CN111898705A - Fault feature parameter selection method based on fuzzy preference relation and adaptive hierarchical clustering - Google Patents

Fault feature parameter selection method based on fuzzy preference relation and adaptive hierarchical clustering Download PDF

Info

Publication number
CN111898705A
CN111898705A CN202010833932.XA CN202010833932A CN111898705A CN 111898705 A CN111898705 A CN 111898705A CN 202010833932 A CN202010833932 A CN 202010833932A CN 111898705 A CN111898705 A CN 111898705A
Authority
CN
China
Prior art keywords
features
hierarchical clustering
clustering
fuzzy
sensitivity
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010833932.XA
Other languages
Chinese (zh)
Other versions
CN111898705B (en
Inventor
郝慧娟
程广河
唐勇伟
郝凤琦
李娟�
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.)
Shandong Computer Science Center National Super Computing Center in Jinan
Original Assignee
Shandong Computer Science Center National Super Computing Center in Jinan
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 Shandong Computer Science Center National Super Computing Center in Jinan filed Critical Shandong Computer Science Center National Super Computing Center in Jinan
Priority to CN202010833932.XA priority Critical patent/CN111898705B/en
Publication of CN111898705A publication Critical patent/CN111898705A/en
Application granted granted Critical
Publication of CN111898705B publication Critical patent/CN111898705B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/231—Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24137—Distances to cluster centroïds

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Algebra (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Computing Systems (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Devices For Executing Special Programs (AREA)

Abstract

The invention discloses a fault characteristic parameter selection method based on fuzzy preference relation and adaptive hierarchical clustering. The invention provides self-adaptive hierarchical clustering based on fuzzy relation based on logsig function, and is applied to fault diagnosis of equipment; sensitive features are calculated and selected based on the fuzzy relation without prior knowledge, so that the intelligence of the method is improved; the use of the optimized features simplifies the feature set, avoids dimension disasters, reduces the calculation burden and improves the fault diagnosis efficiency; the adaptive hierarchical clustering preferred in combination with features has higher diagnostic accuracy.

Description

Fault feature parameter selection method based on fuzzy preference relation and adaptive hierarchical clustering
Technical Field
The invention relates to a fault characteristic parameter selection method based on fuzzy preference relation and adaptive hierarchical clustering, and belongs to the technical field of big data processing.
Background
With the development of science and technology, large-scale equipment is more and more complicated, the cooperation between the part is inseparabler, and the trouble of part all can bring the loss of shutting down, causes great economic loss, can endanger personal safety in the serious case. In addition, if the fault cannot be accurately positioned, blind repair and disassembly can cause precision errors, reliability reduction and the like. Therefore, the fault diagnosis technology is a precondition for ensuring the safe and stable operation of the equipment, and is also important for the maintenance of the equipment.
Due to the fact that the number of measuring points is large, the number of monitoring parameters (force, temperature, vibration, sound, energy, hydraulic pressure and the like) is large, diverse and complex state monitoring big data are formed, and fault diagnosis of equipment enters a big data era. The high-dimensional features can provide richer feature parameters for fault diagnosis, but the feature dimension is too high, and when the scale of the training sample is not large, the influences of overfitting and the like are brought to fault diagnosis and identification, so that the accuracy of fault diagnosis is influenced.
In neural networks, it is common to use
Figure BDA0002638995870000011
The function characterizes the fuzzy relationship between samples, and thus the ordered structure between samples. In different kinds of faults, the larger the difference between the same characteristics is, the more sensitive the characteristics are to the classification of the categories is, and the larger sensitivity coefficient is taken.
The hierarchical clustering algorithm belongs to an unsupervised classification algorithm, is suitable for clustering of data sets with any shapes, does not need to determine parameters such as a clustering center, the number of clusters and the like in advance, but has no uniform standard of end conditions, still needs to set corresponding thresholds, and has larger calculated amount.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a fault characteristic parameter selection method based on fuzzy preference relation and adaptive hierarchical clustering.
The invention provides the following technical scheme:
1) fuzzy preference relationship calculation
1.1) given System S ═<X，Q，U>Wherein X ═ { X ═ X1，x2，…，xNDenotes a sample set, Q ═ Q1，q2，…，qJIs the set of features, U ═ U1，u2，…，uCIs the failure set;
Figure BDA0002638995870000025
xKe.g. X, for qlThe fuzzy preference relation of the epsilon Q is as follows:
Figure BDA0002638995870000021
wherein q isi1，qj1E is Q; i is not equal to j; k is the number of clusters;
1.2) to dijFurther simplification is realized, as shown in a formula (2);
Figure BDA0002638995870000022
wherein Δ q ═ qi1-qj1；
2) Coefficient of sensitivity calculation
Assume a set of raw features q containing class C failuresm，j，m＝1，2，…，N；j＝1，2，…，J}CWhere N is the number of samples per fault, J is the number of features, qn，jRepresents the jth characteristic value of the nth sample;
the total number M of samples of the system S is nxc, and the total number L of data is nxc × J;
calculating the sensitivity coefficient of each characteristic according to the formula 2) to form a fuzzy relation matrix
Figure BDA0002638995870000023
The coefficient of sensitivity for each feature is:
Figure BDA0002638995870000024
3) sensitive feature selection
Sensitivity coefficient (SP) of all features1，SP2，…，SPJ) The front v sensitivity coefficients are selected as sensitivity characteristics Q ' ═ Q ' in sequence from small to large '1，q′2，…，q′vV is the preset number of sensitive features;
the problem of feature redundancy is not considered in the sensitive feature selection, and redundant features may still be included; in order to further improve the efficiency and reduce the feature dimension, the invention uses the self-adaptive hierarchical clustering algorithm to remove redundant features.
4) Removing redundant features based on adaptive hierarchical clustering;
for a certain degree of clustering of the data set, the contour coefficient SkThe definition is as follows:
Figure BDA0002638995870000031
wherein S isIThe contour coefficient of the sample individual is shown, T is the number of samples in the data set, and k is the clustering number;
Figure BDA0002638995870000032
wherein a (I) represents a sample xIAnd the average distance between all other samples belonging to class C, b (I) denotes sample xIAnd the minimum of the average distances of all samples in each class other than class C;
Figure BDA0002638995870000033
Figure BDA0002638995870000034
normalizing the selected sensitive features Q' to obtain a normalized feature set, clustering according to a self-adaptive hierarchical clustering method, and clustering to obtain class numbersc is the preferred number of features, the center of the c class is taken as the preferred feature, and a preferred feature set is formed
Figure BDA0002638995870000035
The invention has the beneficial effects that:
1. the method removes redundant features by using a self-adaptive hierarchical clustering algorithm, adopts a clustering contour coefficient as an index for evaluating the clustering effectiveness, does not need to preset the clustering number, self-adaptively determines the clustering number, and obtains a certain clustering result, so that the inter-class distance is as large as possible, the intra-class distance is as small as possible, and good separability is realized among classes;
2. the invention provides self-adaptive hierarchical clustering based on fuzzy relation based on logsig function, and is applied to fault diagnosis of equipment; sensitive features are calculated and selected based on the fuzzy relation without prior knowledge, so that the intelligence of the method is improved; the use of the optimized features simplifies the feature set, avoids dimension disasters, reduces the calculation burden and improves the fault diagnosis efficiency; the adaptive hierarchical clustering preferred in combination with features has higher diagnostic accuracy.
3. The monitoring data often has the characteristics of ambiguity, uncertainty and the like, the fuzzy preference relationship has inherent advantages, the preference of a decision maker can be better reflected, and the system is more comprehensively described; aiming at the problem of fault diagnosis in a big data form, the method has inherent advantages by combining the fuzzy preference relationship, reduces the feature dimension, removes redundant features, selects the feature combination with the largest fault diagnosis information amount from high-dimensional features, and improves the efficiency of fault diagnosis.
Drawings
FIG. 1 is a graph of the logsig function over the interval [ -1, 1 ];
FIG. 2 is a fuzzy preference relationship based on the features of equation (1);
FIG. 3 is a flow chart of the adaptive hierarchical clustering algorithm of the present invention;
fig. 4 is a schematic diagram of the sensitive feature selection described in example 2.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1 and 3.
A fault characteristic parameter selection method based on fuzzy preference relation and adaptive hierarchical clustering comprises the following steps:
1) fuzzy preference relationship calculation
1.1) given System S ═<X，Q，U>Wherein X ═ { X ═ X1，x2，…，xNDenotes a sample set, Q ═ Q1，q2，…，qJIs the set of features, U ═ U1，u2，…，uCIs the failure set;
Figure BDA0002638995870000041
xKe.g. X, for qlThe fuzzy preference relation of the epsilon Q is as follows:
Figure BDA0002638995870000042
wherein q isi1，qj1E is Q; i is not equal to j; k is the number of clusters;
as can be seen from FIG. 2, dij(q)＝dji(q) when i ═ j, dij(q) 0.5, with increasing | Δ q |, dij(q) increases from 0.5 when q is increasedi，l＞＞qj，lWhen d is greater thanij(q) → 1. Therefore, in feature selection, it is only necessary to characterize the difference between two features, and it is not necessary to describe q in detaili，lWhether greater or less than qj，l。
1.2) to dijFurther simplified as shown in(2) Shown;
Figure BDA0002638995870000051
wherein Δ q ═ qi1-qj1；
As can be seen from FIG. 2, the parameter k takes different values, dijThe change is large, and the preference degree of the fuzzy relation of the features also changes.
2) Coefficient of sensitivity calculation
Assume a set of raw features q containing class C failuresm，j，m＝1，2，…，N；j＝1，2，…，J}CWhere N is the number of samples per fault, J is the number of features, qn，jRepresents the jth characteristic value of the nth sample;
the total number M of samples of the system S is nxc, and the total number L of data is nxc × J;
calculating the sensitivity coefficient of each characteristic according to the formula 2) to form a fuzzy relation matrix
Figure BDA0002638995870000052
The coefficient of sensitivity for each feature is:
Figure BDA0002638995870000053
3) sensitive feature selection
Sensitivity coefficient (SP) of all features1，SP2，…，SPJ) The front v sensitivity coefficients are selected as sensitivity characteristics Q ' ═ Q ' in sequence from small to large '1，q′2，…，q′vV is the preset number of sensitive features;
the problem of feature redundancy is not considered in the sensitive feature selection, and redundant features may still be included; in order to further improve the efficiency and reduce the feature dimension, the invention uses the self-adaptive hierarchical clustering algorithm to remove redundant features.
4) Removing redundant features based on adaptive hierarchical clustering;
for a certain degree of clustering of the data set, the contour coefficient SkThe definition is as follows:
Figure BDA0002638995870000061
wherein S isIThe contour coefficient of the sample individual is shown, T is the number of samples in the data set, and k is the clustering number;
Figure BDA0002638995870000062
wherein a (I) represents a sample xIAnd the average distance between all other samples belonging to class C, b (I) denotes sample xIAnd the minimum of the average distances of all samples in each class other than class C;
Figure BDA0002638995870000063
Figure BDA0002638995870000064
normalizing the selected sensitive features Q' to obtain a normalized feature set, clustering according to an adaptive hierarchical clustering method, wherein the number c of the clusters is the preferred number of the features, and the center of the c classes is taken as the preferred feature to form the preferred feature set
Figure BDA0002638995870000065
Example 2
As shown in fig. 4.
The method of embodiment 1 is used for fault diagnosis and fault type determination of the bearing-integrated simulation system, and comprises the following steps:
the vibration sensor was used to acquire 4 states of the bearing simulation system: normal state, outer ring fault, inner ring fault, rolling element fault;
serial number Operating state Status flag
1 Is normal 0
2 Outer ring failure 1
3 Inner ring failure 2
4 Failure of rolling body 3
A1) Feature extraction
Extracting time domain characteristics, frequency domain characteristics, EEMD decomposed IMF component characteristics and wavelet packet decomposed energy of the original vibration signal, and forming a characteristic set;
a1.1) time-domain features
Mean value:
Figure BDA0002638995870000071
standard deviation:
Figure BDA0002638995870000072
root mean square:
Figure BDA0002638995870000073
peak-to-peak value: fp＝max|x(n)|；
The waveform index is as follows:
Figure BDA0002638995870000074
pulse factor:
Figure BDA0002638995870000075
margin indexes are as follows:
Figure BDA0002638995870000076
crest factor: fcf＝Fp/Frms；
Kurtosis:
Figure BDA0002638995870000077
skewness:
Figure BDA0002638995870000078
wherein x (N) is a time domain sequence of the signal, and N is the number of vibration sample points;
1.2) frequency domain characteristics
Frequency domain mean value:
Figure BDA0002638995870000079
center frequency:
Figure BDA00026389958700000710
frequency root mean square:
Figure BDA00026389958700000711
standard deviation of frequency:
Figure BDA00026389958700000712
(wherein fkIs the frequency value of the K-th line, s (K) is the frequency spectrum of signal x (n), K is the number of lines;
a1.3) energy index
Figure BDA00026389958700000713
Figure BDA00026389958700000714
The i-th sub-band of the layer l of the wavelet packet decomposition is represented.
A2) Sensitive feature selection
The large number of features not only can reduce the calculation efficiency, but also can cause dimension disaster, the sensitive coefficients of 134 features are calculated according to the fuzzy preference relationship-based method, and 41 sensitive features are selected;
A3) preferred feature selection
And (4) performing normalization processing on the selected 41 sensitive features, and clustering by adopting an adaptive hierarchical clustering algorithm. In this example, the finally determined c is 12.
A4) Fault diagnosis
And introducing the 12 optimal characteristics into an adaptive hierarchical clustering algorithm, and identifying the vibration signals which are actually acquired according to the trained fault model to obtain a clustering category, thereby realizing fault diagnosis and determining the fault type. In this embodiment, the classification accuracy reaches 99.4%.
Finally, it should be noted that the above-mentioned contents are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, and that the simple modifications or equivalent substitutions of the technical solutions of the present invention by those of ordinary skill in the art can be made without departing from the spirit and scope of the technical solutions of the present invention.

Claims (1)

1. A fault characteristic parameter selection method based on fuzzy preference relation and adaptive hierarchical clustering is characterized by comprising the following steps:
1) fuzzy preference relationship calculation
1.1) given System S ═<X，Q，U>Wherein X ═ { X ═ X1，x2，...，xNDenotes a sample set, Q ═ Q1，q2，...,qJIs the set of features, U ═ U1,u2,...,uCIs the failure set;
Figure FDA0002638995860000015
with respect to qlThe fuzzy preference relation of the epsilon Q is as follows:
Figure FDA0002638995860000011
wherein q isil，qjlE is Q; i is not equal to j; k is the number of clusters;
1.2) to dijFurther simplification is realized, as shown in a formula (2);
Figure FDA0002638995860000012
wherein Δ q ═ qil-qjl；
2) Coefficient of sensitivity calculation
Assume a set of raw features q containing class C failuresm，j，m＝1，2，...，N；j＝1，2，...，J}CWhere N is the number of samples per fault, J is the number of features, qn，jRepresents the jth characteristic value of the nth sample;
the total number M of samples of the system S is nxc, and the total number L of data is nxc × J;
calculating the sensitivity coefficient of each characteristic according to the formula 2) to form a fuzzy relation matrix
Figure FDA0002638995860000013
The coefficient of sensitivity for each feature is:
Figure FDA0002638995860000014
3) sensitive feature selection
Sensitivity coefficient (SP) of all features1，SP2，...，SPI) The front v sensitivity coefficients are selected as sensitivity characteristics Q ' ═ Q ' in sequence from small to large '1,q'2,...,q'vV is the preset number of sensitive features;
4) removing redundant features based on adaptive hierarchical clustering;
for a certain degree of clustering of the data set, the contour coefficient SkThe definition is as follows:
Figure FDA0002638995860000021
wherein S isIThe contour coefficient of the sample individual is shown, T is the number of samples in the data set, and k is the clustering number;
Figure FDA0002638995860000022
wherein a (I) represents a sample xIAnd the average distance between all other samples belonging to class C, b (I) denotes sample xIAnd the minimum of the average distances of all samples in each class other than class C;
Figure FDA0002638995860000023
Figure FDA0002638995860000024
normalizing the selected sensitive features Q' to obtain a normalized feature set, clustering according to an adaptive hierarchical clustering method, wherein the number c of the clusters is the preferred number of the features, and the center of the c classes is taken as the preferred feature to form the preferred feature set
Figure FDA0002638995860000025
CN202010833932.XA 2020-08-18 2020-08-18 Fault feature parameter selection method based on fuzzy preference relation and self-adaptive hierarchical clustering Active CN111898705B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010833932.XA CN111898705B (en) 2020-08-18 2020-08-18 Fault feature parameter selection method based on fuzzy preference relation and self-adaptive hierarchical clustering

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010833932.XA CN111898705B (en) 2020-08-18 2020-08-18 Fault feature parameter selection method based on fuzzy preference relation and self-adaptive hierarchical clustering

Publications (2)

Publication Number Publication Date
CN111898705A true CN111898705A (en) 2020-11-06
CN111898705B CN111898705B (en) 2023-04-25

Family

ID=73229929

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010833932.XA Active CN111898705B (en) 2020-08-18 2020-08-18 Fault feature parameter selection method based on fuzzy preference relation and self-adaptive hierarchical clustering

Country Status (1)

Country Link
CN (1) CN111898705B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105975995A (en) * 2016-05-26 2016-09-28 山东省计算中心（国家超级计算济南中心） Fuzzy-preference-relation-based multi-vibration-signal fusion method
US20170061072A1 (en) * 2015-09-02 2017-03-02 Guardant Health, Inc. Machine Learning for Somatic Single Nucleotide Variant Detection in Cell-free Tumor Nucleic acid Sequencing Applications
CN106769052A (en) * 2017-03-21 2017-05-31 桂林电子科技大学 A kind of mechanical system rolling bearing intelligent failure diagnosis method based on cluster analysis
CN108170823A (en) * 2018-01-04 2018-06-15 江西师范大学 A kind of Freehandhand-drawing interactive three-dimensional model retrieval method understood based on high-level semantic attribute
CN109143867A (en) * 2018-09-26 2019-01-04 上海海事大学 A kind of energy management method of the hybrid power ship based on ANN Control
CN110991478A (en) * 2019-10-29 2020-04-10 西安建筑科技大学 Method for establishing thermal comfort model and method and system for setting user preference temperature

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170061072A1 (en) * 2015-09-02 2017-03-02 Guardant Health, Inc. Machine Learning for Somatic Single Nucleotide Variant Detection in Cell-free Tumor Nucleic acid Sequencing Applications
CN105975995A (en) * 2016-05-26 2016-09-28 山东省计算中心（国家超级计算济南中心） Fuzzy-preference-relation-based multi-vibration-signal fusion method
CN106769052A (en) * 2017-03-21 2017-05-31 桂林电子科技大学 A kind of mechanical system rolling bearing intelligent failure diagnosis method based on cluster analysis
CN108170823A (en) * 2018-01-04 2018-06-15 江西师范大学 A kind of Freehandhand-drawing interactive three-dimensional model retrieval method understood based on high-level semantic attribute
CN109143867A (en) * 2018-09-26 2019-01-04 上海海事大学 A kind of energy management method of the hybrid power ship based on ANN Control
CN110991478A (en) * 2019-10-29 2020-04-10 西安建筑科技大学 Method for establishing thermal comfort model and method and system for setting user preference temperature

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
文政颖等: "一种基于模糊层次聚类分析的大数据挖掘算法" *
董炜;刘明明;王良顺;赵辉;辜勋;: "基于群决策的道岔控制电路故障诊断方法" *

Also Published As

Publication number Publication date
CN111898705B (en) 2023-04-25

Similar Documents

Publication Publication Date Title
CN110132598B (en) Fault noise diagnosis algorithm for rolling bearing of rotating equipment
CN111666982B (en) Electromechanical equipment fault diagnosis method based on deep neural network
CN106769052A (en) A kind of mechanical system rolling bearing intelligent failure diagnosis method based on cluster analysis
CN109974782B (en) Equipment fault early warning method and system based on big data sensitive characteristic optimization selection
WO2023044978A1 (en) Adversarial-flow-model-based unsupervised fault diagnosis method for mechanical device
CN113255848A (en) Water turbine cavitation sound signal identification method based on big data learning
CN110297469B (en) Production line fault judgment method based on resampling integrated feature selection algorithm
CN110991471B (en) Fault diagnosis method for high-speed train traction system
CN111504635A (en) Planetary gear fault diagnosis method based on differential evolution probability neural network
CN109298633A (en) Chemical production process fault monitoring method based on adaptive piecemeal Non-negative Matrix Factorization
CN114297918A (en) Aero-engine residual life prediction method based on full-attention depth network and dynamic ensemble learning
CN109255201A (en) A kind of ball screw assembly, health evaluating method based on SOM-MQE
CN114871850B (en) Tool wear state assessment method based on vibration signals and BP neural network
Jiao et al. Partly interpretable transformer through binary arborescent filter for intelligent bearing fault diagnosis
CN115290326A (en) Rolling bearing fault intelligent diagnosis method
CN115375026A (en) Method for predicting service life of aircraft engine in multiple fault modes
CN115392393A (en) Temperature measuring instrument state detection method
CN114861349A (en) Rolling bearing RUL prediction method based on model migration and wiener process
CN114443338A (en) Sparse negative sample-oriented anomaly detection method, model construction method and device
CN114742115A (en) Rolling bearing fault diagnosis model and method based on temperature and vibration characteristic fusion
CN114970309A (en) Thermal power equipment state early warning evaluation method
CN111983365B (en) Transformer winding deformation detection method based on oscillatory wave multistage decomposition
Cheng et al. Control chart pattern recognition using wavelet analysis and neural networks
CN111898705A (en) Fault feature parameter selection method based on fuzzy preference relation and adaptive hierarchical clustering
Zhou et al. Degradation State Recognition of Rolling Bearing Based on K‐Means and CNN Algorithm

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