MS-DA-ResNet: A Multiscale Dual-Attention Residual Network for Remaining Useful Life Prediction of Aero-Engines Under Multiple Operating Conditions

Accurate remaining useful life (RUL) prediction of aero-engines under varying operating conditions is challenging because condition-induced sensor variations can obscure degradation signatures. This study evaluates a multiscale dual-attention residual network (MS-DA-ResNet) under a leakage-controlled protocol on the multiple-condition FD002 and FD004 subsets. The 125-cycle cap is applied only to training and validation targets, whereas official NASA test endpoint labels remain uncapped. All models use the same engine-level split, fixed 14-sensor input, causal preprocessing, and best-validation checkpoint, as well as three identical matched seeds. Under the corrected official-label evaluation, MS-DA-ResNet provides the best aggregate results on both datasets: FD002 RMSE 25.14 ± 0.41, MAE 16.71 ± 0.21, R2 0.781 ± 0.007, and NASA Score 5597 ± 389; FD004 RMSE 24.58 ± 0.22, MAE 17.35 ± 0.07, R2 0.797 ± 0.004, and NASA Score 3430 ± 77. MS-DA-ResNet remains competitive but does not achieve universal superiority. Its error-direction behavior is interpreted separately, using Bias and the overestimation ratio. Condition-wise normalization with causal smoothing improves FD004 relative to the tested global-normalization and no-smoothing alternatives. A corrected five-seed L = 30 sensitivity run does not improve the primary L = 15 configuration.