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

Selecting appropriate condition monitoring equipment for rotating machinery is essential for enhancing operational reliability and efficiency. This decision-making challenge is often approached using Multi-Criteria Decision-Making (MCDM) methods. In this study, we propose a novel fuzzy extension of the Combinative Distance-Based Assessment (CODAS) method, which uniquely integrates linguistic variables with trapezoidal fuzzy numbers to manage the uncertainties inherent in the decision-making process. Unlike existing approaches, our method provides a more comprehensive evaluation by effectively handling imprecise information, thereby offering a robust solution for selecting condition monitoring equipment under uncertainty. The proposed fuzzy CODAS method was validated through a case study and compared with two other fuzzy MCDM methods: Fuzzy Analytic Hierarchy Process (AHP) and fuzzy VIKOR. Additionally, a sensitivity analysis was conducted using ten different sets of criteria weights to test the robustness of the results. The analysis demonstrated that the proposed fuzzy CODAS method consistently delivers reliable outcomes, confirming its effectiveness and providing significant benefits to the research community and industrial applications.