Bayesian Estimation for Reliability Engineering: Addressing the Influence of Prior Choice

Leonardo Leoni, Farshad BahooToroody, Saeed Khalaj, Filippo De Carlo, Ahmad BahooToroody and Mohammad Mahdi Abaei
Additional contact information
Leonardo Leoni: Department of Industrial Engineering (DIEF), University of Florence, 50123 Florence, Italy
Farshad BahooToroody: Department of Civil Engineering, University of Parsian, Qazvin 3176795591, Iran
Saeed Khalaj: Department of Civil Engineering, University of Parsian, Qazvin 3176795591, Iran
Filippo De Carlo: Department of Industrial Engineering (DIEF), University of Florence, 50123 Florence, Italy
Ahmad BahooToroody: Marine and Arctic Technology Group, Department of Mechanical Engineering, Aalto University, 11000 Espoo, Finland
Mohammad Mahdi Abaei: Department of Maritime and Transport Technology, Delft University of Technology, 2628 CD Delft, The Netherlands

IJERPH, 2021, vol. 18, issue 7, 1-16

Abstract: Over the last few decades, reliability analysis has attracted significant interest due to its importance in risk and asset integrity management. Meanwhile, Bayesian inference has proven its advantages over other statistical tools, such as maximum likelihood estimation (MLE) and least square estimation (LSE), in estimating the parameters characterizing failure modelling. Indeed, Bayesian inference can incorporate prior beliefs and information into the analysis, which could partially overcome the lack of data. Accordingly, this paper aims to provide a closed-mathematical representation of Bayesian analysis for reliability assessment of industrial components while investigating the effect of the prior choice on future failures predictions. To this end, hierarchical Bayesian modelling (HBM) was tested on three samples with distinct sizes, while five different prior distributions were considered. Moreover, a beta-binomial distribution was adopted to represent the failure behavior of the considered device. The results show that choosing strong informative priors leads to distinct predictions, even if a larger sample size is considered. The outcome of this research could help maintenance engineers and asset managers in integrating their prior beliefs into the reliability estimation process.

Keywords: reliability analysis; hierarchical Bayesian modelling; prior information; beta-binomial failure modelling (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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