Novel Bayesian Approach to Assess System Availability using a Threshold to Censor DataShow others and affiliations
2019 (English)In: International Journal of Performability Engineering, ISSN 0973-1318, Vol. 15, no 5, p. 1314-1325Article in journal (Refereed) Published
Abstract [en]
Assessment of system availability has been studied from the design stage to the operational stage in various system configurations using either analytic or simulation techniques. However, the former cannot handle complicated state changes, and the latter is computationally expensive. This study proposes a Bayesian approach to evaluate system availability. In this approach: 1) Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) are treated as distributions instead of being "averaged" to better describe real scenarios and overcome the limitations of data sample size; 2) Markov Chain Monte Carlo (MCMC) simulations are applied to take advantage of the analytical and simulation methods; and 3) a threshold is set up for Time to Failure (TTR) data and Time to Repair (TTR) data, and new datasets with right-censored data are created to reveal the connections between technical and "Soft" KPIs. 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 by 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). By comparing the results with and without considering the threshold for censoring data, we show the threshold can be used as a monitoring line for continuous improvement in the investigated mining company.
Place, publisher, year, edition, pages
Totem Publisher, Inc. , 2019. Vol. 15, no 5, p. 1314-1325
Keywords [en]
system availability, Bayesian statistics, Gibbs sampling, Kaplan-Meier estimation, mining industry
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-75095DOI: 10.23940/ijpe.19.05.p7.13141325Scopus ID: 2-s2.0-85067024398OAI: oai:DiVA.org:ltu-75095DiVA, id: diva2:1332098
Note
Validerad;2019;Nivå 1;2019-06-27 (johcin)
2019-06-272019-06-272023-09-05Bibliographically approved