Fault Feature Extraction of Gearbox Rolling Bearing based on VMD and Fast-kurtogram
The rolling bearing fault signal of the gearbox is difficult to extract effectively due to noise interference. A fault feature extraction method of gearbox rolling bearing based on VMD and fast-kurtogram is proposed. Firstly, the vibration signal of bearing is decomposed into several Intrinsic Mode...
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Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2020-01-01
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Series: | Jixie chuandong |
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Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2020.01.024 |
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author | Xupeng Die Jianshe Kang Kuo Chi |
author_facet | Xupeng Die Jianshe Kang Kuo Chi |
author_sort | Xupeng Die |
collection | DOAJ |
description | The rolling bearing fault signal of the gearbox is difficult to extract effectively due to noise interference. A fault feature extraction method of gearbox rolling bearing based on VMD and fast-kurtogram is proposed. Firstly, the vibration signal of bearing is decomposed into several Intrinsic Mode Function (IMF) components by VMD, and the component signal with the most prominent fault information is selected by the correlated kurtosis, and then the bandpass filtering is adaptively determined by the fast-kurtogram. Finally, the filtered signal is subjected to squared envelope spectrum analysis to extract fault information. The effectiveness and feasibility of the proposed method are demonstrated by public bearing fault data analysis and gearbox bearing fault experiments. |
format | Article |
id | doaj-art-6cb64b0a255846dbbb46d855714270fc |
institution | Kabale University |
issn | 1004-2539 |
language | zho |
publishDate | 2020-01-01 |
publisher | Editorial Office of Journal of Mechanical Transmission |
record_format | Article |
series | Jixie chuandong |
spelling | doaj-art-6cb64b0a255846dbbb46d855714270fc2025-01-10T14:56:05ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392020-01-014414314929795633Fault Feature Extraction of Gearbox Rolling Bearing based on VMD and Fast-kurtogramXupeng DieJianshe KangKuo ChiThe rolling bearing fault signal of the gearbox is difficult to extract effectively due to noise interference. A fault feature extraction method of gearbox rolling bearing based on VMD and fast-kurtogram is proposed. Firstly, the vibration signal of bearing is decomposed into several Intrinsic Mode Function (IMF) components by VMD, and the component signal with the most prominent fault information is selected by the correlated kurtosis, and then the bandpass filtering is adaptively determined by the fast-kurtogram. Finally, the filtered signal is subjected to squared envelope spectrum analysis to extract fault information. The effectiveness and feasibility of the proposed method are demonstrated by public bearing fault data analysis and gearbox bearing fault experiments.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2020.01.024VMD |
spellingShingle | Xupeng Die Jianshe Kang Kuo Chi Fault Feature Extraction of Gearbox Rolling Bearing based on VMD and Fast-kurtogram Jixie chuandong VMD |
title | Fault Feature Extraction of Gearbox Rolling Bearing based on VMD and Fast-kurtogram |
title_full | Fault Feature Extraction of Gearbox Rolling Bearing based on VMD and Fast-kurtogram |
title_fullStr | Fault Feature Extraction of Gearbox Rolling Bearing based on VMD and Fast-kurtogram |
title_full_unstemmed | Fault Feature Extraction of Gearbox Rolling Bearing based on VMD and Fast-kurtogram |
title_short | Fault Feature Extraction of Gearbox Rolling Bearing based on VMD and Fast-kurtogram |
title_sort | fault feature extraction of gearbox rolling bearing based on vmd and fast kurtogram |
topic | VMD |
url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2020.01.024 |
work_keys_str_mv | AT xupengdie faultfeatureextractionofgearboxrollingbearingbasedonvmdandfastkurtogram AT jianshekang faultfeatureextractionofgearboxrollingbearingbasedonvmdandfastkurtogram AT kuochi faultfeatureextractionofgearboxrollingbearingbasedonvmdandfastkurtogram |