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<p>Reliability centered maintenance (RCM) is a methodology for maintenance optimization developed within the aviation industry and adapted to other industries such as Oil &amp; Gas refining. One of the biggest problems is the lack of quantitative data during criticality analysis and therefore a suitable method is needed to process verbal expressions and the uncertainties encountered. This paper presents a novel application of Fuzzy Inference Systems (FIS) based on Picture Fuzzy Sets (PFS) for determining the criticality of refinery assets to utilize in RCM methodology. Unlike traditional methods, this research uses the Mamdani approach in Picture Fuzzy Inference Systems (PFIS) to best address the uncertainties and lack of quantitative values inherent in the analysis process. The unique application of PFS, rarely combined with fuzzy inference systems, offers distinct advantages in dealing with imprecision and ambiguity in maintenance data. The results of PFIS are compared with those of the matrix method, demonstrating the effectiveness of the proposed approach in refining the decision-making process of criticality determination and maintenance prioritization in refinery operations.</p>

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Criticality Analysis of Refinery Assets Using Picture Fuzzy Inference System in Reliability Centered Maintenance

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  • 出版模式：

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  • 发表日期：

    2025年7月12日

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  • Cagri Bahadir,
  • Cengiz Kahraman

摘要

Reliability centered maintenance (RCM) is a methodology for maintenance optimization developed within the aviation industry and adapted to other industries such as Oil & Gas refining. One of the biggest problems is the lack of quantitative data during criticality analysis and therefore a suitable method is needed to process verbal expressions and the uncertainties encountered. This paper presents a novel application of Fuzzy Inference Systems (FIS) based on Picture Fuzzy Sets (PFS) for determining the criticality of refinery assets to utilize in RCM methodology. Unlike traditional methods, this research uses the Mamdani approach in Picture Fuzzy Inference Systems (PFIS) to best address the uncertainties and lack of quantitative values inherent in the analysis process. The unique application of PFS, rarely combined with fuzzy inference systems, offers distinct advantages in dealing with imprecision and ambiguity in maintenance data. The results of PFIS are compared with those of the matrix method, demonstrating the effectiveness of the proposed approach in refining the decision-making process of criticality determination and maintenance prioritization in refinery operations.