Field degradation modeling and prognostics under time-varying operating conditions: A Bayesian based filtering algorithm

In practice, degradation modeling and prognostics for a product working in field is of importance for condition-based maintenance, meanwhile a challenging work due to the uncertainty in degradation and the time-varying operating conditions. This study investigates a degradation modeling framework for field working product and its applications by incorporating multiple data sources to associate the operating condition and reduce the uncertainty. The Wiener process is adopted to model the degradation process with nonlinearity, unit-to-unit variation, and condition covariates, which is delineated by state space modeling. Meanwhile, the acceleration factor, which establishes the relationship between operating condition and degradation rate, is integrated to adapt the drift parameter. A recursive filtering algorithm based on Bayesian theorem is employed for online updating of drift. Finally, a simulation study and an application on degradation modeling of turbofan engine are given to demonstrate the feasibility and validity of the proposed model.