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作 者:任颖莹 REN Yingying(State Key Laboratory of Shield Machine and Boring Technology,Zhengzhou,Henan 450001,China)
机构地区:[1]盾构及掘进技术国家重点实验室,河南郑州450001
出 处:《施工技术(中英文)》2023年第5期83-89,共7页Construction Technology
基 金:国家重点研发计划(2020YFB1712105);郑州市重大科技创新专项(2020CXZX0045)。
摘 要:针对隧道掘进机管理模式存在配件资源浪费、供应效率不足等问题,结合云平台、物联网技术研究隧道掘进机在运行中的维修资源保障和调配问题,分析设备维修业务需求和功能需求,集成融合装备实时健康状态、维修资源情况和供应商供应能力等信息,提出合适的价值网协同资源调配与供需模式,构建基于云平台的一体化维修资源协同优化调度决策与保障体系,包括装备综合健康评估、备件库存决策、维修资源调度决策、供应链决策等。最后简要介绍该模式下需要的健康评估、优化决策等关键技术。该体系结合装备状态与备件需求预测,采用维修前先预测备件消耗量的策略,实现项目间的联合决策和动态反馈功能,能有效合理地供应维修资源。Aiming at the problems of resource waste and insufficient supply efficiency in the current management mode of tunnel boring machine,combined with the current cloud platform and Internet of things technology,this paper studies the maintenance resource guarantee and allocation of TBM in operation,analyzes the business and functional requirements of equipment maintenance,and puts the equipment in real⁃time health status integration and integration of information such as maintenance resources and supplier supply capacity,put forward appropriate value network collaborative resource allocation and supply and demand mode,also build an integrated maintenance resources collaborative optimization scheduling decision⁃making and support system based on cloud platform,including equipment comprehensive health assessment,spare parts inventory decision⁃making,maintenance resource scheduling decision⁃making,supply chain decision⁃making,etc.Finally,the key technologies such as health assessment and optimization decision⁃making are briefly introduced.The system combines equipment status with spare parts demand prediction,adopts the strategy of predicting spare parts consumption before maintenance,realizes joint decision⁃making and dynamic feedback among projects,which can effectively and reasonably supply maintenance resources in time.
分 类 号:TU17[建筑科学—建筑理论] U455.3[建筑科学—桥梁与隧道工程]
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