Bayesian maintenance decision optimisation based on computing the information value from condition inspections

Title: Bayesian maintenance decision optimisation based on computing the information value from condition inspections
Authors: Zou, GuangBanisoleiman, KianGonzález, Arturo
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Date: 26-Mar-2021
Online since: 2021-05-11T15:14:55Z
Abstract: A challenge in marine and offshore engineering is structural integrity management (SIM) of assets such as ships, offshore structures, mooring systems, etc. Due to harsh marine environments, fatigue cracking and corrosion present persistent threats to structural integrity. SIM for such assets is complicated because of a very large number of rewelded plates and joints, for which condition inspections and maintenance are difficult and expensive tasks. Marine SIM needs to take into account uncertainty in material properties, loading characteristics, fatigue models, detection capacities of inspection methods, etc. Optimising inspection and maintenance strategies under uncertainty is therefore vital for effective SIM and cost reductions. This paper proposes a value of information (VoI) computation and Bayesian decision optimisation (BDO) approach to optimal maintenance planning of typical fatigue-prone structural systems under uncertainty. It is shown that the approach can yield optimal maintenance strategies reliably in various maintenance decision making problems or contexts, which are characterized by different cost ratios. It is also shown that there are decision making contexts where inspection information doesn’t add value, and condition based maintenance (CBM) is not cost-effective. The CBM strategy is optimal only in the decision making contexts where VoI > 0. The proposed approach overcomes the limitation of CBM strategy and highlights the importance of VoI computation (to confirm VoI > 0) before adopting inspections and CBM.
Funding Details: European Commission Horizon 2020
Type of material: Journal Article
Publisher: SAGE
Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Copyright (published version): 2021 SAGE
Keywords: Value of informationBayesian decision optimizationMaintenance optimizationRisk analysisLife cycle assessment
DOI: 10.1177/1748006X20978127
Language: en
Status of Item: Peer reviewed
ISSN: 1748-006X
This item is made available under a Creative Commons License:
Appears in Collections:Critical Infrastructure Group Research Collection
Earth Institute Research Collection
Civil Engineering Research Collection
TRUSS-ITN Research Collection

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