Residual Life Prediction of Bearings Based on SENet-TCN and Transfer Learning
Accurate bearing remaining life prediction guarantees safety and continued profitability for the industry. Variable operating conditions of the bearing and difficulty in obtaining corresponding data labels in the industry result in low prediction accuracy of the model. To solve these problems, a bea...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9955526/ |
| _version_ | 1797986689210122240 |
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| author | Yan Wang Hua Ding Xiaochun Sun |
| author_facet | Yan Wang Hua Ding Xiaochun Sun |
| author_sort | Yan Wang |
| collection | DOAJ |
| description | Accurate bearing remaining life prediction guarantees safety and continued profitability for the industry. Variable operating conditions of the bearing and difficulty in obtaining corresponding data labels in the industry result in low prediction accuracy of the model. To solve these problems, a bearing life prediction model based on an improved temporal convolutional network and transfer learning is proposed. First, the squeeze-and-excitation network is used to mine and recalibrate the deep features of source domain data. Second, the temporal convolutional network is used to calibrate the relationship between the features and lifetime, and the optimal source domain model is trained. Finally, the transfer learning training is conducted with the source domain model to obtain the transfer model, which can accurately predict the remaining life of the multi-operating condition signal. Comparative experiments were performed on IEEE PHM Challenge 2012 bearing life dataset. The results show that the proposed method can better mine the inherent degradation trend of bearings and effectively improve the prediction accuracy of the remaining useful life. Compared with the existing popular prediction methods, the prediction error was reduced by “20.8%” to “51.5%”, which proves the effectiveness and feasibility of the proposed method. |
| first_indexed | 2024-04-11T07:37:04Z |
| format | Article |
| id | doaj.art-122bdef84fe04f7a9848f3954c4ea823 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T07:37:04Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-122bdef84fe04f7a9848f3954c4ea8232022-12-22T04:36:41ZengIEEEIEEE Access2169-35362022-01-011012300712301910.1109/ACCESS.2022.32233879955526Residual Life Prediction of Bearings Based on SENet-TCN and Transfer LearningYan Wang0https://orcid.org/0000-0003-4478-0515Hua Ding1Xiaochun Sun2https://orcid.org/0000-0001-8331-1936Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, ChinaShanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, ChinaShanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, ChinaAccurate bearing remaining life prediction guarantees safety and continued profitability for the industry. Variable operating conditions of the bearing and difficulty in obtaining corresponding data labels in the industry result in low prediction accuracy of the model. To solve these problems, a bearing life prediction model based on an improved temporal convolutional network and transfer learning is proposed. First, the squeeze-and-excitation network is used to mine and recalibrate the deep features of source domain data. Second, the temporal convolutional network is used to calibrate the relationship between the features and lifetime, and the optimal source domain model is trained. Finally, the transfer learning training is conducted with the source domain model to obtain the transfer model, which can accurately predict the remaining life of the multi-operating condition signal. Comparative experiments were performed on IEEE PHM Challenge 2012 bearing life dataset. The results show that the proposed method can better mine the inherent degradation trend of bearings and effectively improve the prediction accuracy of the remaining useful life. Compared with the existing popular prediction methods, the prediction error was reduced by “20.8%” to “51.5%”, which proves the effectiveness and feasibility of the proposed method.https://ieeexplore.ieee.org/document/9955526/Bearingprediction of residual lifeSENettime convolution networkparticle swarm optimisationlife prediction |
| spellingShingle | Yan Wang Hua Ding Xiaochun Sun Residual Life Prediction of Bearings Based on SENet-TCN and Transfer Learning IEEE Access Bearing prediction of residual life SENet time convolution network particle swarm optimisation life prediction |
| title | Residual Life Prediction of Bearings Based on SENet-TCN and Transfer Learning |
| title_full | Residual Life Prediction of Bearings Based on SENet-TCN and Transfer Learning |
| title_fullStr | Residual Life Prediction of Bearings Based on SENet-TCN and Transfer Learning |
| title_full_unstemmed | Residual Life Prediction of Bearings Based on SENet-TCN and Transfer Learning |
| title_short | Residual Life Prediction of Bearings Based on SENet-TCN and Transfer Learning |
| title_sort | residual life prediction of bearings based on senet tcn and transfer learning |
| topic | Bearing prediction of residual life SENet time convolution network particle swarm optimisation life prediction |
| url | https://ieeexplore.ieee.org/document/9955526/ |
| work_keys_str_mv | AT yanwang residuallifepredictionofbearingsbasedonsenettcnandtransferlearning AT huading residuallifepredictionofbearingsbasedonsenettcnandtransferlearning AT xiaochunsun residuallifepredictionofbearingsbasedonsenettcnandtransferlearning |