Utilizing Fuzzy CODAS for Optimal Selection of Condition Monitoring Equipment in Industrial Rotating Machinery

Choosing condition-monitoring equipment for rotating machinery is critical to improving operational reliability and efficiency. Researchers frequently treat this problematic decision-making process as a 'Multi-Criteria Decision-Making (MCDM)' problem. In this paper, we suggest a fuzzy extension of the 'Combinative Distance-Based Assessment (CODAS)' method to deal with the inherent uncertainties in decision-making. The CODAS approach can efficiently evaluate condition monitoring equipment by integrating linguistic factors and trapezoidal fuzzy numbers. In a case study, we use the suggested fuzzy CODAS approach to select condition-monitoring equipment for rotating machinery in unpredictable situations. To validate our findings, we compared the fuzzy CODAS technique with two other multi-criteria decision-making methods: fuzzy AHP (Analytic Hierarchy Process) and fuzzy VIKOR. Additionally, we conducted a sensitivity analysis to assess the robustness of the fuzzy CODAS results. They generate ten random criteria weights and apply each set to the case study. The comparisons and the sensitivity analysis show that the proposed fuzzy CODAS method gives consistent and reliable results.