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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Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2019-01-01
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Series: | Jixie qiangdu |
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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. |
format | Article |
id | doaj-art-7cb88876684e439c9f407a86776c2e58 |
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 |