RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK
The working environment of rolling bearing is complex, the nonlinear vibration signal and the interference of environmental noise lead to the difficulty of fault diagnosis. Therefore, based on the experimental data of bearing damage and the fractal theory, the Intrinsic Time scale Decomposition（ITD）...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2022-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.05.01 |
| _version_ | 1797767615769214976 |
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| author | XIAO JunQing YUE MinNan LI Chun JIN JiangTao XU ZiFei MIAO WeiPao |
| author_facet | XIAO JunQing YUE MinNan LI Chun JIN JiangTao XU ZiFei MIAO WeiPao |
| author_sort | XIAO JunQing |
| collection | DOAJ |
| description | The working environment of rolling bearing is complex, the nonlinear vibration signal and the interference of environmental noise lead to the difficulty of fault diagnosis. Therefore, based on the experimental data of bearing damage and the fractal theory, the Intrinsic Time scale Decomposition（ITD） was used to extract the nonlinear features of vibration signals, and the effective fault feature components were selected. The intelligent fault diagnosis of bearings was realized through Convolutional Neural Network（CNN）. The results show that compared with the existing methods, ITD-CNN has higher accuracy under different SNR. At-4 dB signal to noise ratio, the accuracy is still 2.57%～13.35% higher than the existing methods, which indicates that the proposed method has good recognition ability and generalization performance. |
| first_indexed | 2024-03-12T20:42:22Z |
| format | Article |
| id | doaj.art-a12a17a38fb34054baca5d613b70cc0d |
| institution | Directory Open Access Journal |
| issn | 1001-9669 |
| language | zho |
| last_indexed | 2024-03-12T20:42:22Z |
| publishDate | 2022-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj.art-a12a17a38fb34054baca5d613b70cc0d2023-08-01T07:54:37ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692022-01-01441017102331951407RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORKXIAO JunQingYUE MinNanLI ChunJIN JiangTaoXU ZiFeiMIAO WeiPaoThe working environment of rolling bearing is complex, the nonlinear vibration signal and the interference of environmental noise lead to the difficulty of fault diagnosis. Therefore, based on the experimental data of bearing damage and the fractal theory, the Intrinsic Time scale Decomposition（ITD） was used to extract the nonlinear features of vibration signals, and the effective fault feature components were selected. The intelligent fault diagnosis of bearings was realized through Convolutional Neural Network（CNN）. The results show that compared with the existing methods, ITD-CNN has higher accuracy under different SNR. At-4 dB signal to noise ratio, the accuracy is still 2.57%～13.35% higher than the existing methods, which indicates that the proposed method has good recognition ability and generalization performance.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.05.01Bearing;Intrinsic time scale decomposition;Convolutional neural network;Box dimension;Fault diagnosis |
| spellingShingle | XIAO JunQing YUE MinNan LI Chun JIN JiangTao XU ZiFei MIAO WeiPao RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK Jixie qiangdu Bearing;Intrinsic time scale decomposition;Convolutional neural network;Box dimension;Fault diagnosis |
| title | RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK |
| title_full | RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK |
| title_fullStr | RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK |
| title_full_unstemmed | RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK |
| title_short | RESEARCH ABOUT FAULT DIAGNOSIS OF BEARING BASED ON INSTRINSIC TIME SCALE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK |
| title_sort | research about fault diagnosis of bearing based on instrinsic time scale decomposition and convolutional neural network |
| topic | Bearing;Intrinsic time scale decomposition;Convolutional neural network;Box dimension;Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.05.01 |
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