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

2026-08-06

AI Summary

This work addresses the challenges of error accumulation and violation of degradation irreversibility in existing recursive health indicator prediction methods for long-term remaining useful life (RUL) estimation. To mitigate these issues, the authors propose a hybrid representation mechanism that integrates local and global features to reduce sensitivity to high-frequency noise. Additionally, they introduce a trend-guided recursive consistency loss function, which leverages soft dynamic time warping alignment and a multi-step rollout strategy to effectively bridge the gap between training and inference dynamics. Experimental results on two public bearing datasets demonstrate that the proposed approach significantly enhances the stability of long-term health indicator extrapolation and improves RUL prediction accuracy, while maintaining low computational complexity and strong generalization capability.

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.

Problem

• remaining useful life
• degradation process
• health indicator
• error accumulation
• irreversible degradation trend

Innovation

• Local-Global Feature Mixer
• Trend-Guided Consistent Learning
• Remaining Useful Life Prediction
• Degradation Process Modeling
• Recursive Forecasting