Title:
Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions
Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions
Author(s)
Bole, Brian
Goebel, Kai
Vachtsevanos, George J.
Goebel, Kai
Vachtsevanos, George J.
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Abstract
A generalized Markov chain representation of fault dynamics is presented for the case that available modeling
of fault growth physics and future environmental stresses can be represented by two independent stochastic
process models. A contrived but representatively challenging example will be presented and analyzed, in which
uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing
process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands
to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an
optimal control policy over a finite time window for the case that stochastic models representing physics of
failure and future environmental stresses are known, and the states of both stochastic processes are observable
by implemented control routines. The fundamental limitations of optimization performed in the presence of
uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing
control policy with the outcomes that would be achievable if all modeling uncertainties were removed
from the system.
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Date Issued
2012-06-19
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Text
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Article