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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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Product Design for Six Sigma (DFSS) approach is a structural and disciplined methodology driven by critical to quality characteristics (CTQs). How to identify and decompose the CTQs is the kernel part in the DFSS process. Traditional method only depends on the quality function deployment (QFD) matrix to flow down CTQs roughly. The paper puts forward a novel technical approach for CTQs decomposition from customer requirements into critical technical parameters based on the relational tree. Specifically, this approach emphasizes the systematic process and quantitative computation on quality relation weight. In order to specify the object of product DFSS, the connotation and evolution model of CTQs are created first. Then along the product development process, a decomposition measure for relational tree of CTQs is studied based on the functional and physical trees in Axiomatic Design (AD). And the quality relation weight computation of its nodes by means of Rough Set and fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is explored. Finally, an application on a car body noise vibration harshness (NVH) improvement, as an example, is given, and the decomposition process of NVH related with the functional and physical trees as well as its node weights computation algorithm are expounded in detail.