Joint Online RUL Prediction for Multivariate Deteriorating Systems

Abstract

Stochastic processes and filtering methods are popular tools for degradation modeling and online remaining useful life (RUL) prediction. However, most models are for one-dimensional degradation and various filtering methods can only handle observations from a single system. This paper studies joint online RUL prediction of multi deteriorating systems with multi system observations and measurement errors. A multivariate degradation model equipped with a batch particle filter is developed and built for characterizing multiple dependent performance deteriorations with measurement errors in each system. The batch particle filter is developed for simultaneous online parameter estimation and degradation state identification by leveraging multi system observations. A numerical example and a case study are provided to demonstrate the proposed method. The results show that homogeneous multi system observations from a population of multi deteriorating systems can be jointly processed on-the-fly. Individualized online RUL prediction with improved precision for each system can be achieved through the joint online inference.