An Overview of Selected Prognostic Technologies With Application to Engine Health Management

Abstract

The DoD has various vehicle platforms powered by high performance gas turbine engines that would benefit greatly from predictive health management technologies that can detect, isolate and assess remaining useful life of critical line replaceable units (LRUs) or subsystems. In order to meet these needs for next generation engines, dedicated prognostic algorithms must be developed that are capable of operating in an autonomous and real-time engine health management system software architecture that is distributed in nature. This envisioned prognostic and health management system should allow engine-level reasoners to have visibility and insight into the results of local diagnostic and prognostic technologies implemented down at the LRU and subsystem levels. To accomplish this effectively requires an integrated suite of prognostic technologies that can be applied to critical engine systems and can capture fault/failure mode propagation and interactions that occur in these systems, all the way up through the engine and eventually vehicle level. In the paper, the authors will present a generic set of selected prognostic algorithm approaches that can be applied to gas turbine engines, as well as provide an overview of the required reasoning architecture needed to integrate the prognostic information across the engine.

Remaining useful life estimation methods for predictive maintenance models: defining intervals and strategies for incomplete data

“…The interest in prognosis has been growing for a few decades and many solutions have been developed, tested and improved over the years. These methods can be classified into 3 main categories (Le, 2016) (Vachtsevanos, Lewis, Roemer, Hess, & Wu, 2006) (Soualhi, Razik, Clerc, & Doan, 2014) (Roemer, Byington, Kacprzynski, & Vachtsevanos, 2006): -Physical model-based prognosis that rely on mathematical or physical models of the degradation phenomenon.…”

A survey of maintenance and service logistics management: Classification and research agenda from a maritime sector perspective

“…He demonstrates that when the failure mechanisms are known and the usage or loading of the components is monitored, the uncertainty in the prediction of failures can be significantly reduced. Some other studies in which physical models are used to describe the degradation process of systems such as bearings, gear tooth, and electronic systems include [78,79,80].…”

Decision of Prognostics and Health management under Uncertainty

“…Prognosis is certainly the Achilles' heel of the Prognostics and Health Management (PHM) system and presents major challenges to the CBM/PHM system designer primarily because it entails large-grain uncertainty [4]. Uncertainty management of prognostics holds the key for a successful penetration of prognostics as a key enabler to health management in industrial applications [5].…”