Bayesian estimation for reliability engineering

Addressing the influence of prior choice

Journal Article (2021)
Author(s)

Leonardo Leoni (University of Florence)

Farshad Bahootoroody (University of Parsian)

Saeed Khalaj (University of Parsian)

Filippo De Carlo (University of Florence)

Ahmad BahooToroody (Aalto University)

M.M. Abaei (TU Delft - Ship Design, Production and Operations)

Research Group
Ship Design, Production and Operations
Copyright
© 2021 Leonardo Leoni, Farshad Bahootoroody, Saeed Khalaj, Filippo De Carlo, Ahmad Bahootoroody, M.M. Abaei
DOI related publication
https://doi.org/10.3390/ijerph18073349
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Leonardo Leoni, Farshad Bahootoroody, Saeed Khalaj, Filippo De Carlo, Ahmad Bahootoroody, M.M. Abaei
Research Group
Ship Design, Production and Operations
Issue number
7
Volume number
18
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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.