Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM
It is significant for the evaluation and prediction of the performance degradation of rolling bearings. However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. Therefore, an Attention-LSTM method is propose...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6615920 |
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| _version_ | 1832558867087622144 |
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| author | Yaping Wang Chaonan Yang Di Xu Jianghua Ge Wei Cui |
| author_facet | Yaping Wang Chaonan Yang Di Xu Jianghua Ge Wei Cui |
| author_sort | Yaping Wang |
| collection | DOAJ |
| description | It is significant for the evaluation and prediction of the performance degradation of rolling bearings. However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. Therefore, an Attention-LSTM method is proposed to improve the evaluation and prediction of the performance degradation of rolling bearings. First, to reduce the uncertainty of the manual intervention, performance degradation characteristic indexes of rolling bearings are evaluated and screened by the correlation, the monotonicity, and the robustness. Second, the original characteristic indicator curve is divided into the Health Indicator (HI) curve and the residual curve by means of fixed-window averaging to quantitatively and intuitively reflect the deterioration degree of the rolling bearing performance. Finally, the Attention mechanism is combined with the LSTM model, and a scoring function is established to enhance the prediction accuracy. The scoring function is used to adjust the intermediate output state weight of the LSTM model and improve the prediction accuracy. The appropriate network structure and the parameter configuration are determined, and the prediction model of rolling bearing degradation performance is established. Compared with other models, the method proposed by this paper makes full use of the historical data and is more sensitive to the key information in the long time series, and the eRMSE index and the eMAE index of the two sets of experimental data are minimum, and the prediction accuracy of rolling bearing degradation performance is higher. The model has the strong robustness and the generalization ability, which has the important engineering practical value for the prediction of the equipment health state. |
| format | Article |
| id | doaj-art-6e8aa9aeb24c4b8190d2762fccea007a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-6e8aa9aeb24c4b8190d2762fccea007a2025-02-03T01:31:22ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66159206615920Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTMYaping Wang0Chaonan Yang1Di Xu2Jianghua Ge3Wei Cui4Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaIt is significant for the evaluation and prediction of the performance degradation of rolling bearings. However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. Therefore, an Attention-LSTM method is proposed to improve the evaluation and prediction of the performance degradation of rolling bearings. First, to reduce the uncertainty of the manual intervention, performance degradation characteristic indexes of rolling bearings are evaluated and screened by the correlation, the monotonicity, and the robustness. Second, the original characteristic indicator curve is divided into the Health Indicator (HI) curve and the residual curve by means of fixed-window averaging to quantitatively and intuitively reflect the deterioration degree of the rolling bearing performance. Finally, the Attention mechanism is combined with the LSTM model, and a scoring function is established to enhance the prediction accuracy. The scoring function is used to adjust the intermediate output state weight of the LSTM model and improve the prediction accuracy. The appropriate network structure and the parameter configuration are determined, and the prediction model of rolling bearing degradation performance is established. Compared with other models, the method proposed by this paper makes full use of the historical data and is more sensitive to the key information in the long time series, and the eRMSE index and the eMAE index of the two sets of experimental data are minimum, and the prediction accuracy of rolling bearing degradation performance is higher. The model has the strong robustness and the generalization ability, which has the important engineering practical value for the prediction of the equipment health state.http://dx.doi.org/10.1155/2021/6615920 |
| spellingShingle | Yaping Wang Chaonan Yang Di Xu Jianghua Ge Wei Cui Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM Shock and Vibration |
| title | Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM |
| title_full | Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM |
| title_fullStr | Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM |
| title_full_unstemmed | Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM |
| title_short | Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM |
| title_sort | evaluation and prediction method of rolling bearing performance degradation based on attention lstm |
| url | http://dx.doi.org/10.1155/2021/6615920 |
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