Local-Global Feature Mixer and Trend-Guided Consistent Learning for Remaining Useful Life Prediction of Rotating Machinery

Abstract

Degradation process (DP) modeling is widely used for remaining useful life (RUL) prediction, particularly when run-to-failure data are limited. Neural networks can present complex degradation trajectories without prescribing a fixed degradation function; however, recursive health indicator (HI) forecasting is prone to error accumulation and may fail to preserve the irreversible degradation trend over long horizons. To address these limitations, this study proposes a local-global feature mixer (LGFM) and trend-guided rollout-consistent (TG-RC) loss. The LGFM combines the original HI sequence with statistical and degradation-related features to reduce sensitivity to high-frequency noise and capture both local changes and global degradation states. The TG-RC loss supplements the conventional one-step mean squared error with a recursive multi-step rollout loss and a soft dynamic time warping alignment term based on a global trend prior. Consequently, it reduces the discrepancy between training and recursive inference, while guiding the predicted trajectory toward a consistent degradation direction. Experiments on two public bearing datasets show that the proposed framework improves long-term HI extrapolation stability and RUL prediction performance across different inspection times. The LGFM also maintains low computational complexity, while the TG-RC loss can be incorporated into various DP models to improve their long-term forecasting performance.