Multi-Criteria Decision-Making-Based Critical Component Identification And Prioritization For Predictive Maintenance

Predictive maintenance is currently taking on new relevance. However, recent developments in predictive maintenance focus on selecting the most appropriate algorithm based on the characteristics of a system and data given the critical components. Identifying the critical component has not been difficult because most predictive maintenance has been applied to well-known critical components. However, as the cost of installation for predictive maintenance lowers, it may be desirable to apply predictive maintenance to machines where critical components have not yet been identified, especially machines in small- and medium-sized enterprises (SMEs). In this paper, an identification method for critical components for which predictive maintenance is appropriate is proposed using multi-criteria decision making for application to multi-component, complex machines. This paper proposes a decision-making process considering three different criteria: severity, occurrence, and detectability. The goal is to identify and prioritize critical components for predictive maintenance. The technique for order performance by similarity to the ideal solution (TOPSIS) can take into account decision makers' preferences. Sensitivity analysis is investigated and discussed. The proposed decision-making approach allows a manufacturer to develop a customized introduction process for predictive maintenance.