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

Brian Bole, Kai Goebel, and George Vachtsevanos
Publication Target: 
IJPHM
Publication Issue: 
Special Issue Uncertainty in PHM
Submission Type: 
Full Paper
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ijphm_15_024.pdf393.5 KBJune 30, 2015 - 2:56pm

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.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
024
Page Count: 
9
Submission Keywords: 
prognostics
Asset health management
Markov Decision Process
uncertainty management
Risk-Reward Trade-off
Submission Topic Areas: 
Automated reconfiguration
Component-level PHM
Health management system design and engineering
Submitted by: 
  
 
 
 

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