Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory
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...
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doaj-7fb5fdfcd5a44e359591107ccaab68fb2020-11-25T01:14:20ZengMDPI AGSensors1424-82202019-05-01199209710.3390/s19092097s19092097Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer TheoryYanxue Wang0Fang Liu1Aihua Zhu2Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaBeijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaBeijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaBearing 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.https://www.mdpi.com/1424-8220/19/9/2097rolling element bearinghybrid classifier ensembleDempster-Shafer evidence theoryfuzzy preference relations |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yanxue Wang Fang Liu Aihua Zhu |
spellingShingle |
Yanxue Wang Fang Liu Aihua Zhu Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory Sensors rolling element bearing hybrid classifier ensemble Dempster-Shafer evidence theory fuzzy preference relations |
author_facet |
Yanxue Wang Fang Liu Aihua Zhu |
author_sort |
Yanxue Wang |
title |
Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory |
title_short |
Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory |
title_full |
Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory |
title_fullStr |
Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory |
title_full_unstemmed |
Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory |
title_sort |
bearing fault diagnosis based on a hybrid classifier ensemble approach and the improved dempster-shafer theory |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-05-01 |
description |
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. |
topic |
rolling element bearing hybrid classifier ensemble Dempster-Shafer evidence theory fuzzy preference relations |
url |
https://www.mdpi.com/1424-8220/19/9/2097 |
work_keys_str_mv |
AT yanxuewang bearingfaultdiagnosisbasedonahybridclassifierensembleapproachandtheimproveddempstershafertheory AT fangliu bearingfaultdiagnosisbasedonahybridclassifierensembleapproachandtheimproveddempstershafertheory AT aihuazhu bearingfaultdiagnosisbasedonahybridclassifierensembleapproachandtheimproveddempstershafertheory |
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