Method for Modeling Automotive Manufacturing Equipment Preventive Maintenance Derived by Multi-Criteria Decision and Synthesized Weibull Distribution

To avoid equipment failures in automotive manufacturing activities, particular attention is paid to the design of an effective preventive maintenance strategy model for automotive component processing equipment. The selection of appropriate maintenance intervals as well as the equilibrium between the benefits and costs should be the primary challenges in high-quality maintenance process. In this study, a reliable preventive maintenance strategy model is proposed and the aim is to suggest an appropriated approach for the selection of maintenance intervals from a comprehensive view of importance, hazard, and maintenance cost. First and foremost, a new Fermatean fuzzy entropy (FFE) measure method on the basis of analytic hierarchy process (AHP) is innovatively employed to access more objective weights of each indicator. Moreover, a more objective scoring of importance and hazard indicator is executed to aggregate the expert group judgments. Furthermore, this study emphasizes the introduction of a stable equipment reliability distribution, which is obtained using scientific regression on the basis of failure data. Thus, the maintenance cost of the equipment could be derived based on the equipment’s reliability. As a consequence, the prediction of the probability of failure occurring and preventive maintenance cycle are well validated. In conclusion, the preventive maintenance strategy established in the study not only reduces the inherent subjectivity in multi-criteria decision analysis, but also improves the accuracy of equipment failure probability prediction. Hence, it offers novel perspectives on optimized maintenance intervals and the balance between benefits and costs.