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System availability assessment using a parametric Bayesian approach: a case study of balling drums
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0002-7458-6820
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. 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 (engelsk)Inngår i: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 10, nr 4, s. 739-745Artikkel i tidsskrift (Fagfellevurdert) Published
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).

sted, utgiver, år, opplag, sider
Springer, 2019. Vol. 10, nr 4, s. 739-745
Emneord [en]
Asset management, System availability, Reliability, Maintainability, Bayesian statistics, Markov Chain Monte Carlo (MCMC), Mining industry
HSV kategori
Forskningsprogram
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-75363DOI: 10.1007/s13198-019-00803-yISI: 000489742800023Scopus ID: 2-s2.0-85068994430OAI: oai:DiVA.org:ltu-75363DiVA, id: diva2:1338944
Merknad

Validerad;2019;Nivå 2;2019-10-29 (johcin)

Tilgjengelig fra: 2019-07-25 Laget: 2019-07-25 Sist oppdatert: 2019-10-29bibliografisk kontrollert

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Saari, EsiLin, JingZhang, Liangwei

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