Robust Bayesian Reliability for Complex Systems under Prior-Data Conflict
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4, Issue 3
Abstract
This paper considers the quantification of system reliability in scenarios in which data, that is, failures or the absence of failures, occurring from the system’s use over time, are considered surprising from the perspective of prior information. A generalized, or imprecise, Bayesian approach is presented for general system structures in which the component lifetimes have Weibull distributions with a known shape parameter. For the scale parameter, a specific set of prior distributions is assumed that enables the prior-data conflict to be reflected through the increased imprecision in the posterior reliability bounds.
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Data Availability Statement
The code to reproduce all results and figures from the examples in the section “Examples” is available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
Gero Walter was supported by the DINALOG project Coordinated Advanced Maintenance and Logistics Planning for the Process Industries (CAMPI).
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4 • Issue 3 • September 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Aug 9, 2016
Accepted: Feb 28, 2018
Published online: Jun 14, 2018
Published in print: Sep 1, 2018
Discussion open until: Nov 14, 2018
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