Remaining useful life prediction for two-phase degradation model based on reparameterized inverse Gaussian process

Two-phase degradation is a prevalent degradation mechanism observed in modern systems, typically characterized by a change in the degradation rate or trend of a system's performance at a specific time point. Ignoring this change in degradation models can lead to considerable biases in predicting the remaining useful life (RUL) of the system, and potentially leading to inappropriate condition-based maintenance decisions. To address this issue, we propose a novel two-phase degradation model based on a reparameterized inverse Gaussian process. The model considers variations in both change points and model parameters among different systems to account for subject-to-subject heterogeneity. The unknown parameters are estimated using both maximum likelihood and Bayesian approaches. Additionally, we propose an adaptive replacement policy based on the distribution of RUL. By sequentially obtaining new degradation data, we dynamically update the estimation of model parameters and of the RUL distribution, allowing for adaptive replacement policies. A simulation study is conducted to assess the performance of our methodologies. Finally, a Lithium-ion battery example is provided to validate the proposed model and adaptive replacement policy. Technical details and additional results of case study are available as online supplementary materials.