Induction Motor Fault Diagnosis Based on Fuzzy Support Vector Machine

This paper demonstrates the development of a flexible fault diagnosis methodology that can detect up to ten different faults in the induction motor (IM), simultaneously. The major IM electrical faults, such as the broken rotor bar (BRB), phase unbalance (PUF), and stator winding fault (SWF), and mechanical faults, such as bearing fault (BF), unbalanced rotor (UR), bowed rotor (BR), and misaligned rotor (MR), are considered with different fault severities for the diagnosis. The experiments are conducted with three varying loads and seven different speeds, and the frequency domain vibration and current data are acquired at a relatively low sampling rate of 1 kHz. Several statistical features are extracted and then the best feature-set is selected using the wrapper model. Thereafter, a data classification tool based on the support vector machine (SVM) is used for the fault characterization. Initially, a multi-fault diagnosis is performed by training and testing the SVM at the same operating conditions (i.e., load and speed). The performance of the classifier is found to be very good at all IM operating conditions. The main focus of this study lies in overcoming the fault diagnosis, where the data are unavailable at required operating conditions. This is accomplished by employing interpolation and extrapolation strategies for different loads and speeds. The proposed methodology not only solves practical problem of unavailability of data at different operating conditions but also shows good performance and takes low computation time, which are vital requirements of an online intelligent condition monitoring system.

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

Shock and Vibration ◽

10.1155/2013/403920 ◽

2013 ◽

Vol 20 (2) ◽

pp. 341-349 ◽

Cited By ~ 21

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.121-122.813 ◽

2010 ◽

Vol 121-122 ◽

pp. 813-818 ◽

Cited By ~ 1

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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