Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing

2025/01/13 by Tao, Laifa, Zhao, Zhengduo, Wang, Xuesong +8
#FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2501.07191

Abstract

Accurately predicting the remaining useful life (RUL) of rotating machinery, such as bearings, is essential for ensuring equipment reliability and minimizing unexpected industrial failures. Traditional data-driven deep learning methods face challenges in practical settings due to inconsistent training and testing data distributions and limited generalization for long-term predictions.

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