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System availability assessment using a parametric Bayesian approach: a case study of balling drums
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-7458-6820
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China.ORCID iD: 0000-0001-7310-5717
Department of Management Science, University of Strathclyde, Glasgow, United Kingdom.
2019 (English)In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348Article in journal (Refereed) Epub ahead of print
Abstract [en]

Assessment of system availability usually uses either an analytical (e.g., Markov/semi-Markov) or a simulation approach (e.g., Monte Carlo simulation-based). However, the former cannot handle complicated state changes and the latter is computationally expensive. Traditional Bayesian approaches may solve these problems; however, because of their computational difficulties, they are not widely applied. The recent proliferation of Markov Chain Monte Carlo (MCMC) approaches have led to the use of the Bayesian inference in a wide variety of fields. This study proposes a new approach to system availability assessment: a parametric Bayesian approach using MCMC, an approach that takes advantages of the analytical and simulation methods. By using this approach, mean time to failure (MTTF) and mean time to repair (MTTR) are treated as distributions instead of being “averaged”, which better reflects reality and compensates for the limitations of simulation data sample size. To demonstrate the approach, the paper considers a case study of a balling drum system in a mining company. In this system, MTTF and MTTR are determined in a Bayesian Weibull model and a Bayesian lognormal model respectively. The results show that the proposed approach can integrate the analytical and simulation methods to assess system availability and could be applied to other technical problems in asset management (e.g., other industries, other systems)

Place, publisher, year, edition, pages
Springer, 2019.
Keywords [en]
Asset management, System availability, Reliability, Maintainability, Bayesian statistics, Markov Chain Monte Carlo (MCMC), Mining industry
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-75363DOI: 10.1007/s13198-019-00803-yOAI: oai:DiVA.org:ltu-75363DiVA, id: diva2:1338944
Available from: 2019-07-25 Created: 2019-07-25 Last updated: 2019-07-25

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Saari, EsiLin, JingZhang, Liangwei

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