CN108398939A - A kind of method for diagnosing faults based on DS evidence theories - Google Patents
A kind of method for diagnosing faults based on DS evidence theories Download PDFInfo
- Publication number
- CN108398939A CN108398939A CN201810169966.6A CN201810169966A CN108398939A CN 108398939 A CN108398939 A CN 108398939A CN 201810169966 A CN201810169966 A CN 201810169966A CN 108398939 A CN108398939 A CN 108398939A
- Authority
- CN
- China
- Prior art keywords
- feature
- failure
- fuzzy number
- probability
- triangular fuzzy
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Test And Diagnosis Of Digital Computers (AREA)
Abstract
The present invention is based on evidence theories, provide a kind of method of fault diagnosis, are related to fault diagnosis field.The present invention establishes triangle fuzzy model to each failure, triangle fuzzy model is established to testing data, Basic probability assignment function is generated according to overlapping degree between model to be measured and fault model, the Basic probability assignment function generated under each failure is merged with evidence theory rule of combination and realizes fault diagnosis.The present invention is combined realization fault diagnosis using evidence theory with Triangular Fuzzy Number, has the advantages that calculate simple；Basic probability assignment function generation method proposed by the present invention, realizes the processing to fuzzy message well；The fault diagnosis of rotor may be implemented in method for diagnosing faults proposed by the present invention.
Description
Technical field
The present invention relates to fault diagnosis fields, are a kind of methods for realizing fault diagnosis based on DS evidence theories.
Background technology
Fault diagnosis technology is the engineering science of a produce reality of combining closely, and is the product of modern production development.
With application of the modern science and technology in equipment, the structure of equipment becomes increasingly complex, and function is also more and more perfect, automates journey
It spends higher and higher, since many unavoidable factors influence, equipment can be caused various failures occur, to reduce or lose pre-
Fixed function, or even serious or even catastrophic accident can be caused.
Fault diagnosis technology is exactly to grasp the operation shape of equipment in equipment operation or in the case of not detaching equipment substantially
Condition carries out analyzing processing according to the useful information acquired to diagnosed object test, judge diagnosed object state whether
In abnormality or malfunction.
Information fusion technology is that collaboration utilizes multi-source information, to obtain more objective to things or target, more essential understanding
Informix treatment technology is one of the key technology of intelligence science research.In many Fusion Models and method, D-S cards
It is maximally efficient one of algorithm according to theoretical algorithm.Evidence theory widens the space of elementary events in probability theory for elementary event
Power set, also known as framework of identification establishes Basic probability assignment function (Basic Probability on framework of identification
Assignment, BPA).In addition, evidence theory additionally provides a Dempster rule of combination, which can be in no elder generation
The fusion of evidence is realized in the case of testing information.Particularly, when BPA is only allocated in the list collection proposition of framework of identification
When, BPA is converted to the probability in probability theory, and the fusion results of rule of combination are identical as the Bayes formula in probability theory.From
From the point of view of this angle, DS evidence theories more effectively can indicate and handle uncertain information than probability theory, these features make it
It is widely used in information fusion field.Just because of DS evidence theories with excellent in terms of uncertain knowledge expression
Performance, so its theoretical and application development was very fast in recent years, the theory is in multi-sensor information fusion, medical diagnosis, military affairs
Important function has been played in terms of commander, target identification.
Evidence theory has many advantages, such as, is applied preferably handle in fault diagnosis and appears in device sensor
Uncertain information in signal.
Invention content
In order to realize fault diagnosis, the present invention is based on DS evidence theories, provide a kind of method of fault diagnosis.Use the party
The fault diagnosis that method is realized can be in preferable processing equipment sensor signal uncertain information, accurately to rotor therefore
Barrier makes diagnosis.
The technical solution adopted by the present invention to solve the technical problems includes the following steps：
Step 1：Input n kind failures (are denoted as F1,F2,...,Fn) k kind features fault sample data Dij(i=1,2 ...,
N, j=1,2 ..., k), the fault signature can be used to do the feature of failure modes, and the fault sample data are failures
The measured value of feature, to each failure, each feature establishes Triangular Fuzzy Number model, and framework of identification is Θ={ F1,F2,...,
Fn, the Triangular Fuzzy Number is a fuzzy set on given domain U, refers to any x ∈ U, all there are one number μ (x) ∈
[0,1] is corresponding to it, and μ (x) is known as degrees of membership of the x to U, and μ is known as the membership function of x, the Triangular Fuzzy Number model foundation
Method is：
By failure Fi(i=1,2 ..., n) feature j sample datas DijMinimum value minDij, mean value aveDijAnd maximum value
maxDijRespectively as failure FiThe minimum value of feature j Triangular Fuzzy Number models, mean value, maximum value, then failure FiThe three of feature j
Angle fuzzy number is
Step 2：Input Devices to test k kind feature sample to be tested data Tj, generate Triangular Fuzzy Number model, the triangle
Fuzzy Math Model establish method be：
By feature j sample to be tested data TjMinimum value minTj, mean value aveTjAnd maximum value maxTjRespectively as to be measured
The minimum value of feature j Triangular Fuzzy Number models, mean value, maximum value, then the Triangular Fuzzy Number of feature j to be measured be
Step 3：Triangular Fuzzy Number model to be measured under feature j is generated substantially with failure Triangular Fuzzy Number Model Matching
Probability distribution function mj, the Basic probability assignment function is defined as belonging to any one the subset of Θ in evidence theory
A, m (A) ∈ [0,1], and meetThen m is 2ΘOn Basic probability assignment function, wherein 2ΘFor identification
The power set of frame,It is described substantially general
Rate partition function mjGeneration method is：
It willWithIntersecting area withArea under the curve is (i.e.With x-axis composition closed area area) ratio
As corresponding single subset elements { FiReliability, willWith the intersecting areas of the more a failure fuzzy numbers of feature j withArea under the curve
Reliability of the ratio as corresponding more subset elements, single subset elements { FiRefer in step 1 and if only if subset
A includes i-th (i ∈ [1, N]) a element in framework of identification Θ, and more subset elements refer to that subset A includes in step 1
More than two elements in framework of identification Θ, remember that the sum of reliability of above-mentioned generation is Sum, if Sum >=1, by the letter of above-mentioned generation
Degree normalization, the method for normalizing are：If Sum<1, then by mj{F1,F2,…,FnUpdate
For mj{F1,F2,…,Fn}+1-Sum；
Step 4：K BPA evidence theories rule of combination of generation is merged successively and obtains mF, the evidence theory
Rule of combination beWhereinM1, m2 are two groups of BPA to be fused,
M be m1 merged with m2 after BPA, the conflict factor of K m1, m2,
Step 5：Step 4 is merged using Pignistic probability transformation methods
MF is converted to probability distribution P, and the conversion method is：Wherein
Step 6：Diagnosis is made to equipment fault according to obtained probability distribution P, if P ({ Fi) in maximum probability be more than
0.5, then take P ({ Fi) in the corresponding classification of maximum probability as equipment fault diagnosis result (otherwise it is assumed that can not pay a home visit
It is disconnected).
The beneficial effects of the present invention are the present invention to be combined realization fault diagnosis using evidence theory with Triangular Fuzzy Number,
Have the advantages that calculate simple；Present invention Triangular Fuzzy Number models fault signature, solves the problem of representation of fuzzy message；
Basic probability assignment function generation method proposed by the present invention, realizes the processing to fuzzy message well；The present invention proposes
Method for diagnosing faults, the fault diagnosis of rotor may be implemented.
Description of the drawings
The general flow chart that Fig. 1 present invention realizes.
Fig. 2 is failure F11 sample data D of feature11。
Fig. 3 is 1 testing data T of feature1。
Fig. 4 is 1 triangle fuzzy model to be measured of feature and failure triangle fuzzy model.
Fig. 5 is the intersecting area of two fuzzy numbers.
Fig. 6 is the intersecting area of three fuzzy numbers.
Specific implementation mode
The present invention is further described with example below in conjunction with the accompanying drawings.The example of rotor fault diagnosis is provided herein,
Experimental data comes from [1].[1] in (F is used herein provided with three kinds of failures altogether1,F2,F3Indicate), there are four types of characteristics for each failure
According to respectively comprising five groups of data, every group of 40 observations.To the characteristic of each feature of each failure, four groups are chosen as instruction
Practice sample and generates failure Triangular Fuzzy Number model.Choose failure F3The remaining one group of data of four features (are not chosen as training sample
This data) it is used as test sample, illustrate the implementation steps of proposed method for diagnosing faults.
Step 1：Three kinds of failures of input (are denoted as F1,F2,F3) four kinds of features fault sample data Dij(i=1,2 ..., 3,
J=1,2 ..., 4), the fault signature can be used to do the feature of failure modes, and the fault sample data are failure spies
The measured value of sign, to each failure, each feature establishes Triangular Fuzzy Number model, and framework of identification is Θ={ F1,F2,F3, it is described
Triangular Fuzzy Number is a fuzzy set on given domain U, refers to any x ∈ U, and all there are one number μ (x) ∈ [0,1] therewith
Corresponding, μ (x) is known as degrees of membership of the x to U, and μ is known as the membership function of x, and the method for the Triangular Fuzzy Number model foundation is：
We are with failure F11 Triangular Fuzzy Number of feature illustrates the method for failure Triangular Fuzzy Number model foundation for establishing,
Failure F11 characteristic of feature such as Fig. 2.By failure F11 sample data D of feature11Minimum value 0.1518, mean value 0.1614 and most
Big value 0.1820 is respectively as failure FiThe minimum value of feature j Triangular Fuzzy Number models, mean value, maximum value, then failure F1Feature 1
Triangular Fuzzy Number be
Step 2：Input 4 kinds of feature sample to be tested data T of Devices to testj(j=1,2 ..., 4) generates Triangular Fuzzy Number
The method of model, the Triangular Fuzzy Number model foundation is：
We illustrate the method for Triangular Fuzzy Number model foundation to be measured by taking the foundation of 1 Triangular Fuzzy Number to be measured of feature as an example, wait for
Survey 1 characteristic of feature such as Fig. 3.By 1 sample to be tested data T of feature1Minimum value 0.3207, mean value 0.3387 and maximum value
0.3476 respectively as 1 Triangular Fuzzy Number model of feature to be measured minimum value, mean value, maximum value, then the Triangle Module of feature 1 to be measured
Pasting number is
Step 3：Triangular Fuzzy Number model to be measured under feature j is generated substantially with failure Triangular Fuzzy Number Model Matching
Probability distribution function mj, the Basic probability assignment function is defined as belonging to any one the subset of Θ in evidence theory
A, m (A) ∈ [0,1], and meetThen m is 2ΘOn Basic probability assignment function, wherein 2ΘFor identification
The power set of frame,It is described substantially general
Rate partition function mjGeneration method is：
It willWithIntersecting area withArea under the curve is (i.e.With x-axis composition closed area area) ratio
As corresponding single subset elements { FiReliability, willWith the intersecting areas of the more a failure fuzzy numbers of feature j withBelow curve
Reliability of the long-pending ratio as corresponding more subset elements, single subset elements { FiRefer in step 1 and if only if son
It includes i-th (i ∈ [1, N]) a element in framework of identification Θ to collect A, and more subset elements refer to subset A packets in step 1
More than two elements in Θ containing framework of identification, remember that the sum of reliability of above-mentioned generation is Sum, if Sum >=1, by above-mentioned generation
Reliability normalizes, and the method for normalizing is：If Sum<1, then by mj{F1,F2,…,FnMore
It is newly mj{F1,F2,…,Fn}+1-Sum；
We are with m1Illustrate BPA generation methods for generation：
(1) it calculatesArea under the curve S=(0.3476-0.3207) * 1/2=0.0135；
(2)m1({F1), m1({F2), m1({F3) generation：
It calculatesWithIntersecting areaThen
It calculatesWithIntersecting areaThen
It calculatesWithIntersecting areaThen
(3)m1({F1,F2), m1({F1,F3), m1({F2,F3), m1({F1,F2,F3) generation：
It calculatesWithIntersecting areaThen
It calculatesWithIntersecting areaThen
It calculatesWithIntersecting areaThen
It calculatesWithIntersecting areaThen
(4) the sum of reliability of above-mentioned generation Sum=0.2848, therefore update m1({F1,F2,F3)=0+1-Sum=
0.7152。
M is produced with same method2,m3,m4, as a result as follows：
m2({F1)=0.0867, m2({F2)=0.2094, m2({F3)=0.3216, m2({F1,F2)=0.0365,
m2({F1,F3)=0.0547, m2({F2,F3)=0.1694, m2({F1,F2,F3)=0.1217；
m3({F1)=0, m3({F2)=0, m3({F3)=0.4482, m3({F1,F2)=0, m3({F1,F3)=0, m3
({F2,F3)=0, m3({F1,F2,F3)=0.5518；
m4({F1)=0, m4({F2)=0, m4({F3)=0.9745, m4({F1,F2)=0, m4({F1,F3)=0, m4
({F2,F3)=0, m4({F1,F2,F3)=0.0255；
Step 4：4 BPA evidence theories rules of combination of generation are merged successively and obtain mF, the evidence theory
Rule of combination beWhereinm1,m2For two groups of BPA to be fused,
M is m1With m2BPA after fusion, K m1,m2The conflict factor,
Fusion results are：mF({F1)=0.0013, mF({F2)=0.0031, mF({F3)=0.9898, mF({F1,
F2)=0.0006, mF({F1,F3)=0.0008, mF({F2,F3)=0.0026, mF({F1,F2,F3)=0.0018；
Step 5：Step 4 is merged using Pignistic probability transformation methods
mFProbability distribution P is converted to, the conversion method is：Wherein
Step 6：Diagnosis is made to equipment fault according to obtained probability distribution P, if P ({ Fi) in maximum probability be more than
0.5, then take P ({ Fi) in the corresponding classification of maximum probability as equipment fault diagnosis result (otherwise it is assumed that can not pay a home visit
It is disconnected)；
Maximum probability is P ({ F in probability distribution P3) and P ({ F3})>0.5, therefore equipment fault is diagnosed as F3, and it is true
Fault type is consistent.
Bibliography
[1] text grows into forest, the shores Xu Xiao multi-source uncertain information blending theories and application [M] Science Presses, and 2012.
Claims (1)
1. a kind of method for diagnosing faults based on DS evidence theories, it is characterised in that include the following steps：
Step 1：Input n kind failures (are denoted as F1,F2,...,Fn) k kind features fault sample data Dij(i=1,2 ..., n, j
=1,2 ..., k), the fault signature can be used to do the feature of failure modes, and the fault sample data are fault signatures
Measured value, Triangular Fuzzy Number model is established to each failure each feature, framework of identification is Θ={ F1,F2,...,Fn, institute
It is a fuzzy set on given domain U to state Triangular Fuzzy Number, refer to any x ∈ U, all there are one number μ (x) ∈ [0,1] with
Correspondence, μ (x) is known as degrees of membership of the x to U, and μ is known as the membership function of x, and the method for the Triangular Fuzzy Number model foundation is：
By failure Fi(i=1,2 ..., n) feature j sample datas DijMinimum value minDij, mean value aveDijAnd maximum value maxDij
Respectively as failure FiThe minimum value of feature j Triangular Fuzzy Number models, mean value, maximum value, then failure FiThe triangle of feature j is fuzzy
Number is
Step 2：Input Devices to test k kind feature sample to be tested data Tj, generate Triangular Fuzzy Number model, the Triangular Fuzzy Number
The method of model foundation is：
By feature j sample to be tested data TjMinimum value minTj, mean value aveTjAnd maximum value maxTjRespectively as feature j triangles to be measured
The minimum value of Fuzzy Math Model, mean value, maximum value, then the Triangular Fuzzy Number of feature j to be measured be
Step 3：Triangular Fuzzy Number model to be measured under feature j is generated into elementary probability with failure Triangular Fuzzy Number Model Matching
Partition function mj, the Basic probability assignment function is defined as belonging to any one the subset A, m of Θ in evidence theory
(A) [0,1] ∈, and meetThen m is 2ΘOn Basic probability assignment function, wherein 2ΘTo recognize frame
The power set of frame,The elementary probability
Partition function mjGeneration method is：
It willWithIntersecting area withArea under the curve is (i.e.With x-axis composition closed area area) ratio conduct
Corresponding list subset elements { FiReliability, willWith the intersecting areas of the more a failure fuzzy numbers of feature j withThe ratio of area under the curve
It is worth the reliability as corresponding more subset elements, single subset elements { FiRefer in step 1 and if only if subset A packets
I-th (i ∈ [1, N]) a element in Θ containing framework of identification, more subset elements refer to that subset A includes identification in step 1
More than two elements in frame Θ remember that the sum of reliability of above-mentioned generation is that Sum returns the reliability of above-mentioned generation if Sum >=1
One changes, and the method for normalizing is：If Sum<1, then by mj{F1,F2,…,FnIt is updated to mj
{F1,F2,…,Fn}+1-Sum；
Step 4：K BPA evidence theories rule of combination of generation is merged successively and obtains mF, the combination of the evidence theory
Rule isWhereinm1,m2For two groups of BPA to be fused, m m1With
m2BPA after fusion, K m1,m2The conflict factor,
Step 5：The m for being merged step 4 using Pignistic probability transformation methodsFTurn
It is changed to probability distribution P, the conversion method is：Wherein
Step 6：Diagnosis is made to equipment fault according to obtained probability distribution P, if P ({ Fi) in maximum probability be more than 0.5, then
Take P ({ Fi) in the corresponding classification of maximum probability as equipment fault diagnosis result (otherwise it is assumed that diagnosis can not be made).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810169966.6A CN108398939B (en) | 2018-03-01 | 2018-03-01 | Fault diagnosis method based on DS evidence theory |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810169966.6A CN108398939B (en) | 2018-03-01 | 2018-03-01 | Fault diagnosis method based on DS evidence theory |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108398939A true CN108398939A (en) | 2018-08-14 |
| CN108398939B CN108398939B (en) | 2020-01-10 |
Family
ID=63091426
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810169966.6A Active CN108398939B (en) | 2018-03-01 | 2018-03-01 | Fault diagnosis method based on DS evidence theory |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108398939B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109165632A (en) * | 2018-09-20 | 2019-01-08 | 上海电力学院 | A kind of equipment fault diagnosis method based on improvement D-S evidence theory |
| CN110059413A (en) * | 2019-04-19 | 2019-07-26 | 中国航空无线电电子研究所 | A kind of method for diagnosing faults |
| CN110166437A (en) * | 2019-04-19 | 2019-08-23 | 杭州电子科技大学 | The method that mobile target defence optimal policy based on DS evidential reasoning is chosen |
| CN111366884A (en) * | 2018-12-26 | 2020-07-03 | 西安西电高压开关有限责任公司 | Active electronic current transformer and laser service life evaluation method and device thereof |
| CN111506994A (en) * | 2020-04-14 | 2020-08-07 | 西北工业大学 | Motor rotor fault diagnosis method based on intelligent set |
| CN111506045A (en) * | 2020-04-24 | 2020-08-07 | 西北工业大学 | Fault diagnosis method based on single-value intelligent set correlation coefficient |
| CN112949145A (en) * | 2021-03-31 | 2021-06-11 | 西南大学 | Transformer fault diagnosis method based on Duval Pentagons fault BPA function |
| CN117171710A (en) * | 2023-11-02 | 2023-12-05 | 四川乐电新能源科技有限公司 | Fault diagnosis method and fault diagnosis device for power system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007297931A (en) * | 2006-04-28 | 2007-11-15 | Nissan Motor Co Ltd | Failure diagnosis device for exhaust emission control device for internal combustion engine |
| CN102208028A (en) * | 2011-05-31 | 2011-10-05 | 北京航空航天大学 | Fault predicting and diagnosing method suitable for dynamic complex system |
| CN102721941A (en) * | 2012-06-20 | 2012-10-10 | 北京航空航天大学 | 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 |
| CN102968109A (en) * | 2012-12-03 | 2013-03-13 | 西南大学 | Safety instrument system based on D-S (Dempster/Shafer) evidence theory |
| CN103577707A (en) * | 2013-11-15 | 2014-02-12 | 上海交通大学 | Robot failure diagnosis method achieved by multi-mode fusion inference |
-
2018
- 2018-03-01 CN CN201810169966.6A patent/CN108398939B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007297931A (en) * | 2006-04-28 | 2007-11-15 | Nissan Motor Co Ltd | Failure diagnosis device for exhaust emission control device for internal combustion engine |
| CN102208028A (en) * | 2011-05-31 | 2011-10-05 | 北京航空航天大学 | Fault predicting and diagnosing method suitable for dynamic complex system |
| CN102721941A (en) * | 2012-06-20 | 2012-10-10 | 北京航空航天大学 | 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 |
| CN102968109A (en) * | 2012-12-03 | 2013-03-13 | 西南大学 | Safety instrument system based on D-S (Dempster/Shafer) evidence theory |
| CN103577707A (en) * | 2013-11-15 | 2014-02-12 | 上海交通大学 | Robot failure diagnosis method achieved by multi-mode fusion inference |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109165632A (en) * | 2018-09-20 | 2019-01-08 | 上海电力学院 | A kind of equipment fault diagnosis method based on improvement D-S evidence theory |
| CN111366884A (en) * | 2018-12-26 | 2020-07-03 | 西安西电高压开关有限责任公司 | Active electronic current transformer and laser service life evaluation method and device thereof |
| CN110059413A (en) * | 2019-04-19 | 2019-07-26 | 中国航空无线电电子研究所 | A kind of method for diagnosing faults |
| CN110166437A (en) * | 2019-04-19 | 2019-08-23 | 杭州电子科技大学 | The method that mobile target defence optimal policy based on DS evidential reasoning is chosen |
| CN110059413B (en) * | 2019-04-19 | 2022-11-15 | 中国航空无线电电子研究所 | Fault diagnosis method |
| CN111506994A (en) * | 2020-04-14 | 2020-08-07 | 西北工业大学 | Motor rotor fault diagnosis method based on intelligent set |
| CN111506045A (en) * | 2020-04-24 | 2020-08-07 | 西北工业大学 | Fault diagnosis method based on single-value intelligent set correlation coefficient |
| CN112949145A (en) * | 2021-03-31 | 2021-06-11 | 西南大学 | Transformer fault diagnosis method based on Duval Pentagons fault BPA function |
| CN117171710A (en) * | 2023-11-02 | 2023-12-05 | 四川乐电新能源科技有限公司 | Fault diagnosis method and fault diagnosis device for power system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108398939B (en) | 2020-01-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108398939A (en) | A kind of method for diagnosing faults based on DS evidence theories | |
| CN108520266B (en) | A kind of Time Domain Fusion method for diagnosing faults based on DS evidence theory | |
| CN108446458B (en) | A kind of Weighted Fusion rotor method for diagnosing faults based on DS evidence theory | |
| CN106790008B (en) | Machine learning system for detecting abnormal host in enterprise network | |
| CN108920426B (en) | A kind of method for diagnosing faults based on power equal operator and DS evidence theory | |
| CN109117353A (en) | The fusion method and device of fault diagnosis result | |
| CN104460654B (en) | A kind of gear imperfection failure diagnosis Rules extraction method based on quantization characteristic relation | |
| Liu et al. | Multi-information fusion fault diagnosis based on KNN and improved evidence theory | |
| CN112990259B (en) | Early fault diagnosis method for rotary mechanical bearing based on improved transfer learning | |
| Wang et al. | Network-combined broad learning and transfer learning: A new intelligent fault diagnosis method for rolling bearings | |
| CN116894187A (en) | Gear box fault diagnosis method based on deep migration learning | |
| DE112021003629T5 (en) | COMPACT REPRESENTATION AND TIME SERIES SEGMENT RETRIEVAL THROUGH DEEP LEARNING | |
| CN109088744A (en) | Powerline network abnormal intrusion detection method, device, equipment and storage medium | |
| CN110059413A (en) | A kind of method for diagnosing faults | |
| Hamada et al. | Bayesian binomial assurance tests for system reliability using component data | |
| Samizade et al. | Passive browser identification with multi-scale convolutional neural networks | |
| Yue et al. | Multiple Source-Free Domain Adaptation Network Based on Knowledge Distillation for Machinery Fault Diagnosis | |
| Khurshid et al. | Emergence of universal scaling in isotropic turbulence | |
| Xing et al. | Community detection for gossip dynamics with stubborn agents | |
| Yang et al. | Multigrained hybrid neural network for rotating machinery fault diagnosis using joint local and global information | |
| CN114038289A (en) | Molecular motion simulation system and method based on cryoelectron microscope | |
| Petrović et al. | Real-Time Circuit Breaker Availability Assessment in the Transmission Network | |
| Ma et al. | Ensembles of fuzzy cognitive map classifiers based on quantum computation | |
| Zhang et al. | Fault pattern recognition of rolling bearing based on smoothness prior approach and dual-input depth spatial–temporal fusion | |
| Wang et al. | Application of mutual information maximization convolutional neural network in bearing feature extraction |
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 |