A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction

With the rapid development of sensor and information technology, now multisensor data relating to the system degradation process are readily available for condition monitoring and remaining useful life (RUL) prediction. The traditional data fusion and RUL prediction methods are either not flexible enough to capture the highly nonlinear relationship between the health condition and the multisensor data or have not fully utilized the past observations to capture the degradation trajectory. In this article, we propose a joint prognostic model (JPM), where Bayesian linear models are developed for multisensor data, and an artificial neural network is proposed to model the nonlinear relationship between the residual life, the model parameters of each sensor data, and the observation epoch. A Bayesian updating scheme is developed to calculate the posterior distributions of the model parameters of each sensor data, which are further used to estimate the posterior predictive distributions of the residual life. The effectiveness and advantages of the proposed JPM are demonstrated using the commercial modular aero-propulsion system simulation data set.