@article{DU2026, 
author = {Shaoyang DU and Chao ZHANG and Shaoping WANG and Rentong CHEN and Yuwei ZHANG and Rui MU},
title = {Remaining useful life prediction for hydraulic spool valve based on physics-informed Gamma process},
year = {2026},
journal = {Chinese Journal of Aeronautics},
volume = {39},
number = {3},
keywords = {Hydraulic equipment, Physics-informed Gamma process, Physics of Failure, Remaining Useful Life prediction, Stochastic models, Wear of materials},
url = {https://www.sciopen.com/article/10.1016/j.cja.2025.103824},
doi = {10.1016/j.cja.2025.103824},
abstract = {Internal leakage caused by wear in hydraulic spool valves represents a critical failure mode that threatens the performance of aircraft hydraulic systems and compromises flight safety. Due to complex operational loads and time-varying material properties, the relationship between wear state and Remaining Useful Life (RUL) is nonlinear. Consequently, accurately modeling this wear remains a significant challenge, as existing research often neglects the coupled effects of material properties, stress conditions, and dynamic lubrication parameters. To address this issue, this study proposes a novel framework integrating physical mechanisms with stochastic processes to enhance wear degradation modelling and RUL prediction. First, a Physics-of-Failure (PoF) model is developed based on Archard’s wear theory, which characterizes tribological behavior at the contact interface and accounts for the effects of lubrication and load conditions. Next, a Gamma process is introduced to model the degradation trajectory, with physical parameters guiding the specification of the time-scale function. A Bayesian expectation–maximization algorithm is employed to estimate and update the model parameters. Finally, a numerical simulation and case study on spool valves are conducted to demonstrate the effectiveness of the proposed model. The cross-validation results confirmed that the introduction of random effects effectively reduces the impact of uncertainty on physics-informed modeling. This study offers a systematic solution to RUL prediction for hydraulic systems.}
}