SHAP-Based Feature Selection with a Hybrid Convolutional and Recurrent Deep Learning Framework for Remaining Useful Life Prediction of Aero-Engines

作者：N. Ouafek, Tarek Berghout, Samira Abderrezek, Abdelhabib Bourouis · 发表于：International Journal of Prognostics and Health Management · 年份：2026 · DOI：10.36001/ijphm.2026.v17i1.4693 · 被引用次数：1

Prognostics and health management for aeroengines is crucial, especially for reducing catastrophic loss of life and minimizing maintenance costs. Accurate Remaining Useful Life (RUL) estimation optimizes schedules, lowers costs, and enhances safety. Physics-based modeling for RUL assessment is challenging due to its inability to fully account for system and environment-related dynamics. Therefore, machine and deep learning approaches are highly recommended. This paper proposes a new method for RUL prediction of turbofan engines using the well-known (CMAPSS) dataset. Generally, data from acquired engines may be corrupted by anomalies due to sensor failures or environmental disturbances, which can affect the accuracy of prediction models. Therefore, this paper combines advanced techniques for reducing irrelevant and redundant sensor signals by applying Shapley Additive Explanations (SHAP) to refine feature selection by quantifying each sensor’s contribution to RUL prediction, ensuring both interpretability and efficiency. Then an anomaly detection and removal followed by RUL prediction with deep learning are used for such complex tasks. Specifically, K-means clustering, autoencoders, Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM) networks. This approach enables effective data preprocessing by detecting and removing anomalies, thus enhancing the quality of the training data. Moreover, uncertainty analysis is performed to assess the reliability of the pre...