A verification methodology for prognostic algorithms

Prognosis is a fundamental enabling technique for condition-based maintenance (CBM) systems and prognostics and health management (PHM) systems and therefore, plays a critical role in the successful deployment of these systems. The purpose of prognosis is to predict the remaining useful life of a system/subsystem or a component when a fault is detected. Although different prognostic algorithms have been developed and tentatively applied to various mechanical and electrical systems in the past decade, the verification and validation (V&V) remains a challenging open problem. The difficulties lie in the facts that first, there is usually no statistically sufficient data to do V&V and second, there is no rigorous and general V&V framework available. In this paper, a verification methodology based on exact filtering and Monte Carlo method is proposed to verify a user defined particle-filtering based prognostic algorithm. The methodology is a general one that can be extended to verification of other prognostic algorithms straightforward. When statistically sufficient data are available, validation can be implemented under the similar framework. The verification methodology is demonstrated on the prognosis of a seeded-fault planetary helicopter gearbox carrier.