FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON ICEEMD-FastICA
In response to the difficulty in extracting early fault feature signals of rolling bearings, a joint fault diagnosis method based on Improved Complete Ensemble Empirical Mode Decomposition (ICEEMD) and Independent Component Analysis(ICA) was proposed. This method utilized the kurtosis criterion to r...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-04-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.02.004 |
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| _version_ | 1841534116786339840 |
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| author | MA WeiPing HONG KunYue AN Ning SONG YuZhou |
| author_facet | MA WeiPing HONG KunYue AN Ning SONG YuZhou |
| author_sort | MA WeiPing |
| collection | DOAJ |
| description | In response to the difficulty in extracting early fault feature signals of rolling bearings, a joint fault diagnosis method based on Improved Complete Ensemble Empirical Mode Decomposition (ICEEMD) and Independent Component Analysis(ICA) was proposed. This method utilized the kurtosis criterion to reconstruct the Intrinsic Mode Function (IMF) obtained from ICEEMD and combined it with Fast Independent Component Analysis (FastICA) for noise reduction and unmixing, significantly reducing the noise in the measured signals. The maximum energy amplitude was obtained at the fault feature frequency, making it easy to identify fault features. Through experimental research and analysis, it is shown that this method can significantly reduce noise interference and highlight fault frequency components. Compared with the method combining ICEEMD and envelope spectrum, the signal-to-noise ratio is inereased by 29.54%, which can more accurately identify fault features and meet the discrimination requirements for rolling bearing faults, thus providing a new approach for bearing fault feature extraction. |
| format | Article |
| id | doaj-art-c318f5d345c0420cb62d86f6d91d1ecb |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-04-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-c318f5d345c0420cb62d86f6d91d1ecb2025-01-15T02:45:42ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-04-014628128563938661FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON ICEEMD-FastICAMA WeiPingHONG KunYueAN NingSONG YuZhouIn response to the difficulty in extracting early fault feature signals of rolling bearings, a joint fault diagnosis method based on Improved Complete Ensemble Empirical Mode Decomposition (ICEEMD) and Independent Component Analysis(ICA) was proposed. This method utilized the kurtosis criterion to reconstruct the Intrinsic Mode Function (IMF) obtained from ICEEMD and combined it with Fast Independent Component Analysis (FastICA) for noise reduction and unmixing, significantly reducing the noise in the measured signals. The maximum energy amplitude was obtained at the fault feature frequency, making it easy to identify fault features. Through experimental research and analysis, it is shown that this method can significantly reduce noise interference and highlight fault frequency components. Compared with the method combining ICEEMD and envelope spectrum, the signal-to-noise ratio is inereased by 29.54%, which can more accurately identify fault features and meet the discrimination requirements for rolling bearing faults, thus providing a new approach for bearing fault feature extraction.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.02.004Improved complete ensemble empirical mode decompositionBlind source separationIndependent component analysisFault diagnosisNoise reduction |
| spellingShingle | MA WeiPing HONG KunYue AN Ning SONG YuZhou FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON ICEEMD-FastICA Jixie qiangdu Improved complete ensemble empirical mode decomposition Blind source separation Independent component analysis Fault diagnosis Noise reduction |
| title | FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON ICEEMD-FastICA |
| title_full | FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON ICEEMD-FastICA |
| title_fullStr | FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON ICEEMD-FastICA |
| title_full_unstemmed | FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON ICEEMD-FastICA |
| title_short | FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON ICEEMD-FastICA |
| title_sort | fault diagnosis method of rolling bearing based on iceemd fastica |
| topic | Improved complete ensemble empirical mode decomposition Blind source separation Independent component analysis Fault diagnosis Noise reduction |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.02.004 |
| work_keys_str_mv | AT maweiping faultdiagnosismethodofrollingbearingbasedoniceemdfastica AT hongkunyue faultdiagnosismethodofrollingbearingbasedoniceemdfastica AT anning faultdiagnosismethodofrollingbearingbasedoniceemdfastica AT songyuzhou faultdiagnosismethodofrollingbearingbasedoniceemdfastica |