Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model
Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. Estimating the RUL enables the assessment of health bearing, maintenance planning, and significant cost reduction, thereby fostering industrial development. Existing models rely on traditional feature engin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/6906 |
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| author | Feifan Li Zhuoheng Dai Lei Jiang Chanfei Song Caiming Zhong Yingna Chen |
| author_facet | Feifan Li Zhuoheng Dai Lei Jiang Chanfei Song Caiming Zhong Yingna Chen |
| author_sort | Feifan Li |
| collection | DOAJ |
| description | Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. Estimating the RUL enables the assessment of health bearing, maintenance planning, and significant cost reduction, thereby fostering industrial development. Existing models rely on traditional feature engineering with feature changes because operating conditions pose a major challenge to the generalization of RUL prediction models. This study focuses on neural network-based feature engineering and the downstream prediction of the RUL, eliminating the need for specific prior knowledge and simplifying the development and maintenance of models. Initially, a convolutional neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation characteristics of the engineered features and predict the RUL through regression. Finally, the study examines the influence of operating conditions in the model and integrates domain adaptation to minimize differences in feature distribution, thereby enhancing the model’s generalizability for the RUL prediction. |
| format | Article |
| id | doaj-art-68ac72fb73bf4ff8943cda14fa0d1325 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-68ac72fb73bf4ff8943cda14fa0d13252024-11-08T14:41:23ZengMDPI AGSensors1424-82202024-10-012421690610.3390/s24216906Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation ModelFeifan Li0Zhuoheng Dai1Lei Jiang2Chanfei Song3Caiming Zhong4Yingna Chen5School of Information Engineering, College of Science & Technology, Ningbo University, Ningbo 315000, ChinaSchool of Information Engineering, College of Science & Technology, Ningbo University, Ningbo 315000, ChinaSchool of Information Engineering, College of Science & Technology, Ningbo University, Ningbo 315000, ChinaSchool of Information Engineering, College of Science & Technology, Ningbo University, Ningbo 315000, ChinaSchool of Information Engineering, College of Science & Technology, Ningbo University, Ningbo 315000, ChinaSchool of Information Engineering, College of Science & Technology, Ningbo University, Ningbo 315000, ChinaPredicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. Estimating the RUL enables the assessment of health bearing, maintenance planning, and significant cost reduction, thereby fostering industrial development. Existing models rely on traditional feature engineering with feature changes because operating conditions pose a major challenge to the generalization of RUL prediction models. This study focuses on neural network-based feature engineering and the downstream prediction of the RUL, eliminating the need for specific prior knowledge and simplifying the development and maintenance of models. Initially, a convolutional neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation characteristics of the engineered features and predict the RUL through regression. Finally, the study examines the influence of operating conditions in the model and integrates domain adaptation to minimize differences in feature distribution, thereby enhancing the model’s generalizability for the RUL prediction.https://www.mdpi.com/1424-8220/24/21/6906bearingRULCNNBi-LSTMdomain adaptation |
| spellingShingle | Feifan Li Zhuoheng Dai Lei Jiang Chanfei Song Caiming Zhong Yingna Chen Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model Sensors bearing RUL CNN Bi-LSTM domain adaptation |
| title | Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model |
| title_full | Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model |
| title_fullStr | Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model |
| title_full_unstemmed | Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model |
| title_short | Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model |
| title_sort | prediction of the remaining useful life of bearings through cnn bi lstm based domain adaptation model |
| topic | bearing RUL CNN Bi-LSTM domain adaptation |
| url | https://www.mdpi.com/1424-8220/24/21/6906 |
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