A hybrid prognosis and health monitoring strategy by integrating particle filters and neural networks for gas turbine engines

In this paper, a novel hybrid structure is proposed for the development of health monitoring techniques of nonlinear systems by integration of model-based and computationally intelligent and data-driven techniques. In our proposed health monitoring framework, the well-known particle filtering method is utilized to estimate the states as well as the health parameters of the system. Simultaneously, the system observations are predicted through an observation forecasting scheme which is developed based on artificial neural networks to construct observation profiles for future time horizons. As a case study, the proposed approach is applied to predict the health condition of a gas turbine engine when it is affected by degradation damage.