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Machine failures in manufacturing systems interrupt production operations and cause production loss. The conventional methods for failure prevention are to perform preventive maintenance before failure occurs. In these methods, a fixed maintenance threshold (FMT) is obtained using the lifetime distribution of each machine. This threshold can then be used to trigger maintenance work-orders. A problem with the conventional technique is that it does not consider the updated state of the system, which continues to change before and after maintenance. Therefore, unnecessarily high costs can be incurred due to unexpected equipment failure (lack of maintenance) or excessive maintenance. In this paper, a reliability-based dynamic maintenance threshold (DMT) is calculated based on the updated equipment status. The benefits of the DMT are demonstrated in a numerical case study on a drilling process. The results illustrate that the maintenance policy using the DMT can reduce unscheduled downtime, increase equipment availability, and utilize the equipment remaining useful life more effectively than a conventional FMT-based maintenance policy.

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This article proposes a component-level predictive maintenance scheduling framework in which system residual life prediction information is utilized. In the proposed framework, predictive maintenance scheduling is only triggered when a system is close to failure. A lifetime margin is used to infer that a system is close to failure but still safely operational when predictive maintenance scheduling is triggered. Some technical elements are detailed to facilitate the implementation of the proposed framework. A simulation study based on an example predictive maintenance scheduling framework (framework B) is provided to illustrate the implementation of the proposed framework, and compare framework B with the corresponding counterpart only consisting of predictive maintenance scheduling models (framework A). Given a relatively low noise level of system degradation processes, compared with the classical prognostics framework only consisting of predictive maintenance scheduling models, the proposed framework is similarly effective in failure prevention and more economic in that several premature preventive maintenance acts are saved.