Next Article in Journal
An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
Previous Article in Journal
Monitoring of Epoxy-Grouted Bonding Strength Development between an Anchored Steel Bar and Concrete Using PZT-Enabled Active Sensing
Article Menu
Issue 9 (May-1) cover image

Export Article

Open AccessArticle

Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory

1,*, 1,2 and 1
1
Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2
School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2097; https://doi.org/10.3390/s19092097
Received: 19 January 2019 / Revised: 25 April 2019 / Accepted: 30 April 2019 / Published: 6 May 2019
(This article belongs to the Section Intelligent Sensors)
  |  
PDF [3591 KB, uploaded 6 May 2019]
  |     |  

Abstract

Bearing fault diagnosis of a rotating machine plays an important role in reliable operation. A novel intelligent fault diagnosis method for roller bearings has been developed based on a proposed hybrid classifier ensemble approach and the improved Dempster-Shafer theory. The improved Dempster-Shafer theory well considered the combination of unreliable evidence sources, the uncertainty information of basic probability assignment, and the relative credibility of the evidence on the weights in the process of decision making under the framework of fuzzy preference relations, which can effectively deal with conflicts of the evidences and then well improve the diagnostic accuracy for the hybrid classifier ensemble. The effectiveness of the improved Dempster-Shafer theory has been verified via a numerical example. In addition, deep neural networks, a support vector machine, and extreme learning machine techniques have been utilized in the single-stage classification based on singular spectrum entropy, power spectrum entropy, time-frequency entropy, and wavelet packet energy spectrum entropy in this work. Performances of the proposed hybrid ensemble classifier has been demonstrated on a bearing test-rig, compared with the original Dempster-Shafer theory. It can be found that the overall error rate can be greatly reduced with the hybrid ensemble classifier and the improved Dempster-Shafer theory. View Full-Text
Keywords: rolling element bearing; hybrid classifier ensemble; Dempster-Shafer evidence theory; fuzzy preference relations rolling element bearing; hybrid classifier ensemble; Dempster-Shafer evidence theory; fuzzy preference relations
Figures

Figure 1

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

Share & Cite This Article

MDPI and ACS Style

Wang, Y.; Liu, F.; Zhu, A. Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory. Sensors 2019, 19, 2097.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top