[Show abstract][Hide abstract] ABSTRACT: Self-optimizing mechatronic systems are a new class of technical systems. On the one hand, new challenges regarding dependability arise from their additional complexity and adaptivity. On the other hand, their abilities enable new concepts and methods to improve the dependability of mechatronic systems. This paper introduces a multi-level dependability concept for self-optimizing mechatronic systems and shows how planning can be used to improve the availability and reliability of systems in the operating stages.
Computational Intelligence in Control and Automation, 2009. CICA 2009. IEEE Symposium on; 05/2009
[Show abstract][Hide abstract] ABSTRACT: This paper focuses on the need of the large equipment manufacturing industry to adapt collaborative operation to transform the industry to cloud manufacturing services and to solve the new problem of federal resources coordination in complete service operation. We systematically study federal resources cooperation under cloud manufacturing mode to complete a large complex project. The primary research contents are divided into four points. First, a system structure of cloud manufacturing service mode is presented. Second, a synergy logic framework from the global system perspective is designed based on generalised partial global planning. Third, a multi-level system coordination mechanism is established by integrating various methods, including the bidding game mechanism for enterprise external resources, the planning control mechanisms for enterprise internal resource and the global collaborative optimisation mechanism for enterprise global federal resources. Finally, a cloud manufacturing service platform for a typical enterprise is developed by combining theory with practice. The results can realise collaborative management in resource selection and configuration, service process planning control and service information feedback in cloud manufacturing service, as well as achieve overall synergy effect for the system.
International Journal of Production Research 01/2014; 52(2). DOI:10.1080/00207543.2013.825383 · 1.48 Impact Factor
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