Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy

Entropy

Li-Ye Zhao¹  Lei Wang¹  Ru-Qiang Yan¹ 

[1] School of Instrument Science and Engineering, Southeast University, No. 2, Sipailou, Nanjing 210096, China; E-Mails:

关键词: wavelet packet decomposition;    multi-scale permutation entropy;    rolling bearings;    fault diagnosis;    hidden Markov model;   

DOI  :  10.3390/e17096447

来源: mdpi

【 摘 要 】

This paper presents a rolling bearing fault diagnosis approach by integrating wavelet packet decomposition (WPD) with multi-scale permutation entropy (MPE). The approach uses MPE values of the sub-frequency band signals to identify faults appearing in rolling bearings. Specifically, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of sub-frequency band signals by means of the WPD method. Then, each sub-frequency band signal is divided into a series of subsequences, and MPEs of all subsequences in corresponding sub-frequency band signal are calculated. After that, the average MPE value of all subsequences about each sub-frequency band is calculated, and is considered as the fault feature of the corresponding sub-frequency band. Subsequently, MPE values of all sub-frequency bands are considered as input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of the rolling bearing. Experimental study on a data set from the Case Western Reserve University bearing data center has shown that the presented approach can accurately identify faults in rolling bearings.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.