Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine

2016 ◽  
Vol 297 ◽  
pp. 128-140 ◽  
Author(s):  
Jun Hang ◽  
Jianzhong Zhang ◽  
Ming Cheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Fuzzy Support Vector Machine

scholarly journals Wavelet Correlation Feature Scale Entropy and Fuzzy Support Vector Machine Approach for Aeroengine Whole-Body Vibration Fault Diagnosis

2013 ◽  
Vol 20 (2) ◽  
pp. 341-349 ◽  
Author(s):  
Cheng-Wei Fei ◽  
Guang-Chen Bai
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Whole Body Vibration ◽  
Learning Ability ◽  
Whole Body ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Vibration Signals ◽  
Body Vibration ◽  
Feature Scale

In order to correctly analyze aeroengine whole-body vibration signals, Wavelet Correlation Feature Scale Entropy (WCFSE) and Fuzzy Support Vector Machine (FSVM) (WCFSE-FSVM) method was proposed by fusing the advantages of the WCFSE method and the FSVM method. The wavelet coefficients were known to be located in high Signal-to-Noise Ratio (S/N or SNR) scales and were obtained by the Wavelet Transform Correlation Filter Method (WTCFM). This method was applied to address the whole-body vibration signals. The WCFSE method was derived from the integration of the information entropy theory and WTCFM, and was applied to extract the WCFSE values of the vibration signals. Among the WCFSE values, theWFSE1andWCFSE2values on the scale 1 and 2 from the high band of vibration signal were believed to acceptably reflect the vibration feature and were selected to construct the eigenvectors of vibration signals as fault samples to establish the WCFSE-FSVM model. This model was applied to aeroengine whole-body vibration fault diagnosis. Through the diagnoses of four vibration fault modes and the comparison of the analysis results by four methods (SVM, FSVM, WESE-SVM, WCFSE-FSVM), it is shown that the WCFSE-FSVM method is characterized by higher learning ability, higher generalization ability and higher anti-noise ability than other methods in aeroengine whole-vibration fault analysis. Meanwhile, this present study provides a useful insight for the vibration fault diagnosis of complex machinery besides an aeroengine.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM

2010 ◽  
Vol 121-122 ◽  
pp. 813-818 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Fuzzy Support Vector Machine ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

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