EM based estimation for hybrid prognostic models

Prognostic health monitoring (PHM) is an important element of condition based maintenance and logistics support. The accuracy of prediction and the associated confidence in the prediction greatly influences overall performance and subsequent actions either for maintenance or logistics support. A prognostic model is a mathematical framework for making such predictions. Accuracy of prognosis is directly dependent on how closely one can capture the system and component interactions. In this paper, we consider a hybrid model for the prognosis of deteriorating systems and come up with identification and estimation algorithms. The model considered herewith represents the degrading system as collection of prognostic states (health vectors) which evolve continuously over time. The model includes an age dependent deterioration distribution, component interactions, as well as effects of discrete events arising from line maintenance actions and or abrupt faults. Authors develop the estimation and system identification scheme for such models. This model provides non-trivial challenges for system identification and parameter estimation. In this paper, we derive Expectation Maximization (EM) based system identification and a recursive Bayesian state estimation for predicting the health of degrading asset. The efficiency of the health prediction has been demonstrated for the prognosis of APU using simulated and field data.