Dual-attention enhanced variational encoding for interpretable remaining useful life prediction

In Prognostics Health Management (PHM), predicting Remaining Useful Life (RUL) is a key technique for equipment health evaluation. The utilization of deep learning methods has improved prediction accuracy. However, these approaches often fail to provide the transparency and interpretability that maintenance personnel require to diagnose equipment degradation effectively. To address this challenge, a Dual-Attention Enhanced Variational Encoding (DAEVE) approach based on Transformer is developed for more interpretable RUL prediction. This framework integrates both sensor and time step encoders, a latent space with inductive bias and a regression model: the fusion encoder compresses input data into a three-dimension(3-D) latent space, facilitating both the prediction and interpretation of the equipment degradation process. Four turbofan aircraft engine datasets are applied in extensive experiments to evaluate the efficacy of proposed method. The results demonstrate that DAEVE outperforms most state-of-the-art methods in prediction accuracy. Furthermore, the proposed method exhibits the latent degradation trajectories and more informative sensors in diverse stages. This research could enhance maintenance decision-making processes and reduce operational risks, contributing to the advancement of predictive maintenance in the aerospace and related industries.