Abstract

Failure prognostic algorithms require to reduce the computational burden associated with their implementation to ensure real-time performance in embedded systems. In this regard, this paper presents a method that allows to significantly reduce this computational cost in the case of particle-filter-based prognostic algorithms, which is based on a time-variant prognostic update rate. In this proposed scheme, the performance of the prognostic algorithm within short-term prediction horizons is continuously compared with respect to the outcome of Bayesian state estimators. Only if the discrepancy between prior and posterior knowledge is greater than a given threshold, it is suggested to execute the prognostic algorithm once again and update Time-of-Failure estimates. In addition, a novel metric to evaluate the performance of any prognostic algorithm in real-time is hereby presented. The proposed actualization scheme is implemented, tested, and validated in two case studies related to the problem of State-of-Charge (SOC) prognostics. The obtained results show that the proposed strategy allows to significantly reduce the computational cost while keeping the standards in terms of algorithm efficacy.