Railway Rolling Bearing Fault Diagnosis Based on Muti-scale IMF Permutation Entropy and SA-SVM Classifier

YAO Dechen; YANG Jianwei; CHENG Xiaoqing; WANG Xing

doi:10.3901/JME.2018.09.168

Journal of Mechanical Engineering >

2018 , Vol. 54 >Issue 9: 168 - 176

DOI: https://doi.org/10.3901/JME.2018.09.168

Railway Rolling Bearing Fault Diagnosis Based on Muti-scale IMF Permutation Entropy and SA-SVM Classifier

YAO Dechen ,
YANG Jianwei ,
CHENG Xiaoqing ,
WANG Xing

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1. School of Machine-electricity and Automobile Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044;
2. Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering Architecture, Beijing 100044;
3. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044;
4. Department of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024

Received date: 2017-05-05

Revised date: 2018-02-01

Online published: 2018-05-05

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Abstract

The vibration signals resulting from rolling bearings are non-linear and non-stationary, an approach for the fault diagnosis of railway rolling bearings using the multi-scale IMF permutation entropy and SA-SVM classifier is proposed. The signal is first denoised using wavelet de-noising (WD) as a pre-filter, which improves the subsequent decomposition into a number of intrinsic mode functions (IMFs) using ensemble empirical mode decompose (EEMD). Secondly, the multi-scale IMF permutation entropy are extracted as feature parameters. Finally, the extracted features are given input to SA-SVM for an automated fault diagnosis procedure. The results demonstrate its effectiveness for railway rolling bearings fault diagnosis. The fault diagnosis system has high application value in practical engineering.

Key words： EEMD; multi-scale IMF permutation entropy; SA-SVM; railway rolling bearing; fault diagnosis

Cite this article

YAO Dechen , YANG Jianwei , CHENG Xiaoqing , WANG Xing . Railway Rolling Bearing Fault Diagnosis Based on Muti-scale IMF Permutation Entropy and SA-SVM Classifier[J]. Journal of Mechanical Engineering, 2018 , 54(9) : 168 -176 . DOI: 10.3901/JME.2018.09.168

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