PRIME: A Physics-Guided Residual Integrated Framework for Multi-Task Aircraft Engine Diagnostics

Accurate aircraft engine diagnostics is essential for ensuring operational safety and enabling predictive maintenance under heterogeneous operating conditions. Although deep learning models can effectively capture high-dimensional multivariate sensor dynamics, purely data-driven approaches often entangle operating-condition variability with degradation-sensitive patterns, which limits robustness and generalization. This paper introduces PRIME, a physics-guided residual integrated framework for multi-task aircraft engine diagnostics. Rather than embedding explicit thermodynamic equations or physical constraints into the optimization process, PRIME relies on a physically motivated residual decomposition strategy that separates operating-condition-driven nominal behavior from degradation-sensitive sensor deviations. Specifically, nominal responses are estimated from operating-condition representations and subtracted from observed sensor signals to isolate fault-relevant residual patterns. These residual representations are then processed by a hybrid temporal architecture combining temporal convolutional networks and transformer-based self-attention, enabling joint modeling of local degradation signatures and long-range temporal dependencies. Within a unified optimization framework, PRIME simultaneously performs Fault Detection (FD), Fault Type Classification (FTC), and Health State Estimation (HSE). Extensive experiments on NASA C-MAPSS, N-CMAPSS, and the ALFA dataset show consistent and statistically significant improvements over strong baseline models. For FD and FTC, PRIME achieves gains of approximately 2%–4% over the strongest neural baselines evaluated under the same protocol, with larger margins over classical machine learning approaches. For HSE, PRIME yields more faithful degradation trajectories, leading to systematic reductions in estimation error across single- and multi-regime datasets. When Remaining Useful Life (RUL) is projected from the learned health trajectory through a threshold-based mechanism, the resulting estimates also improve substantially, with RMSE reductions of up to about 22% under complex operating conditions. These results show that physics-guided residual disentanglement improves robustness, interpretability, and multi-task diagnostic performance. More broadly, they support the view that HSE provides a useful latent degradation representation for downstream prognostic assessment, even though RUL is not directly optimized by the model.