Critical ranking of steam handling unit using integrated cloud model and extended PROMETHEE for maintenance purpose
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ORIGINAL ARTICLE
Critical ranking of steam handling unit using integrated cloud model and extended PROMETHEE for maintenance purpose Nitin Panwar1
· Sanjeev Kumar1
Received: 22 June 2020 / Accepted: 26 September 2020 © The Author(s) 2020
Abstract To determine the critical component in an industry is one of the most important tasks performed by maintenance personnel to choose the best maintenance policy. Therefore, the purpose of the current paper is to develop a methodology based on integrated cloud model and extended preference ranking organization method for enrichment evaluation (PROMETHEE) method for finding the most critical component of the framework by ranking the failure causes of the system from multiple decision maker perspective. For this purpose, ranking of failure causes is performed by taking into account five factors namely chances of occurrence of failure (F 0 ), non-detection probability (N d ), downtime duration (Dd ), spare part criticality (S pc ) and safety risk (S r ). In this paper, first the primary and secondary weight of decision makers are calculated based on the uncertainty degree and divergence degree, respectively, to determine overall weight using cloud model theory by converting the uncertain linguistic evaluation matrix into interval cloud matrix, and then ranking of the steam handling subunit of paper making unit in a paper mill using extended PROMETHEE. The effectiveness of the proposed methodology is explained by considering steam handling subunit of paper making unit to find the critical component. Keywords Cloud model · Linguistic · PROMETHEE · Failure mode effect analysis · Multi-criteria decision making (MCDM)
Introduction In today’s world as the competition is increasing day by day, it is necessary for industries to meet the order regularly and to supply quality items as with time, disintegration and break down of framework occurs which result in sudden breakdown of the framework and safety risks. To evade such circumstances, it is must to adopt an appropriate maintenance policy which is imperative with a particular conclusion to repair/reinstate the disintegrated framework before total failure. Kaplan and Garrick [1] measured the risk in terms of probability of disappointment event, in this way firms perform maintenance procedures as a strategy for directing the risks. Grievink et al. [2] detailed that within start-up phase, servicing expenses are expected to be 2–6% of investment expenses and Bevilacqua and Braglia [3] stated due to need
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Literature review Nitin Panwar [email protected] Sanjeev Kumar [email protected]
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of maintenance exercises, its cost may expend up to 70% of total manufacturing expenses based upon the industry, above 15% for heavy process industries [4]. Also doing corrective maintenance will diminish the expenditure cost, but may result in failure of the machine which ultimately results in excessive loss to the firm [5]. It causes according to Moore and Starr [6] inherent misfortunes to the organization due to unsatisfied clients and Za
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