Towards Real-World RUL Prediction for Aircraft Compressors Under Variable Conditions with Physics-Guided Domain Adaptation

Remaining useful life prediction for aircraft centrifugal air compressors in real commercial operations poses challenges that controlled benchmark datasets do not expose. In-flight sensor signals superimpose genuine degradation on continuously varying operating conditions, and no channel can be assumed a priori to carry a reliable degradation signature. Moreover, models trained on one subset of a fleet generalize poorly to unseen aircraft and installation positions, a cross-domain problem of underappreciated practical severity. This paper presents a framework combining physics-guided processing with adaptive temporal encoding. A population-level selection procedure identifies surge margin as the most consistent degradation indicator across the fleet. Operating regime filtering and windowed aggregation recover the health trend from operational noise, and two physically motivated features encoding current health level and cumulative degradation rate enable cross-unit comparison without absolute signal values. Sinusoidal positional encoding provides lifecycle context without data leakage, and a structured cross-domain evaluation identifies encoding period mismatch as the primary mechanism of performance loss under fleet transfer. An adaptive recalibration scheme estimates each target unit's lifecycle scale from early observations alone, requiring no future information or labeled target data. The approach yields substantial, consistent gains in cross-domain accuracy across transfer scenarios of increasing domain distance, and a Gaussian Process Regression model further provides calibrated uncertainty estimates supporting risk-informed maintenance scheduling.

Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations

Accurate Remaining Useful Life prediction is critical for industrial predictive maintenance. However, real-world deployment is challenging due to the irregular nature of sensor observations, characterized by asynchronous sampling, burst missingness, and temporal jitter. Compounding this issue, purely data-driven models often generate physically implausible degradation trajectories that violate the irreversible nature of damage accumulation. To address this, we propose PC-MambaSDE, a unified continuous-time framework for robust RUL prediction under irregular observations. Specifically, we design a Mask-Aware Continuous Mamba Encoder that explicitly leverages observation masks to extract context-rich control signals. Furthermore, we introduce a Physics-Guided Latent SDE with parametrically rectified hybrid drift, superimposing a global physical bias to enforce monotonic degradation even amid severe observation gaps. Additionally, we formulate RUL prediction as a boundary value problem via a Terminal Degradation Penalty, which decouples a Health Index dimension and applies a penalty loss to guide trajectories toward the failure state. Theoretically, we prove that our variational objective is mathematically equivalent to minimizing the KL divergence via Girsanov's theorem, and we guarantee the global asymptotic stability of the learned dynamics through Lyapunov analysis. To enable rigorous evaluation, we develop a Hybrid Irregularity Generation Scheme that simulates realistic industrial imperfections. Extensive experiments on public benchmarks demonstrate that PC-MambaSDE significantly outperforms state-of-the-art methods, particularly under extreme observation scarcity, validating the efficacy of embedding physical priors into continuous-time latent dynamics.