A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

Author:

Liang Mingliang1,Su Dongmin1,Hu Daidi2,Ge Mingtao2ORCID

Affiliation:

1. Zhengzhou Railway Vocational & Technical College, Zhengzhou 451460, China

2. College of Electronics and Information Engineering, SIAS International University, Xinzheng 451150, China

Abstract

A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.

Funder

Henan High-Speed Railway Operation and Maintenance Engineering Research Center

Publisher

Hindawi Limited

Subject

Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Condensed Matter Physics,Civil and Structural Engineering

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