Abstract
The requirement for advanced manufacturing systems and the need to ensure system efficiency and economy have led to an increased emphasis on maintenance policies. However, conventional preventive maintenance (PM) neglects maintenance effects and environmental condition. Hence, a condition-based predictive maintenance (CBPM) policy for intelligent monitored system subjected to degradation is now presented. The aim is to simultaneously optimize two objectives: maximize equipment availability for efficiency and minimize maintenance cost for economy. The multiple attribute value theory is used to determine the optimal PM intervals in different maintenance cycles. Furthermore, a hybrid hazard rate recursion evolution incorporating two improvement factors and the environmental factor is developed. Both maintenance effects and environmental condition are integrated into maintenance scheduling. Finally, through a case study, the analysis shows that the application of CBPM policy results in a noticeable increase in the availability–benefits and the cost–benefits. It indicates that CBPM policy can lead to significant gains by considering maintenance effects and environmental condition.
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