ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION

Rolling bearing as a key component of rotating equipment, its performance seriously affect the safe operation of the equipment. As the equipment condition is complex, the impact component of the fault feature is often submerged by the noise signal, therefore the fault feature cannot be extracted eff...

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Main Authors: LIANG Kai, LIU Tao, MA PeiYuan, WU Xing
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Strength 2019-01-01
Series:Jixie qiangdu
Subjects:
Online Access:http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.03.005
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author LIANG Kai
LIU Tao
MA PeiYuan
WU Xing
author_facet LIANG Kai
LIU Tao
MA PeiYuan
WU Xing
author_sort LIANG Kai
collection DOAJ
description Rolling bearing as a key component of rotating equipment, its performance seriously affect the safe operation of the equipment. As the equipment condition is complex, the impact component of the fault feature is often submerged by the noise signal, therefore the fault feature cannot be extracted effectively. A method was proposed based on improved complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) and kurtosis index by this paper. Firstly, the improved CEEMDAN method is used to add adaptive white noise to each signal in the decomposition process, a unique residue was computed to obtain each intrinsic model function(IMF), compared with ensemble empirical mode decomposition(EEMD), the decomposition is complete. Secondly, the kurtosis index is calculated of each IMF to select the reconstructed IMF component and the kurtosis index is used to select the most suitable reconstructed signal. Finally, the bearing fault feature is obtained by envelope demodulation. The results confirm that this method has better decomposition effect, better adaptability and highlight the impact of the bearing fault.
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institution Kabale University
issn 1001-9669
language zho
publishDate 2019-01-01
publisher Editorial Office of Journal of Mechanical Strength
record_format Article
series Jixie qiangdu
spelling doaj-art-7cb88876684e439c9f407a86776c2e582025-01-15T02:29:48ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692019-01-014153253930604668ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTIONLIANG KaiLIU TaoMA PeiYuanWU XingRolling bearing as a key component of rotating equipment, its performance seriously affect the safe operation of the equipment. As the equipment condition is complex, the impact component of the fault feature is often submerged by the noise signal, therefore the fault feature cannot be extracted effectively. A method was proposed based on improved complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) and kurtosis index by this paper. Firstly, the improved CEEMDAN method is used to add adaptive white noise to each signal in the decomposition process, a unique residue was computed to obtain each intrinsic model function(IMF), compared with ensemble empirical mode decomposition(EEMD), the decomposition is complete. Secondly, the kurtosis index is calculated of each IMF to select the reconstructed IMF component and the kurtosis index is used to select the most suitable reconstructed signal. Finally, the bearing fault feature is obtained by envelope demodulation. The results confirm that this method has better decomposition effect, better adaptability and highlight the impact of the bearing fault.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.03.005CEEMDANKurtosisSignal reconstructionRolling bearingFault diagnosis
spellingShingle LIANG Kai
LIU Tao
MA PeiYuan
WU Xing
ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION
Jixie qiangdu
CEEMDAN
Kurtosis
Signal reconstruction
Rolling bearing
Fault diagnosis
title ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION
title_full ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION
title_fullStr ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION
title_full_unstemmed ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION
title_short ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION
title_sort rolling bearing fault feature extraction research based on improved ceemdan and reconstruction
topic CEEMDAN
Kurtosis
Signal reconstruction
Rolling bearing
Fault diagnosis
url http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2019.03.005
work_keys_str_mv AT liangkai rollingbearingfaultfeatureextractionresearchbasedonimprovedceemdanandreconstruction
AT liutao rollingbearingfaultfeatureextractionresearchbasedonimprovedceemdanandreconstruction
AT mapeiyuan rollingbearingfaultfeatureextractionresearchbasedonimprovedceemdanandreconstruction
AT wuxing rollingbearingfaultfeatureextractionresearchbasedonimprovedceemdanandreconstruction