A Matrix-Statistics-Aware Attention Mechanism for Robust RUL Estimation in Aero-Engines

Prognostics and Health Management (PHM) is a vital approach which aims to predict the failure of engineering systems at an early stage and optimize maintenance strategies. It operates through continuous system monitoring, anomaly detection, fault detection, and Remaining Useful Life (RUL) estimation. Accurate RUL prediction for aircraft engines is critical for enhancing operational safety and minimizing maintenance costs. Traditional methods are largely dependent on handcrafted features and domain-specific knowledge. They often fail to capture the nonlinear and high-dimensional degradation dynamics of real-world systems. In this study, we propose an enhanced deep learning architecture combining Long Short-Term Memory (LSTM) and Bidirectional LSTM networks with a new Matrix-Statistics-Aware Attention (LSTM-MSAA) method. Unlike conventional attention methods, our proposed method incorporates auxiliary scalar features, such as the Frobenius norm, spectral norm, and soft rank, into the attention score computation. This hybrid model provides a more informative representation of engine state transitions. The model is evaluated on both legacy and newly released C-MAPSS datasets from NASA’s Prognostics Data Repository. Experimental results reveal a reduction in RMSE compared to baseline models, validating the effectiveness of our attention fusion strategy in capturing intricate degradation behaviors and improving predictive performance.