FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM
A large number of complex features need to be extracted for the fault diagnosis of wind turbine rolling bearings. A parallel bearing fault diagnosis model based on attention mechanism, ResNext network and long short-term memory (LSTM) network was proposed. Firstly, the collected one-dimensional vibr...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2023-12-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.06.001 |
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| _version_ | 1841534195891961856 |
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| author | DU HaoFei ZHANG Chao LI JianJun |
| author_facet | DU HaoFei ZHANG Chao LI JianJun |
| author_sort | DU HaoFei |
| collection | DOAJ |
| description | A large number of complex features need to be extracted for the fault diagnosis of wind turbine rolling bearings. A parallel bearing fault diagnosis model based on attention mechanism, ResNext network and long short-term memory (LSTM) network was proposed. Firstly, the collected one-dimensional vibration signal was preprocessed; then it was input into the model in two ways to extract features, and one of them was input into the ResNext module embedded in the attention mechanism. The attention mechanism can increase the weight of important features and reduce model operations. The other channel was input to the LSTM network to extract the dependence of the vibration signal on the time series. Finally, the two extracted features are fused and input to the Softmax layer for fault classification. The experimental results show that, compared with the current bearing fault diagnosis method based on deep learning, the proposed method performs better in bearing fault classification accuracy. |
| format | Article |
| id | doaj-art-b180ce80216b4357a6278bdb76b523b8 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-b180ce80216b4357a6278bdb76b523b82025-01-15T02:45:00ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692023-12-01451271127955273089FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTMDU HaoFeiZHANG ChaoLI JianJunA large number of complex features need to be extracted for the fault diagnosis of wind turbine rolling bearings. A parallel bearing fault diagnosis model based on attention mechanism, ResNext network and long short-term memory (LSTM) network was proposed. Firstly, the collected one-dimensional vibration signal was preprocessed; then it was input into the model in two ways to extract features, and one of them was input into the ResNext module embedded in the attention mechanism. The attention mechanism can increase the weight of important features and reduce model operations. The other channel was input to the LSTM network to extract the dependence of the vibration signal on the time series. Finally, the two extracted features are fused and input to the Softmax layer for fault classification. The experimental results show that, compared with the current bearing fault diagnosis method based on deep learning, the proposed method performs better in bearing fault classification accuracy.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.06.001Fault diagnosisAttention mechanismResNextLSTM |
| spellingShingle | DU HaoFei ZHANG Chao LI JianJun FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM Jixie qiangdu Fault diagnosis Attention mechanism ResNext LSTM |
| title | FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM |
| title_full | FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM |
| title_fullStr | FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM |
| title_full_unstemmed | FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM |
| title_short | FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM |
| title_sort | fault diagnosis of wind turbine bearing based on senet resnext lstm |
| topic | Fault diagnosis Attention mechanism ResNext LSTM |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.06.001 |
| work_keys_str_mv | AT duhaofei faultdiagnosisofwindturbinebearingbasedonsenetresnextlstm AT zhangchao faultdiagnosisofwindturbinebearingbasedonsenetresnextlstm AT lijianjun faultdiagnosisofwindturbinebearingbasedonsenetresnextlstm |