Rolling bearing fault diagnosis based on ITD Lempel-Ziv complexity and PSO-SVM

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (24) : 102-107.

Zhang Xiao-long, Zhang Qing, Qin Xian-rong, Sun Yuan-tao

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Abstract

A method for rolling bearing fault diagnosis based on intrinsic time scale decomposition (ITD), Lempel-Ziv complexity and support vector machine (SVM) based on particle swarm optimization (PSO) algorithm was proposed. The rolling bearing vibration signal was decomposed to several proper rotation (PR) components with ITD method. The distribution of Lempel-Ziv complexity of PR components under different fault conditions was distinguishing. The Lempel-Ziv complexity of PR components was calculated to construct the feature vector for each sample. The feature vector acted as the input of SVM to accomplish the classification of different fault types. And the PSO algorithm was employed to search for the best SVM parameters to achieve higher percentage of classification accuracy. The experimental research results indicate that the proposed method has the advantage of high computation efficiency and good prediction without the influence of variation in load.

Key words

intrinsic time scale decomposition (ITD) / Lempel-Ziv complexity / support vector machine (SVM) / particle swarm optimization (PSO) / rolling bearing / fault diagnosis

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Zhang Xiao-long, Zhang Qing, Qin Xian-rong, Sun Yuan-tao . Rolling bearing fault diagnosis based on ITD Lempel-Ziv complexity and PSO-SVM[J]. Journal of Vibration and Shock, 2016, 35(24): 102-107

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