Multiple model moving horizon estimation approach to prognostics in coupled systems

This article presents a novel MM-MHE algorithm for online prediction of the component survival functions based on their usage profiles. The framework employs Cox PHM based on offline and online data for the RUL prediction. The proposed approach has been validated by way of application to data derived from an automotive ETC system simulator. The MM-MHE algorithm shows excellent performance (R² and MSE) in the presence of significant measurement noise over all windows and converges to the correct cluster number. The future work includes application of this approach to continuous PID and to account for the uncertainty in RUL estimation. In the near future, by simple transformations, the authors plan on implementing the MM-MHE algorithm for measurements and states between (-∞,∞) and considering the effects of process noise, hence, modifying the cost function accordingly. A potential extension of the Cox PHM framework for prognosis of coupled systems will be to model the coupled survival dynamics as monotone positive linear systems or monotone Markov processes in which the state matrix is a Metzler matrix (i.e., has nonnegative off-diagonal elements).