Selection Criteria for Evaluating Predictive Maintenance Techniques for Rotating Machinery using the Analytic Hierarchical Process (AHP)

Abstract

A primary industry objective is to ensure that machinery remains operational through effective management. Predictive maintenance plays a significant role in monitoring the working condition of machinery. The goal of this study is to establish the criteria for evaluating predictive maintenance techniques for rotating machines utilizing the Analytic Hierarchy Process (AHP). To achieve this target, survey data were collected from questionnaires and interviews with 20 experts, which had at least 20 years of professional experience. The Analytic Hierarchy Process (AHP) was utilized for criteria selection. The findings disclosed that predictive maintenance factors for rotating machines were ranked as follows: vibration analysis (45.5%), acoustic analysis (22.7%), oil analysis (22.4%), infrared thermography (5.8%), and wear particle analysis (3.6%). The Consistency Ratio (CR) was determined to be less than 10%, indicating a high level of consensus among the experts. Given the elevated importance attributed to vibration analysis, it can be concluded that the latter is the primary criterion for selecting predictive maintenance techniques for rotating machines.