RUL prediction based on two-phase wiener process

The degradation model and the remaining useful life (RUL) prediction are two key metrics for machine health prognostics. However, the precision of RUL prediction highly depends on the validity of the degradation model. Consequently, the uncertainty of RUL prediction based on the two-phase degradation process may cause the estimated product reliability to differ from its actual value and lead to a faulty predictive maintenance strategy. However, recent studies have rarely compared the RUL predictions based on two-phase and single-phase degradation models. In this study, a real-time RUL prediction approach was developed for a two-phase linear Wiener degradation process. A two-phase Wiener degradation model was proposed along with an estimation method for the change point and model parameters. Based on a state-space model and Markov chain Monte Carlo, the drift coefficient of the degradation model was updated, and a real-time RUL prediction model was developed. To examine whether the two-phase Wiener degradation model was incorrectly assumed to be single-phase, a case study was conducted to analyze RUL prediction based on the original and incorrect models. The results demonstrated that to obtain a more accurate RUL prediction, a sufficient number of samples should be used in addition to updating the drift coefficient of the degradation model when new measurement data are available.