An Integrated Attention-BiLSTM Approach for Probabilistic Remaining Useful Life Prediction

Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies, effectively reducing both the frequency of failures and associated costs. As a core component of PHM, RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making. However, deep learning models often falter when processing raw, noisy temporal signals, fail to quantify prediction uncertainty, and face challenges in effectively capturing the nonlinear dynamics of equipment degradation. To address these issues, this study proposes a novel deep learning framework. First, a new bidirectional long short-term memory network integrated with an attention mechanism is designed to enhance temporal feature extraction with improved noise robustness. Second, a probabilistic prediction framework based on kernel density estimation is constructed, incorporating residual connections and stochastic regularization to achieve precise RUL estimation. Finally, extensive experiments on the C-MAPSS dataset demonstrate that our method achieves competitive performance in terms of RMSE and Score metrics compared to state-of-the-art models. More importantly, the probabilistic output provides a quantifiable measure of prediction confidence, which is crucial for risk-informed maintenance planning, enabling managers to optimize maintenance strategies based on a quantifiable understanding of failure risk.