Multi-View Temporal Structure-Aware Learning for Remaining Useful Life Prediction

Abstract

The accurate prediction of Remaining Useful Life (RUL) is fundamental to Prognostics and Health Management (PHM), enabling predictive maintenance and ensuring the operational safety of complex industrial systems. While deep learning models have demonstrated significant potential in RUL estimation, existing approaches often struggle with noisy sensor signals, inconsistent predictions across overlapping time windows, and a lack of explicit modeling for the underlying temporal structure of the degradation process. This paper proposes a novel multi-view temporal structure-aware learning framework to address these challenges. The framework introduces a multi-view temporal perturbation mechanism that generates multiple perspectives of the degradation state, coupled with a Transformer-based backbone to capture long-range dependencies. To enhance stability and physical rationality, we design a multi-view consistency regularization term and a temporal ordering constraint learning mechanism. These components ensure that the model produces stable predictions across temporal shifts and adheres to the inherent monotonic degradation patterns. Experimental evaluations conducted on the NASA C-MAPSS dataset demonstrate that the proposed method significantly improves prediction accuracy, stability, and structural consistency compared to state-of-the-art baselines.