Fault-Type-Aware Remaining Useful Life Prediction of Aircraft Engines Using an Integrated Deep Learning Framework

Accurate Remaining Useful Life (RUL) prediction is essential for reducing maintenance costs and improving operational efficiency in high-value, complex systems such as aircraft engines. Data-driven approaches have emerged as a primary methodology in RUL estimation research, demonstrating significant improvements in performance. However, discrepancies in degradation trajectories across multiple failure modes can adversely affect the prediction accuracy. To address this challenge, this study proposes an integrated framework based on TS K-Means–BiLSTM to perform RUL prediction considering different failure modes. Specifically, Time Series K-Means Clustering (TS K-Means) is used to cluster time series data into latent failure-mode groups, and a Bidirectional Long Short-Term Memory (BiLSTM) network is subsequently employed to predict the RUL for each group. The proposed framework is validated using the Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA. Experimental results show that the proposed model outperforms existing methods. In addition, it achieves better results than the comparison Bi-LSTM model trained under the same conditions but without fault-type separation. This improvement likely results from minimizing interference between degradation patterns, allowing the model to better distinguish the unique behaviors associated with each fault type. Consequently, the proposed approach demonstrates strong potential for practical RUL prediction tasks.