An Intelligent Fault Diagnosis Method Based on Multiscale Entropy and SVMs

Authors:
Long Zhang;Guoliang Xiong;Hesheng Liu;Huijun Zou;Weizhong Guo
Affiliations:
School of Mechanical Eng., Shanghai Jiaotong University, Shanghai, China 200240;School of Mechatronic Eng., East China Jiaotong University, Nanchang, China 330013;Department of physics, Shangrao Normal College, Shangrao, China 334001;School of Mechanical Eng., Shanghai Jiaotong University, Shanghai, China 200240;School of Mechanical Eng., Shanghai Jiaotong University, Shanghai, China 200240
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

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Abstract

Sample entropy (SampEn) has been applied in many literatures as a statistical feature to describe the regularity of a time series. However, as components of mechanical system usually interact and couple with each other, SampEn may cause inaccurate or incomplete description of a mechanical vibration signal due to the fact that SampEn is calculated at only one single scale. In this paper, a new method, named multiscale entropy (MSE), taking into account multiple time scales, was introduced for feature extraction from fault vibration signal. MSE in tandem with support vector machines (SVMs) constitutes the proposed intelligent fault diagnosis method. Details on the parameter selection of SVMs were discussed. In addition, performances between SVMs and artificial neural networks (ANNs) were compared. Experiment results verified the proposed model.