Coupling-sensitive feature decoupling network for robust aero-engine RUL estimation under mixed operating conditions

Remaining useful life (RUL) estimation for aero-engines becomes unreliable under mixed operating conditions, where onboard sensor measurements are non-stationary and entangle two concurrent drivers: irreversible degradation evolution and reversible operating-response dynamics. When these effects are mixed in the measurement stream, data-driven estimators may overfit condition-induced fluctuations, causing biased RUL estimates after regime shifts. To mitigate such coupling-induced bias, we propose a lightweight coupling-sensitive feature decoupling network (CFDN) whose architecture encourages partial separation between degradation-related trends and condition-related fluctuations at the representation level, rather than enforcing a strict factorial decomposition. CFDN adopts a dual-stream backbone: a Trend Stream tracks slowly varying degradation representations, while a Fluctuation Stream summarizes short-term operating responses through attention pooling. We further introduce a zero-initialized residual fusion scheme that starts from a trend-only estimator and progressively incorporates fluctuation information to improve optimization stability under strong coupling. Experiments on the NASA Commercial Modular Aero-Propulsion System Simulation benchmark, supported by controlled ablations, branch-level quantitative analyses, and same-protocol multi-seed comparisons with paired Wilcoxon signed-rank tests and bootstrap 95% confidence intervals, show that CFDN is particularly effective on mixed-condition subsets and exhibits the smallest seed-level variability among the compared baselines. A supplementary pilot study on N-CMAPSS provides feasibility evidence on a more flight-realistic dataset, rather than a comprehensive cross-dataset generalization benchmark.