In this paper, an adaptive inverse Gaussian stochastic process is developed to characterize the degradation process of condition monitored components. The knowledge of the degradation process is updated through the parameter of the process when new observations are available. The updating is performed through a general Bayesian filtering process within a state space model setting. The proposed adaptive model is history-dependent and can adjust itself to the sudden changes in degradation signals. The numerical case study shows that the variance of the RUL distribution obtained from the adaptive model is less than that of the conventional inverse Gaussian model and the predictive accuracy is improved by using the adaptive model in terms of TMSE. To validate our adaptive model further, we conduct a model prediction accuracy test. Our test result enables us to conclude that our model is stable, robust and beneficial for the application in prognostics and health management of systems.
