WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON 1D-CNN AND SWLSTM
Aiming at the subtle fault features of the wind turbines rolling bearing, the fault signal is nonlinear, non-stationary and contains noise interference, and the fault signal has the characteristics of space and time feature information, a space-time fusion convolutional shared weight long short-term...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
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.1001.9669.2023.06.006 |
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| Summary: | Aiming at the subtle fault features of the wind turbines rolling bearing, the fault signal is nonlinear, non-stationary and contains noise interference, and the fault signal has the characteristics of space and time feature information, a space-time fusion convolutional shared weight long short-term memory network (CSwLSTM) model based on one-dimensional convolutional neural network (1D-CNN) and the shared weight long short-term memory network (SWLSTM) was proposed for wind turbine rolling bearing fault diagnosis. Using the Western Reserve University rolling bearing dataset for experiment, compared with the convolutional long short-term memory network (CLSTM) model and convolutional gated recurrent unit network (CGRU) model with the same structure, CSWLSTM model had a significant improvement in the convergence of the training dataset. The training time was reduced by 39.9% and 19.0%, respectively. The model parameters were reduced by 63. 3% and 53.4%, respectively. The accuracy was increased by 1.0% and 1.5%, the precision rate was increased by 1.0% and 1.7%, and the recall rate was increased by 0.9% and 1.0% on the test dataset, respectively. The simulation experiment results show that the CSWLSTM model has good application potential in the wind turbine rolling bearing fault diagnosis. |
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| ISSN: | 1001-9669 |