developing a new fuzzy clustering method for equipment maintenance

Data can enhance equipment maintenance and asset management by providing predictive insights and minimizing downtime. implementing data gathering and predictive maintenance systems is essential for improving reliability and cost efficiency. however, addressing challenges such as high implementation costs, data integration issues, and the need for skilled personnel is crucial for maximizing their benefits. maintenance managers at a steel holding company in iran, as a case study aimed to implement predictive maintenance but faced high costs for full implementation. selecting a subset of equipment parts posed a complex decision-making problem, as eligibility needed to be based on maintenance criteria rather than traditional factors like price and location. to address this, we proposed a framework using machine learning to cluster equipment parts based on maintenance-related criteria. while clustering simplifies decision-making, it introduces uncertainty. to mitigate this, we represent each cluster with a trapezoidal fuzzy number. the silhouette method is employed to determine the optimal number of clusters, followed by the k-means++ method for clustering. our approach successfully grouped 201 equipment parts into seven clusters based on criteria such as importance, maintenance period, and daily working hours. fuzzy logic is used to interpret the clusters, reducing uncertainty and ensuring that no equipment is overlooked.