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2013 | OriginalPaper | Chapter

Decision Making and Optimization for Inspection Planning under Parametric Uncertainty of Underlying Models

Authors : Nicholas Nechval, Gundars Berzins, Vadim Danovich, Konstantin Nechval

Published in: Contemporary Challenges and Solutions in Applied Artificial Intelligence

Publisher: Springer International Publishing

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Certain fatigued structures must be inspected in order to detect fatigue damages that would otherwise not be apparent. A technique for obtaining optimal inspection strategies is proposed for situations where it is difficult to quantify the costs associated with inspections and undetected failure. For fatigued structures, for which failures (fatigue damages) are only detected at the time of inspection, it is important to be able to determine the optimal times of inspection. Fewer inspections will lead to lower fatigue reliability of the structure upon demand, and frequent inspections will lead to higher cost. When there is a fatigue reliability requirement, the problem is usually to develop an inspection strategy that meets the reliability requirements. It is assumed that only the functional form of the underlying invariant distribution of time-to-failure is specified, but some or all of its parameters are unspecified. The invariant embedding technique proposed in this paper allows one to construct an optimal inspection strategy under parametric uncertainty. This strategy represents a sequence of inspection times satisfying the specific criterion, which takes into account the predetermined value of the conditional fatigue reliability of the structure. A numerical example is given.

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Metadata
Title
Decision Making and Optimization for Inspection Planning under Parametric Uncertainty of Underlying Models
Authors
Nicholas Nechval
Gundars Berzins
Vadim Danovich
Konstantin Nechval
Copyright Year
2013
Publisher
Springer International Publishing
DOI
https://doi.org/10.1007/978-3-319-00651-2_17

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