Next Article in Journal
Subspace Coding for Networks with Different Level Messages
Previous Article in Journal
Dynamical Systems Induced on Networks Constructed from Time Series
Article Menu

Export Article

Open AccessArticle
Entropy 2015, 17(9), 6447-6461; doi:10.3390/e17096447

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

1,2
,
1,2
and
1,*
1
School of Instrument Science and Engineering, Southeast University, No. 2, Sipailou, Nanjing 210096, China
2
Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology, Ministry of Education, No. 2, Sipailou, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Academic Editor: J. A. Tenreiro Machado
Received: 21 July 2015 / Revised: 9 September 2015 / Accepted: 16 September 2015 / Published: 21 September 2015
(This article belongs to the Section Complexity)
View Full-Text   |   Download PDF [1122 KB, uploaded 21 September 2015]   |  

Abstract

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. View Full-Text
Keywords: wavelet packet decomposition; multi-scale permutation entropy; rolling bearings; fault diagnosis; hidden Markov model wavelet packet decomposition; multi-scale permutation entropy; rolling bearings; fault diagnosis; hidden Markov model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhao, L.-Y.; Wang, L.; Yan, R.-Q. Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy. Entropy 2015, 17, 6447-6461.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top