Degradation modeling and RUL prediction for wet clutch with improved inverse Gaussian process considering dynamic measurement noise

Abstract

Accurate remaining useful life (RUL) prediction serves as a critical enabler for predictive maintenance of wet multi-disc clutches. While the inverse Gaussian (IG) process models have demonstrated potential in degradation modeling, their application to sealed clutch systems remains constrained by two unresolved challenges: (1) inherent heterogeneity in degradation trajectories, and (2) non-stationary uncertainties in operational data acquisition. This study presents a dual-random-effect enhanced IG process model that systematically addresses these limitations through three key innovations: First, a bivariate random effects structure decouples unit-to-unit variability from temporal degradation stochasticity. Second, state-dependent measurement uncertainties are mathematically formulated to capture noise characteristics that evolve with degradation progression. Third, a Bayesian Markov Chain Monte Carlo (MCMC) framework enables robust parameter estimation from degradation observations, synergistically integrated with sliding-window Monte Carlo simulations for reliability inference. Validated against clutch degradation datasets, the proposed method achieves more accurate RUL prediction under limited degradation observations compared to conventional IG and Gamma models. These advancements establish a new paradigm for prognostic modeling for wet clutches.