- Title
- Validation framework of bayesian networks in asset management decision-making
- Creator
- Morey, Stephen; Chattopadhyay, Gopinath; Larkins, Jo-ann
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184716
- Identifier
- vital:16578
- Identifier
-
https://doi.org/10.1007/978-3-030-93639-6_31
- Identifier
- ISBN:2195-4356 (ISSN); 9783030936389 (ISBN)
- Abstract
- Capital-intensive industries are under increasing pressure from capital constraints to extend the life of long-life assets and to defer asset renewals. Assets in most of those industries have complex life-cycle management challenges in aspects of design, manufacture, maintenance and service contracts, the usage environment, and changes in support personnel over the asset life. A significant challenge is the availability and quality of relevant data for informed decision-making in assuring reliability, availability and safety. There is a need for better-informed maintenance decisions and cost-effective interventions in managing the risk and assuring performance of those assets. Bayesian networks have been considered in asset management applications in recent years for addressing these challenges, by modelling of reliability, maintenance decisions, life extension and prognostics, across a wide range of technological domains of complex assets. However, models of long-life assets are challenging to validate, particularly due to issues with data scarcity and quality. A literature review on Bayesian networks in asset management in this paper shows that there is a need for further work in this area. This paper discusses the issues and challenges of validation of Bayesian network models in asset management and draws on findings from literature research to propose a preliminary validation framework for Bayesian network models in life-cycle management applications of capital-intensive long-life assets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- International Congress and Workshop on Industrial AI, IAI 2021, Virtual online, 6-7 October 2021, published in Lecture Notes in Mechanical Engineering p. 360-369
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022, The Author(s)
- Rights
- Open Access
- Subject
- Asset management; Bayesian network; Life extension; Maintenance; Model validation; Reliability
- Full Text
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