Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model

Li, Feifan; Dai, Zhuoheng; Jiang, Lei; Song, Chanfei; Zhong, Caiming; Chen, Yingna

doi:10.3390/s24216906

Open AccessArticle

Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model

by

Feifan Li

,

Zhuoheng Dai

,

Lei Jiang

,

Chanfei Song

,

Caiming Zhong

^* and

Yingna Chen

^*

School of Information Engineering, College of Science & Technology, Ningbo University, Ningbo 315000, China

^*

Authors to whom correspondence should be addressed.

Sensors 2024, 24(21), 6906; https://doi.org/10.3390/s24216906

Submission received: 29 September 2024 / Revised: 22 October 2024 / Accepted: 25 October 2024 / Published: 28 October 2024

(This article belongs to the Section Industrial Sensors)

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Abstract

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.

Keywords: bearing; RUL; CNN; Bi-LSTM; domain adaptation

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MDPI and ACS Style

Li, F.; Dai, Z.; Jiang, L.; Song, C.; Zhong, C.; Chen, Y. Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model. Sensors 2024, 24, 6906. https://doi.org/10.3390/s24216906

AMA Style

Li F, Dai Z, Jiang L, Song C, Zhong C, Chen Y. Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model. Sensors. 2024; 24(21):6906. https://doi.org/10.3390/s24216906

Chicago/Turabian Style

Li, Feifan, Zhuoheng Dai, Lei Jiang, Chanfei Song, Caiming Zhong, and Yingna Chen. 2024. "Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model" Sensors 24, no. 21: 6906. https://doi.org/10.3390/s24216906

APA Style

Li, F., Dai, Z., Jiang, L., Song, C., Zhong, C., & Chen, Y. (2024). Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model. Sensors, 24(21), 6906. https://doi.org/10.3390/s24216906

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Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model

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