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Dissertation/Thesis Abstract

Methodology for Fleet Uncertainty Reduction with Unsupervised Learning
by Modarres, Ceena, M.S., University of Maryland, College Park, 2016, 77; 10253325
Abstract (Summary)

Operational and environmental variance can skew reliability metrics and increase uncertainty around lifetime estimates. For this reason, fleet-wide analysis is often too general for accurate predictions on heterogeneous populations. Also, modern sensor based reliability and maintainability field and test data provide a higher level of specialization and disaggregation to relevant integrity metrics (e.g., amount of damage, remaining useful life). Modern advances, like Dynamic Bayesian Networks, reduce uncertainty on a unit-by-unit basis to apply condition-based maintenance. This thesis presents a methodology for leveraging covariate information to identify sub- populations. This population segmentation based methodology reduces fleet uncertainty for more practical resource allocation and scheduled maintenance. First, the author proposes, validates, and demonstrates a clustering based methodology. Afterwards, the author proposes the application of the Student-T Mixture Model (SMM) within the methodology as a versatile tool for modeling fleets with unclear sub-population boundaries. SMM’s fully Bayesian formulation, which is approximated with Variational Bayes (VB), is motivated and discussed. The scope of this research includes a new modeling approach, a proposed algorithm, and example applications.

Indexing (document details)
Advisor: Fuge, Mark D.
Commitee: Balachandran, Balakumar, Droguett, Enrique L., Mosleh, Ali
School: University of Maryland, College Park
Department: Reliability Engineering
School Location: United States -- Maryland
Source: MAI 56/05M(E), Masters Abstracts International
Subjects: Engineering
Keywords: Clustering, Reliability engineering, Student-t mixture models, Uncertainty, Unsupervised learning, Variational bayes
Publication Number: 10253325
ISBN: 978-0-355-06058-4
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