Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis

Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis

This paper presents an enhanced rolling bearing fault diagnosis approach, based on optimized wavelet packet transform (WPT) assisted with quantitative wavelet function selection. Mutual information is utilized as a quantitative measure to select the most suitable wavelet function for the WPT-based v...

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Main Authors: Ruqiang Yan, Mengxiao Shan, Jianwei Cui, Yahui Wu
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2015/794921
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spelling doaj-55eca905cf1a4aa5856d8be48482793a2020-11-24T23:04:31ZengHindawi LimitedShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/794921794921Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault DiagnosisRuqiang Yan0Mengxiao Shan1Jianwei Cui2Yahui Wu3School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, ChinaChangcheng Institute of Metrology and Measurement, Aviation Industry Corporation of China, Key Laboratory of Science and Technology on Metrology & Calibration, Beijing 100095, ChinaThis paper presents an enhanced rolling bearing fault diagnosis approach, based on optimized wavelet packet transform (WPT) assisted with quantitative wavelet function selection. Mutual information is utilized as a quantitative measure to select the most suitable wavelet function for the WPT-based vibration analysis. Energy features from coefficients of an optimal set of orthogonal wavelet subspaces which resulted from the WPT-based vibration analysis are input to different classifiers. The fault states of the rolling bearings can then be identified. Experiment studies conducted on a rolling bearing test system have verified the effectiveness of the proposed approach for rolling bearing fault diagnosis.http://dx.doi.org/10.1155/2015/794921
collection DOAJ
language English
format Article
sources DOAJ
author Ruqiang Yan
Mengxiao Shan
Jianwei Cui
Yahui Wu
spellingShingle Ruqiang Yan
Mengxiao Shan
Jianwei Cui
Yahui Wu
Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis
Shock and Vibration
author_facet Ruqiang Yan
Mengxiao Shan
Jianwei Cui
Yahui Wu
author_sort Ruqiang Yan
title Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis
title_short Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis
title_full Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis
title_fullStr Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis
title_full_unstemmed Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis
title_sort mutual information-assisted wavelet function selection for enhanced rolling bearing fault diagnosis
publisher Hindawi Limited
series Shock and Vibration
issn 1070-9622
1875-9203
publishDate 2015-01-01
description This paper presents an enhanced rolling bearing fault diagnosis approach, based on optimized wavelet packet transform (WPT) assisted with quantitative wavelet function selection. Mutual information is utilized as a quantitative measure to select the most suitable wavelet function for the WPT-based vibration analysis. Energy features from coefficients of an optimal set of orthogonal wavelet subspaces which resulted from the WPT-based vibration analysis are input to different classifiers. The fault states of the rolling bearings can then be identified. Experiment studies conducted on a rolling bearing test system have verified the effectiveness of the proposed approach for rolling bearing fault diagnosis.
url http://dx.doi.org/10.1155/2015/794921
work_keys_str_mv AT ruqiangyan mutualinformationassistedwaveletfunctionselectionforenhancedrollingbearingfaultdiagnosis
AT mengxiaoshan mutualinformationassistedwaveletfunctionselectionforenhancedrollingbearingfaultdiagnosis
AT jianweicui mutualinformationassistedwaveletfunctionselectionforenhancedrollingbearingfaultdiagnosis
AT yahuiwu mutualinformationassistedwaveletfunctionselectionforenhancedrollingbearingfaultdiagnosis
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