Controlling Tracking Performance for System Health Management - A Markov Decision Process Formulation

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Published Nov 3, 2020
Brian Bole Kai Goebel George Vachtsevanos

Abstract

After an incipient fault mode has been detected a logical question to ask is: How long can the system continue to be operated before the incipient fault mode degrades to a failure condition? In many cases answering this question is complicated by the fact that further fault growth will depend on how the system is intended to be used in the future. The problem is then complicated even further when we consider that the future operation of a system may itself be conditioned on estimates of a system’s current health and on predictions of future fault evolution. This paper introduces a notationally convenient formulation of this problem as a Markov decision process. Prognostics-based fault management policies are then shown to be identified using standard Markov decision process optimization techniques. A case study example is analyzed, in which a discrete random walk is used to represent time-varying system loading demands. A comparison of fault management policies computed with and without future uncertainty is used to illustrate the limiting effects of model uncertainty on prognostics-informed fault management policies.

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Keywords

prognostics, uncertainty management, Asset health management, Markov Decision Process, Risk-Reward Trade-off

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Technical Papers