Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

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Fault Diagnosis for Closed Loop Nonlinear System Using Generalized Frequency Response Functions and Least Square Support Vector Machine

2019 1st International Conference on Industrial Artificial Intelligence (IAI) ◽

10.1109/iciai.2019.8850734 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jialiang Zhang ◽

Jianfu Cao

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Nonlinear System ◽

Frequency Response ◽

Closed Loop ◽

Least Square ◽

Support Vector ◽

Response Functions ◽

Frequency Response Functions

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Fault Diagnosis of Nonlinear Analog Circuit Based on Generalized Frequency Response Function and LSSVM Classifier Fusion

Mathematical Problems in Engineering ◽

10.1155/2020/8274570 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Jialiang Zhang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Frequency Response ◽

Frequency Response Function ◽

Analog Circuit ◽

Least Square ◽

Classifier Fusion ◽

Support Vector ◽

Response Functions ◽

Nonlinear Analog

For fault diagnosis of nonlinear analog circuit, a novel method based on generalized frequency response function (GFRF) and least square support vector machine (LSSVM) classifier fusion is presented. The sinusoidal signal is used as the input of analog circuit, and then, the generalized frequency response functions are estimated directly by the time-domain formulations. The discrete Fourier transform of measurement data is avoided. After obtaining the generalized frequency response functions, the amplitudes of the GFRFs are chosen as the fault feature parameters. A classifier fusion algorithm based on least square support vector machine (LSSVM) is used for fault identification. Two LSSVM multifault classifiers with different kernel functions are constructed as subclassifiers. Fault diagnosis experiments of resistor-capacitance (RC) circuit and Sallen Key filter are carried out, respectively. The results show that the estimated GFRFs of the circuit are accurate, and the fault diagnosis method can get high recognition rate.

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A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Energies ◽

10.3390/en12214170 ◽

2019 ◽

Vol 12 (21) ◽

pp. 4170 ◽

Cited By ~ 5

Author(s):

Bing Zeng ◽

Jiang Guo ◽

Wenqiang Zhu ◽

Zhihuai Xiao ◽

Fang Yuan ◽

...

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Least Square ◽

Ratio Method ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Local Optimum ◽

Grey Wolf ◽

Diagnosis Model ◽

Transformer Fault

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

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Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine

Measurement ◽

10.1016/j.measurement.2020.107571 ◽

2020 ◽

Vol 156 ◽

pp. 107571 ◽

Cited By ~ 11

Author(s):

Yan Xue ◽

Dongyang Dou ◽

Jianguo Yang

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Convolution Neural Network ◽

Support Vector ◽

Deep Convolution Neural Network

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Rolling-bearings fault diagnosis based-on empirical mode decomposition and least square support vector machine

Journal of Mechanical Engineering ◽

10.3901/jme.2007.04.088 ◽

2007 ◽

Vol 43 (04) ◽

pp. 88 ◽

Cited By ~ 6

Author(s):

Taiyong WANG

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Least Square ◽

Support Vector ◽

Rolling Bearings ◽

Mode Decomposition

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Rotating Machine Fault Diagnosis Based on Optimal Morphological Filter and Local Tangent Space Alignment

Shock and Vibration ◽

10.1155/2015/893504 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 11

Author(s):

Shaojiang Dong ◽

Lili Chen ◽

Baoping Tang ◽

Xiangyang Xu ◽

Zhengyuan Gao ◽

...

Keyword(s):

Fault Diagnosis ◽

Tangent Space ◽

Learning Algorithm ◽

Support Vector ◽

Morphological Filter ◽

Rotating Machine ◽

Machine Fault Diagnosis ◽

Mode Decomposition ◽

Machine Fault ◽

Local Tangent

In order to identify the fault of rotating machine effectively, a new method based on the morphological filter optimized by particle swarm optimization algorithm (PSO) and the nonlinear manifold learning algorithm local tangent space alignment (LTSA) is proposed. Firstly, the signal is purified by the morphological filter; the filter’s structure element (SE) is selected by PSO method. Then the filtered signals are decomposed by the empirical mode decomposition (EMD) method, and the extract features are mapped into the LTSA to extract the character features; then the support vector machine (SVM) model is used to achieve the rotating machine fault diagnosis. The proposed method is evaluated by vibration signals measured from bearings with faults. Results show that the method can effectively remove the noise and extract the fault features, so the rotating machine fault diagnosis can be achieved effectively.

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Fault Classification Model of Rotor Based on Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.66-68.1982 ◽

2011 ◽

Vol 66-68 ◽

pp. 1982-1987

Author(s):

Wei Niu ◽

Guo Qing Wang ◽

Zheng Jun Zhai ◽

Juan Cheng

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Classification Model ◽

Fault Classification ◽

Support Vector ◽

Vibration Signals ◽

Good Classification ◽

Speed Change ◽

Diagnosis Model

The vibration signals of rotating machinery in operation consist of plenty of information about its running condition, and extraction and identification of fault signals in the process of speed change are necessary for the fault diagnosis of rotating machinery. This paper improves DDAG classification method and proposes a new fault diagnosis model based on support vector machine to solve the problem of restricting the rotating machinery fault intelligent diagnosis due to the lack of fault data samples. The testing results demonstrate that the model has good classification precision and can correctly diagnose faults.

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Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

Journal of Sensors ◽

10.1155/2017/8092691 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 15

Author(s):

Xiaochen Zhang ◽

Dongxiang Jiang ◽

Te Han ◽

Nanfei Wang ◽

Wenguang Yang ◽

...

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Clustering Algorithm ◽

Imbalanced Data ◽

Principal Component ◽

Rotating Machinery ◽

Support Vector ◽

Data Set ◽

Diagnosis Model ◽

Sample Set

To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

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