Collaborative Online RUL Prediction of Multiple Assets With Analytically Recursive Bayesian Inference

Abstract

By using in situ health information, many existing studies for online remaining useful life (RUL) prediction adopt a stochastic process-based degradation model and a computation-intensive parameter estimation method for RUL prediction of a single operating asset. Nevertheless, it is common that there are multiple assets under operation, and it would be more statistically efficient to jointly update their RULs by allowing information sharing among them for model parameter estimation. To this end, we propose a collaborative RUL prediction framework with closed-form online update. The framework is a hybrid algorithm that combines the conjugate prior for part of the model parameters and a stochastic approximation to the rest parameters. With this framework, a recursive online Bayesian algorithm is developed to jointly update the model parameters and RUL prediction using data from multiple operating assets. The effectiveness of the proposed method is demonstrated through a simulation study and two real cases.