Availability-Aware Remaining Useful Life Prediction for Aero-Engines with Unavailable Sensor Channels

Abstract

Aero-engine remaining useful life (RUL) prediction supports condition-based maintenance, yet most data-driven models assume fixed sensor availability. Power-supply, acquisition, or communication failures can invalidate this assumption. We propose the Remaining Useful Life Dual-Attention Robust Network (RUL-DARNet), which combines a convolutional neural network–long short-term memory (CNN–LSTM) backbone with training-stage whole-channel Sensor Dropout (SD), Mask-Aware (MA) feature attention, and temporal attention. SD exposes the model to reduced sensor sets, whereas MA excludes unavailable channels from feature-attention normalization using an explicit availability mask. Ten seeds and ten paired masks were evaluated across four Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) subsets under controlled synthetic sensor unavailability. Trajectory metrics use a 125-cycle label cap and equal engine weighting. At the prespecified FD001 40% Missing Completely at Random endpoint, RUL-DARNet attained an RMSE of 17.474 ± 1.410 cycles, compared with 19.023 ± 0.706 for SD-only. Adding MA after SD reduced RMSE by 1.549 cycles in nine of ten seeds after Holm correction. Benefits weakened or reversed under value-related missingness, multiple operating conditions, and several trajectory-level outages. Training-stage exposure accounts for most of the observed robustness, while mask-aware reweighting provides a smaller, conditional benefit within the tested C-MAPSS protocols when availability labels are reliable and the remaining channels retain degradation information.