Due to the increasing desire for having more autonomous vehicle platforms and life cycle support mechanisms, there is a great need for the development of prognostic health management technologies that can detect, isolate and assess remaining useful life of critical subsystems. To meet these needs for next generation systems, dedicated prognostic algorithms must be developed that are capable of operating in an autonomous and real-time vehicle health management system that is distributed in nature and can assess overall vehicle health and its ability to complete a desired mission. This envisioned prognostic and health management system should allow vehicle-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 systems and can capture fault/failure mode propagation and interactions that occur in these systems, all the way up through the vehicle level. In the chapter, the authors will present a generic set of selected prognostic algorithm approaches, as well as provide an overview of the required vehicle-level reasoning architecture needed to integrate the prognostic information across systems.
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Byington, C.S., Roemer, M.J. (2009). Selected Prognostic Methods with Application to an Integrated Health Management System. In: Valavanis, K.P. (eds) Applications of Intelligent Control to Engineering Systems. Intelligent Systems, Control, and Automation: Science and Engineering, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3018-4_1
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