复杂产品智能设计与主动运维“双馈式”制造服务方法体系

“Doubly-fed”Manufacturing Service of Intelligent Design and Preventive Maintenance for Complex Products

利用生命周期数据实现设计与运维协同是复杂产品开发和维护的主要模式,也是保证产品稳定、可靠运行的重要途径。在分析当前制造企业面临的挑战与现有制造服务模式研究不足的基础上,提出一种复杂产品智能设计与主动运维“双馈式”制造服务新模式,并设计相应的体系架构。提出并讨论上述新模式的关键技术体系,包括融合物联技术的制造与运维数据主动感知与增值计算、运维过程多源异构数据与知识工程融合的智能化设计、基于产品设计知识与运维过程感知的主动预防性维修等。通过上述技术的实施,可有效促进产品生命周期各阶段数据的交互共享和业务的协同优化,进而可构建一种生命周期数据泛在互联、设计前馈指导运维服务、运维反馈改进产品设计的闭环运作机制,以提升产品设计的智能化和产品运维的预见性。所提新模式和技术体系对生命周期数据驱动的智能制造研究和应用有重要借鉴价值。

Realizing the coordination of design and operation&maintenance(O&M)based on lifecycle data is the main mode of complex product development and O&M.It is also an important way to ensure the stable and reliable operation of products.Based on analysis of the challenges of manufacturing enterprise and the deficiencies of existing research on manufacturing service paradigms,a new pattern and architecture of“doubly-fed”manufacturing service of intelligent design and preventive maintenance for complex product is proposed and designed.Key technology system of the new pattern is put forward and elaborated,which included the proactive perceiving and value-adding calculation of manufacturing and O&M data by combining the IoT(Internet of Things)technologies,the intelligent design by fusion of multi-source heterogeneous O&M data and knowledge engineering,the preventive maintenance based on product design knowledge and O&M process data,etc.By applying these technologies,the interactive sharing of data and collaborative optimization of business among all lifecycle phases can be facilitated effectively.Furthermore,a closed-loop operation mechanism characterized by the ubiquitous interconnection of lifecycle data,the optimization of O&M service based on design knowledge and the improvement of product design by O&M data feedback are established.As a result,the intelligence of product design and the predictability of product O&M are enhanced.The proposed new pattern and technology system could provide important referential value to the research and application of lifecycle data driven smart manufacturing.