Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing
This work addresses the challenge of accurate long-term RUL prediction for rotating machinery, which is essential for preventing industrial failures, though it appears incremental as it adapts existing LLM techniques to a specific domain.
The paper tackles the problem of predicting the remaining useful life of bearings, a critical task for industrial reliability, by proposing a method based on pre-trained large language models to address issues like data distribution inconsistencies and limited generalization in traditional deep learning approaches.
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.