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Article

Remaining Useful Life Prediction of Electric Drive Bearings in New Energy Vehicles: Based on Degradation Assessment and Spatiotemporal Feature Fusion

1
School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China
2
Collaborative Innovation Center of Hennan Province for High-End Bearing, Henan University of Science and Technology, Luoyang 471000, China
*
Author to whom correspondence should be addressed.
Machines 2025, 13(10), 914; https://doi.org/10.3390/machines13100914
Submission received: 4 September 2025 / Revised: 25 September 2025 / Accepted: 2 October 2025 / Published: 3 October 2025

Abstract

Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, complicating the modeling of time dependent relationships and degradation states; therefore, a piecewise linear degradation model is appropriate. An RUL prediction method is proposed based on degradation assessment and spatiotemporal feature fusion, which extracts strongly time correlated features from bearing vibration data, evaluates sensitive indicators, constructs weighted fused degradation features, and identifies abrupt degradation points. On this basis, a piecewise linear degradation model is constructed that uses a path graph structure to represent temporal dependencies and a temporal observation window to embed temporal features. By incorporating GAT-LSTM, RUL prediction for bearings is performed. The method is validated on the XJTU-SY dataset and on a loaded ball bearing test rig for electric vehicle drive motors, yielding comprehensive vibration measurements for life prediction. The results show that the method captures deep degradation information across the full bearing life cycle and delivers accurate, robust predictions, providing guidance for the health assessment of electric drive bearings in new energy vehicles.
Keywords: new energy vehicles; electric drive bearing; remaining useful life; graph attention network new energy vehicles; electric drive bearing; remaining useful life; graph attention network

Share and Cite

MDPI and ACS Style

Yang, F.; Dong, E.; Zhong, Z.; Zhang, W.; Cui, Y.; Ye, J. Remaining Useful Life Prediction of Electric Drive Bearings in New Energy Vehicles: Based on Degradation Assessment and Spatiotemporal Feature Fusion. Machines 2025, 13, 914. https://doi.org/10.3390/machines13100914

AMA Style

Yang F, Dong E, Zhong Z, Zhang W, Cui Y, Ye J. Remaining Useful Life Prediction of Electric Drive Bearings in New Energy Vehicles: Based on Degradation Assessment and Spatiotemporal Feature Fusion. Machines. 2025; 13(10):914. https://doi.org/10.3390/machines13100914

Chicago/Turabian Style

Yang, Fang, En Dong, Zhidan Zhong, Weiqi Zhang, Yunhao Cui, and Jun Ye. 2025. "Remaining Useful Life Prediction of Electric Drive Bearings in New Energy Vehicles: Based on Degradation Assessment and Spatiotemporal Feature Fusion" Machines 13, no. 10: 914. https://doi.org/10.3390/machines13100914

APA Style

Yang, F., Dong, E., Zhong, Z., Zhang, W., Cui, Y., & Ye, J. (2025). Remaining Useful Life Prediction of Electric Drive Bearings in New Energy Vehicles: Based on Degradation Assessment and Spatiotemporal Feature Fusion. Machines, 13(10), 914. https://doi.org/10.3390/machines13100914

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