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: Yaping Wang, Chaonan Yang, Di Xu, Jianghua Ge, Wei Cui
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2021/6615920
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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.
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institution Kabale University
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language English
publishDate 2021-01-01
publisher Wiley
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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
work_keys_str_mv AT yapingwang evaluationandpredictionmethodofrollingbearingperformancedegradationbasedonattentionlstm
AT chaonanyang evaluationandpredictionmethodofrollingbearingperformancedegradationbasedonattentionlstm
AT dixu evaluationandpredictionmethodofrollingbearingperformancedegradationbasedonattentionlstm
AT jianghuage evaluationandpredictionmethodofrollingbearingperformancedegradationbasedonattentionlstm
AT weicui evaluationandpredictionmethodofrollingbearingperformancedegradationbasedonattentionlstm