A novel tool for Bayesian reliability analysis using AHP as a framework for prior elicitation. (March 2020)

• Record Type: Journal Article
• Authors: Syed, Zaki; Shabarchin, Oleg; Lawryshyn, Yuri
• Is Part Of: Journal of loss prevention in the process industries. Volume 64(2020)
• Publication Date: 2020-03
• Subjects: Bayesian reliability analysis -- Quantitative risk analysis -- Expert elicitation -- Analytic hierarchy process -- Weibull; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2019.104024
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: Quantitative risk assessment (QRA) is a powerful and popular technique to support risk-based decisions. Unfortunately, QRAs are often hampered by significant uncertainty in the frequency of failure estimation for physical assets. This uncertainty is largely due to lack of quality failure data in published sources. The failure data may be limited, incompatible and/or outdated. Consequently, there is a need for robust methods and tools that can incorporate all available information to facilitate reliability analysis of critical assets such as pipelines, pressure vessels, rotating equipment, etc. This paper presents a novel practical approach that can be used to help overcome data scarcity issues in reliability analysis. A Bayesian framework is implemented to cohesively integrate objective data with expert opinion with the aim toward deriving time to failure distributions for physical assets. The Analytic Hierarchy Process is utilized to aggregate time to failure estimates from multiple experts to minimize biases and address inconsistencies in their estimates. These estimates are summarized in the form of informative priors that are implemented in a Bayesian update procedure for the Weibull distribution. The flexibility of the proposed methodology allows for efficiently dealing with data limitations. Application of the proposed approach is illustrated using a case study. Highlights: A method to merge expert opinion with failure data for Bayesian reliability analysis. AHP is used to obtain expert knowledge to construct informative priors. Metropolis-Hastings Markov Chain Monte Carlo method is used for Bayesian updating. The result is more accurate and precise estimation of model parameters. An elegant way of addressing uncertainty considering all available information.