A Bayesian‐Markov process for reliability prediction
Sun, Yong , Ma, Lin, & Fidge, Colin J. (2011) A Bayesian‐Markov process for reliability prediction. In Proceedings of the 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM), COMADEM International, Clarion Hotel, Stavanger, Norway.
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Accurate reliability prediction for large-scale, long lived engineering is a crucial foundation for effective asset risk management and optimal maintenance decision making. However, a lack of failure data for assets that fail infrequently, and changing operational conditions over long periods of time, make accurate reliability prediction for such assets very challenging. To address this issue, we present a Bayesian-Marko best approach to reliability prediction using prior knowledge and condition monitoring data. In this approach, the Bayesian theory is used to incorporate prior information about failure probabilities and current information about asset health to make statistical inferences, while Markov chains are used to update and predict the health of assets based on condition monitoring data. The prior information can be supplied by domain experts, extracted from previous comparable cases or derived from basic engineering principles. Our approach differs from existing hybrid Bayesian models which are normally used to update the parameter estimation of a given distribution such as the Weibull-Bayesian distribution or the transition probabilities of a Markov chain. Instead, our new approach can be used to update predictions of failure probabilities when failure data are sparse or nonexistent, as is often the case for large-scale long-lived engineering assets.
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|Item Type:||Conference Paper|
|Keywords:||Reliability prediction, Condition monitoring, Bayesian Markov process|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Systems Development Methodologies (080608)|
|Divisions:||Current > Schools > School of Chemistry, Physics & Mechanical Engineering|
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2011 [please consult the authors]|
|Deposited On:||27 Mar 2012 08:33|
|Last Modified:||27 Mar 2012 08:33|
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