On reliability challenges of repairable systems using hierarchical bayesian inference and maximum likelihood estimation

Journal article (2020)

Authors

Ahmad BahooToroody University of Florence
M.M. Abaei Ship Design, Production and Operations - Mechanical, Maritime and Materials Engineering
Ehsan Arzaghi Queensland University of Technology
Guozheng Song Norwegian University of Science and Technology (NTNU)
Filippo De Carlo University of Florence
Nicola Paltrinieri Norwegian University of Science and Technology (NTNU)
Rouzbeh Abbassi Macquarie University
Research Group
Ship Design, Production and Operations (Mechanical, Maritime and Materials Engineering) (TU Delft)
Maximum likelihood estimation Time dependency Failure modelling Hierarchical Bayesian analysis Repairable system
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Published Date

2020

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Ship Design, Production and Operations

Abstract

Failure modelling and reliability assessment of repairable systems has been receiving a great deal of attention due to its pivotal role in risk and safety management of process industries. Meanwhile, the level of uncertainty that comes with characterizing the parameters of reliability models require a sound parameter estimator tool. For the purpose of comparison and cross-verification, this paper aims at identifying the most efficient and minimal variance parameter estimator. Hierarchical Bayesian modelling (HBM) and Maximum Likelihood Estimation (MLE) approaches are applied to investigate the effect of utilizing observed data on inter-arrival failure time modelling. A case study of Natural Gas Regulating and Metering Stations in Italy has been considered to illustrate the application of proposed framework. The results highlight that relaxing the renewal process assumption and taking the time dependency of the observed data into account will result in more precise failure models. The outcomes of this study can help asset managers to find the optimum approach to reliability assessment of repairable systems.