Gear fault diagnosis method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion

• Published in: Advances in mechanical engineering Vol. 10; no. 11; p. 168781401881103
• Main Authors: Pan, Lizheng, Zhu, Dashuai, She, Shigang, Song, Aiguo, Shi, Xianchuan, Duan, Suolin
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.11.2018; Sage Publications Ltd; SAGE Publishing
• Subjects: Accuracy; Fault diagnosis; Feature extraction; Independent component analysis; Kernel functions; Model accuracy; Product design; Support vector machines; Wavelet analysis; fault diagnosis; independent component analysis; support vector machine; wavelet packet; Gear
• ISSN: 1687-8132; 1687-8140; 1687-8140
• DOI: 10.1177/1687814018811036

Aiming at the problem of gear fault diagnosis, in order to effectively extract the features and improve the accuracy of gear fault diagnosis, the method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion is proposed in this research. The proposed wavelet-packet independent component analysis feature extraction method can effectively combine the advantages of wavelet packet and independent component analysis methods and acquire more comprehensive feature information. Besides, the proposed kernel-function-fusion support vector machine can well integrate the advantage characteristics of each kernel function. The energy features of wavelet packet coefficients are acquired with four-layer wavelet packet decomposition and then the extracted energy features are further optimized by the independent component analysis method. The kernel-function-fusion support vector machine method is adopted to realize the gear fault diagnosis. Two kernel function models with the best self-classification accuracy are employed to serve the gear fault diagnosis corporately. The test samples are primarily classified by the main kernel function model, and then some samples are selected to be reclassified with the other kernel function model. Finally, the two kernel function models cooperate to determine the type of test samples. The comparison investigations demonstrate that the proposed method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion achieves very high diagnosis accuracy.