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With the advent of Industry 4.0, maintenance strategy faces new demands to avoid the hysteresis of the conventional passive maintenance mode and the non-feasibility of the periodic preventive maintenance model. In view of the inherent polymorphism of manufacturing systems and with the objective of maximizing benefits, a novel cost-oriented predictive maintenance based on mission reliability state for manufacturing systems is proposed. First, the cyber-physical system is adopted to organize and analyze big data in the operational process of manufacturing systems in terms of predictive analytics in cyber manufacturing environment. Second, a new connotation of mission reliability is defined based on the big operational data to comprehensively characterize the dynamic state of the equipment health states and the qualified degree of the production task. Third, the predictive maintenance mode based on mission reliability state is quantified by the comprehensive cost, and the relationship between mission reliability and cost is established. Thereafter, costoriented dynamic predictive maintenance strategy is proposed. Finally, a case study on the maintenance decision-making problem of a cylinder head manufacturing system is presented. The final result shows that the comprehensive cost can be further reduced by the proposed method relative to the traditional periodic preventive maintenance strategy.

Multi-state-oriented mission reliability modeling is the premise of intelligent scheduling and predictive maintenance for the multi-station manufacturing system. Previous studies on reliability modeling for manufacturing system could only provide a static reliability model based on the basic reliability of the components of manufacturing systems, which cannot support reliability-oriented production scheduling and preventive maintenance effectively. To resolve this dilemma, a multi-state-oriented mission reliability modeling for multi-station manufacturing system is proposed. First, the mapping relationship between the produced product reliability and mission reliability of the manufacturing system is proposed as the basis for modeling, and the connotation of mission reliability is elaborated by analyzing the polymorphisms of the multi-station manufacturing system. Second, a graphical representation to improve the state transparency named as Quality State Task Network is proposed based on production data by integrating the variability of task-demands propagation as well as the multi-state in material quality and machine performance. Third, the mission reliability modeling method based on the Quality State Task Network is proposed. Finally, a case study of cylinder-head manufacturing system has been applied to validate the proposed model.

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Accurate and dynamic reliability modeling for the running manufacturing system is the prerequisite to implement preventive maintenance. However, existing studies could not output the reliability value in real time because their abandonment of the quality inspection data originated in the operation process of manufacturing system. Therefore, this paper presents an approach to model the manufacturing system reliability dynamically based on their operation data of process quality and output data of product reliability. Firstly, on the basis of importance explanation of the quality variations in manufacturing process as the linkage for the manufacturing system reliability and product inherent reliability, the RQR chain which could represent the relationships between them is put forward, and the product qualified probability is proposed to quantify the impacts of quality variation in manufacturing process on the reliability of manufacturing system further. Secondly, the impact of qualified probability on the product inherent reliability is expounded, and the modeling approach of manufacturing system reliability based on the qualified probability is presented. Thirdly, the preventive maintenance optimization strategy for manufacturing system driven by the loss of manufacturing quality variation is proposed. Finally, the validity of the proposed approach is verified by the reliability analysis and optimization example of engine cover manufacturing system.