MHT: A multiscale hourglass-transformer for remaining useful life prediction of aircraft engine

Abstract

Remaining useful life (RUL) prediction of aircraft engines is significant in the health monitoring, operation, and maintenance of aircraft. Capturing more comprehensive device degradation trends at different time scales and extracting long-term dependencies effectively among elements in long time series are two challenges in the field of aircraft engine RUL estimation. To address the aforementioned challenges, this paper proposes a novel multiscale Hourglass-Transformer (MHT) aircraft engine RUL prognostics. Specifically, an hourglass-shaped multiscale feature extractor (HME) is designed based on one-dimensional convolutional neural network, which can scale the time sequence into multi-time scales for feature fusion. Then, a transformer network is employed to further extract features from the fused feature map and output the RUL. To enhance inter-scale data attention, a pyramid self-attention mechanism is employed in both the encoder and decoder. Finally, the superiority and effectiveness of this approach are verified on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Furthermore, the robustness and generalization capability of this method are further validated on New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset.