Multi-scale mixed-learning and evaluation prediction method for the remaining useful life of aero-engine

Abstract The aircraft engine, as the “power core” of an aircraft, has its operational efficiency and state stability directly linked to flight safety and passenger life assurance. The prediction of remaining useful life (RUL) is crucial for fault prognostics and health management (PHM) of engine systems. However, current RUL prediction methods face two gaps: (1) The current average accuracy of engines operating under different conditions and experiencing various damage modes requires improvement. (2) These methods overlook reducing uncertainty from the perspective of their framework design, which is crucial for industry decision-making. To bridge these gap, a multi-scale mixed-learning and evaluation prediction method (MMEPTMIXER), along with the Multi-scale Fusion Temporal Convolutional Network-Deep Time-series mixer (MFTCN-DTSmixer) integrated within it, is proposed to implement the RUL prediction. MMEPTMIXER combines maximum mean deviation with Multi-Level Quantile Loss (MLQL) to reduce uncertainty regions and improve model understanding of data. MFTCN-DTSmixer employs MFTCN and TSmixers’ parallelization to enhance local and global temporal feature extraction, improving prediction accuracy and reducing predictive uncertainty. Finally, MMEPTMIXER was applied to the C-MAPSS dataset, compared with 26 state-of-the-art methods, achieving an average root mean square error reduction rate of 10.16% and an average Score reduction rate of 28.07% across the corresponding four datasets. Furthermore, the method delivers accurate and robust RUL prediction results for the N-CMAPSS dataset. This research provides a robust supplement for the RUL prediction of aircraft engines as well as predictive maintenance in multi-sensor systems.