Next Article in Journal
A Kullback–Leibler View of Maximum Entropy and Maximum Log-Probability Methods
Previous Article in Journal
Specific and Complete Local Integration of Patterns in Bayesian Networks
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Entropy 2017, 19(5), 231; doi:10.3390/e19050231

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on EWT and Ambiguity Correlation Classifiers

1
Department of Electronic Engineering, Zhengzhou Railway Vocational & Technical College, No. 9 Qiancheng Road, Zhengdong New District, Zhengzhou 451460, Henan, China
2
College of Electronics and Information Engineering, SIAS International University, No. 168 Renmin Road, Xinzheng 451150, Henan, China
*
Author to whom correspondence should be addressed.
Academic Editors: Raúl Alcaraz Martínez and Kevin Knuth
Received: 20 March 2017 / Revised: 6 May 2017 / Accepted: 15 May 2017 / Published: 18 May 2017
(This article belongs to the Section Information Theory)
View Full-Text   |   Download PDF [3529 KB, uploaded 18 May 2017]   |  

Abstract

According to non-stationary characteristic of the acoustic emission signal of rolling element bearings, a novel fault diagnosis method based on empirical wavelet transform (EWT) and ambiguity correlation classification (ACC) is proposed. In the proposed method, the acoustic emission signal acquired from a one-channel sensor is firstly decomposed using the EWT method, and then the mutual information of decomposed components and the original signal is computed and used to extract the noiseless component in order to obtain the reconstructed signal. Afterwards, the ambiguity correlation classifier, which has the advantages of ambiguity functions in the processing of the non-stationary signal, and the combining of correlation coefficients, is applied. Finally, multiple datasets of reconstructed signals for different operative conditions are fed to the ambiguity correlation classifier for training and testing. The proposed method was verified by experiments, and experimental results have shown that the proposed method can effectively diagnose three different operative conditions of rolling element bearings with higher detection rates than support vector machine and back-propagation (BP) neural network algorithms. View Full-Text
Keywords: ambiguity correlation classifier; empirical wavelet transform; faults diagnosis; rolling element bearings ambiguity correlation classifier; empirical wavelet transform; faults diagnosis; rolling element bearings
Figures

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

Jiang, X.; Wu, L.; Ge, M. A Novel Faults Diagnosis Method for Rolling Element Bearings Based on EWT and Ambiguity Correlation Classifiers. Entropy 2017, 19, 231.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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