Adaptive Degradation Modeling With Non-Markovian Characteristics for Remaining Useful Life Prediction

Stochastic process-based methods have been widely used for predicting the remaining useful life (RUL) in engineering system health management. However, real-world degradation processes often exhibit non-Markovian dynamics, nonlinear evolution patterns, and varying operating conditions, which pose significant challenges for accurate RUL prediction. To address these challenges, this study proposes an adaptive RUL prediction framework tailored for nonlinear degradation with structural variability and memory effects. The degradation process is initially modeled using fractional Brownian motion (FBM) based on a segment of historical degradation data. During operation, the model adequacy is continuously assessed through a prediction error metric. If the error exceeds a predefined threshold, a prediction error model is activated to recalibrate the model structure. Subsequently, the drift coefficient is updated using an enhanced variational Bayesian Kalman filter (VBKF). The RUL is predicted based on the first hitting time (FHT) concept, from which an approximate analytical distribution is derived. Model parameters are identified through maximum likelihood estimation (MLE). Finally, the effectiveness and adaptability of the proposed approach are demonstrated through case studies involving a blast furnace and lithium-ion batteries.