Development of a Predictive Model for Maintenance Strategies of Automotive Parts Processing Equipment Based on Multi-Criteria Decision Analysis 2024-01-5081

With the increasing demand of human–machine interaction under a scenario of the novel Maintenance Strategy 5.0, it sparks off a growing requisition of reliable maintenance strategies to maintain operations in good order. In this study, a novel hierarchical maintenance strategy model based on multi-criteria decision analysis (MCDA) is proposed to pledge scientific maintenance. First, failure mode and effects analysis (FMEA) based on negative information and Deng entropy is introduced to assess the equipment maintenance requirement level. Subsequently, the improved average rank method is selected to fit the Weibull distribution function, which is able to better qualify the characteristics lifespan of target equipment. Moreover, hybrid effect with multi-criteria decision-making, in aspects of risk priority, expert assessment as well as human interference of failure are deduced, which highlights the scientific significance and credibility of the recommended maintenance levels and times. Finally, the feasibility of the predictive maintenance schedule is verified through gray correlation analysis (GRA). Overall, the proposed model takes into account the effects brought by failure modes, subjective uncertainty, and human interference on the maintenance strategy; it, therefore, provides a new insight on the assessment of the intertwined relationship between maintenance and reliability.