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dc.contributor.authorLeonardo, Leoni
dc.contributor.authorAhmad, Bahoo Toroody
dc.contributor.authorFilippo, De Carlo
dc.contributor.authorPaltrinieri, Nicola
dc.date.accessioned2019-01-30T14:13:08Z
dc.date.available2019-01-30T14:13:08Z
dc.date.created2018-11-06T14:07:03Z
dc.date.issued2018
dc.identifier.issn0950-4230
dc.identifier.urihttp://hdl.handle.net/11250/2583175
dc.description.abstractDuring the last decades, the vital role of maintenance activities in industries including natural gas distribution system has cleared up progressively. High costs may induce to reduced maintenance and, in turn, lead to a lower availability and high risk of undesired events. Therefore, a probabilistic model, based on an acceptable level of risk, is required to avoid under and over estimation of maintenance time interval. This paper presents an advanced Risk-based Maintenance (RBM) methodology to optimize maintenance time schedule. Bayesian Network (BN) is applied to model the risk and the associated uncertainty. The developed method can assist the asset managers to work out the exact maintenance time for each component according to the risk level. To demonstrate and discuss the applicability of the methodology, a case study of Natural Gas Reduction and Measuring Station in Italy is considered. Results prove that the most critical components are the calculator and pilots, while the most reliable one is the odorization. Furthermore, the pressure and temperature gauge (PTG), the remote control system (RCS) and the meter are predicted as the components that require less time to transit from minor risk to catastrophic risk.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.titleDeveloping a risk-based maintenance model for a Natural Gas Regulating and Metering Station using Bayesian Networknb_NO
dc.title.alternativeDeveloping a risk-based maintenance model for a Natural Gas Regulating and Metering Station using Bayesian Networknb_NO
dc.typeJournal articlenb_NO
dc.description.versionsubmittedVersionnb_NO
dc.source.pagenumber17-24nb_NO
dc.source.volume57nb_NO
dc.source.journalJournal of Loss Prevention in the Process Industriesnb_NO
dc.identifier.doi10.1016/j.jlp.2018.11.003
dc.identifier.cristin1627554
dc.description.localcodeThis is a submitted manuscript of an article published by Elsevier Ltd in Journal of Loss Prevention in the Process Industries, 3 November 2018.nb_NO
cristin.unitcode194,64,92,0
cristin.unitnameInstitutt for maskinteknikk og produksjon
cristin.ispublishedfalse
cristin.fulltextpreprint
cristin.qualitycode1


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