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A Bayesian Network Approach to Early Reliability Assessment of Complex Systems


Abstract Bayesian networks are powerful tools in system reliability assessment due to their flexibility in modeling the reliability structure of complex systems. This dissertation develops Bayesian network models for system reliability analysis through the use of Bayesian inference techniques.

Bayesian networks generalize fault trees by allowing components and subsystems to be related by conditional probabilities instead of deterministic relationships; thus, they provide analytical advantages to the situation when the failure structure is not well understood, especially during the product design stage. In order to tackle this problem, one needs to utilize auxiliary information such as the reliability information from similar products and domain exp... (more)
Created Date 2016
Contributor Yontay, Petek (Author) / Pan, Rong (Advisor) / Montgomery, Douglas C (Committee member) / Shunk, Dan L (Committee member) / Du, Xiaoping (Committee member) / Arizona State University (Publisher)
Subject Industrial engineering / Bayesian inference / Bayesian networks / incomplete information / reliability assessment
Type Doctoral Dissertation
Extent 157 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Industrial Engineering 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis