rapid generation method based on fault decision network
CN109581190B (en) Optimal diagnosis tree generation method for circuit fault diagnosis
CN111999657B (en) Method for evaluating driving mileage of lithium ion battery of electric vehicle in residual life
CN111007399B (en) Lithium battery state of charge prediction method based on improved generation countermeasure network
CN109060398B (en) Multi-source information equipment fault diagnosis method
CN109617888B (en) Abnormal flow detection method and system based on neural network
CN110348615B (en) Cable line fault probability prediction method based on ant colony optimization support vector machine
CN110568360B (en) Lithium battery aging diagnosis method based on fuzzy logic algorithm
CN111274540A (en) Fault diagnosis tree generation method based on information entropy and dynamic programming
CN112132102B (en) Intelligent fault diagnosis method combining deep neural network with artificial bee colony optimization
CN115051864B (en) PCA-MF-WNN-based network security situation element extraction method and system
CN105228185A (en) A kind of method for Fuzzy Redundancy node identities in identification communication network
CN109581194B (en) Dynamic generation method for electronic system fault test strategy
CN113821452B (en) Intelligent test method for dynamically generating test case according to test performance of tested system
CN115791174B (en) Rolling bearing abnormality diagnosis method, system, electronic equipment and storage medium
CN112036079B (en) Diesel engine multi-information fusion diagnosis method based on ANFIS
CN114818859A (en) Method and device for diagnosing condition of heat distribution pipe network, terminal equipment and storage medium
CN115454473A (en) Data processing method based on deep learning vulnerability decision and information security system
CN113887452A (en) Fault diagnosis optimization method based on correlation matrix
CN112801367A (en) Fault prediction method based on ARMret model considering rare variables
CN112685957A (en) Method for predicting relay protection defects
CN113159640A (en) Method, device, equipment and medium for determining evaluation index weight
CN113312722A (en) Reliability prediction optimization method for key components of urban rail train
CN113657604A (en) Device and method for operating an inspection table
CN111460456B (en) Attack method for realizing clear decision tree and fuzzy decision tree based on sensitivity

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