APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS
Aiming at the problems of traditional bearings fault diagnosis methods had such shortcomings as largely dependent on expert prior knowledge and difficulty in fault feature extraction,a method based on compressive sensing（CS） and improved deep wavelet neural network（DWNN） was proposed. Firstly,the co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2020-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.04.003 |
| _version_ | 1797767682879127552 |
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| author | DU XiaoLei CHEN ZhiGang ZHANG Nan XU Xu |
| author_facet | DU XiaoLei CHEN ZhiGang ZHANG Nan XU Xu |
| author_sort | DU XiaoLei |
| collection | DOAJ |
| description | Aiming at the problems of traditional bearings fault diagnosis methods had such shortcomings as largely dependent on expert prior knowledge and difficulty in fault feature extraction,a method based on compressive sensing（CS） and improved deep wavelet neural network（DWNN） was proposed. Firstly,the collected vibration data of bearings were de-noised and compressed by CS. Secondly,the improved wavelet auto-encoder was designed to construct the DWNN,and the " crosslayer" connection was introduced to alleviate the gradient disappearance of the network. Finally,unsupervised pre-training of DWNN was performed using a large amount of unlabeled compressed data and supervised and fine-tuned with a small amount of tagged data to realize fault discrimination. The experimental results show that the method can effectively identify the bearings with multiple fault types and multiple fault severities,which is less affected by prior knowledge and subjective knowledge and avoids complex artificial feature extraction process. The feature extraction ability and recognition ability of proposed method are superior than artificial neural network,deep belief network,deep sparse auto-encoder and so on. |
| first_indexed | 2024-03-12T20:43:20Z |
| format | Article |
| id | doaj.art-a678688623b1471184d0c800f851a0a9 |
| institution | Directory Open Access Journal |
| issn | 1001-9669 |
| language | zho |
| last_indexed | 2024-03-12T20:43:20Z |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj.art-a678688623b1471184d0c800f851a0a92023-08-01T07:51:49ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692020-01-014277778530608599APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSISDU XiaoLeiCHEN ZhiGangZHANG NanXU XuAiming at the problems of traditional bearings fault diagnosis methods had such shortcomings as largely dependent on expert prior knowledge and difficulty in fault feature extraction,a method based on compressive sensing（CS） and improved deep wavelet neural network（DWNN） was proposed. Firstly,the collected vibration data of bearings were de-noised and compressed by CS. Secondly,the improved wavelet auto-encoder was designed to construct the DWNN,and the " crosslayer" connection was introduced to alleviate the gradient disappearance of the network. Finally,unsupervised pre-training of DWNN was performed using a large amount of unlabeled compressed data and supervised and fine-tuned with a small amount of tagged data to realize fault discrimination. The experimental results show that the method can effectively identify the bearings with multiple fault types and multiple fault severities,which is less affected by prior knowledge and subjective knowledge and avoids complex artificial feature extraction process. The feature extraction ability and recognition ability of proposed method are superior than artificial neural network,deep belief network,deep sparse auto-encoder and so on.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.04.003Rolling bearing;Fault diagnosis;Compressive sensing;Deep wavelet neural network |
| spellingShingle | DU XiaoLei CHEN ZhiGang ZHANG Nan XU Xu APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS Jixie qiangdu Rolling bearing;Fault diagnosis;Compressive sensing;Deep wavelet neural network |
| title | APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS |
| title_full | APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS |
| title_fullStr | APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS |
| title_full_unstemmed | APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS |
| title_short | APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS |
| title_sort | application of compressive sensing and improved deep wavelet neural network in bearing fault diagnosis |
| topic | Rolling bearing;Fault diagnosis;Compressive sensing;Deep wavelet neural network |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.04.003 |
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