Query-Vector-Focused Recurrent Attention for Remaining Useful Life Prediction

Prognostics and health management (PHM) plays a crucial role in ensuring the reliability and operational efficiency of industrial systems through continuous monitoring. Among PHM tasks, accurate remaining useful life (RUL) prediction is essential for preventing failures and optimizing maintenance strategies. Recently, attention-based deep learning models have been actively explored for RUL prediction. In attention mechanisms, the query vector enhances the effectiveness of feature selection by determining which parts of the input the model focuses on. However, existing RUL prediction models typically extract query, key, and value features through the same process, leading to a generic attention mechanism that lacks label awareness. As a result, these models may struggle to capture task-specific degradation patterns, which are critical for precise RUL estimation. To address this limitation, we propose a recurrent attention network designed to learn a well-structured query vector. The proposed method explicitly incorporates label characteristics and temporal correlations, improving its ability to focus on task-relevant features. By applying this well-structured query vector within the attention mechanism, our approach effectively enhances feature representation and improves the predictive accuracy of RUL estimation. We evaluate our method on two public benchmark datasets, and experimental results demonstrate that the proposed approach achieves superior performance compared to existing methods.