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工程（英文）
工程（英文）

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2095-8099

工程（英文）/Journal EngineeringCSTPCDCSCD北大核心SCI
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    An Intelligent Quality Control Method for Manufacturing Processes Based on a Human-Cyber-Physical Knowledge Graph

    Shilong WangJinhan YangBo YangDong Li...
    242-260页
    查看更多>>摘要：Quality management is a constant and significant concern in enterprises.Effective determination of cor-rect solutions for comprehensive problems helps avoid increased backtesting costs.This study proposes an intelligent quality control method for manufacturing processes based on a human-cyber-physical(HCP)knowledge graph,which is a systematic method that encompasses the following elements:data management and classification based on HCP ternary data,HCP ontology construction,knowledge extrac-tion for constructing an HCP knowledge graph,and comprehensive application of quality control based on HCP knowledge.The proposed method implements case retrieval,automatic analysis,and assisted decision making based on an HCP knowledge graph,enabling quality monitoring,inspection,diagnosis,and maintenance strategies for quality control.In practical applications,the proposed modular and hier-archical HCP ontology exhibits significant superiority in terms of shareability and reusability of the acquired knowledge.Moreover,the HCP knowledge graph deeply integrates the provided HCP data and effectively supports comprehensive decision making.The proposed method was implemented in cases involving an automotive production line and a gear manufacturing process,and the effectiveness of the method was verified by the application system deployed.Furthermore,the proposed method can be extended to other manufacturing process quality control tasks.
      原文链接:
    • 万方数据

    Digital Twins for Engineering Asset Management:Synthesis,Analytical Framework,and Future Directions

    Yongkui LiQinyue WangXiyu PanJian Zuo...
    261-275页
    查看更多>>摘要：Effective engineering asset management(EAM)is critical to economic development and improving liv-ability in society,but its complexity often impedes optimal asset functionalities.Digital twins(DTs)could revolutionize the EAM paradigm by bidirectionally linking the physical and digital worlds in real time.There is great industrial and academic interest in DTs for EAM.However,previous review studies have predominately focused on technical aspects using limited life-cycle perspectives,failing to holistically synthesize DTs for EAM from the managerial point of view.Based on a systematic literature review,we introduce an analytical framework for describing DTs for EAM,which encompasses three levels:DT 1.0 for technical EAM,DT 2.0 for technical-human EAM,and DT 3.0 for technical-environmental EAM.Using this framework,we identify what is known,what is unknown,and future directions at each level.DT 1.0 addresses issues of asset quality,progress,and cost management,generating technical value.It lacks multi-objective self-adaptive EAM,however,and suffers from high application cost.It is impera-tive to enable closed-loop EAM in order to provide various functional services with affordable DT 1.0.DT 2.0 accommodates issues of human-machine symbiosis,safety,and flexibility management,generating managerial value beyond the technical performance improvement of engineering assets.However,DT 2.0 currently lacks the automation and security of human-machine interactions and the managerial value related to humans is not prominent enough.Future research needs to align technical and managerial value with highly automated and secure DT 2.0.DT 3.0 covers issues of participatory governance,orga-nization management,sustainable development,and resilience enhancement,generating macro social value.Yet it suffers from organizational fragmentation and can only address limited social governance issues.Numerous research opportunities exist to coordinate different stakeholders.Similarly,future research opportunities exist to develop DT 3.0 in a more open and complex system.
      原文链接:
    • 万方数据
    • 维普

    Physics Guided Deep Learning-Based Model for Short-Term Origin-Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

    Shuxin ZhangJinlei ZhangLixing YangFeng Chen...
    276-296页
    查看更多>>摘要：Accurate origin-destination(OD)demand prediction is crucial for the efficient operation and manage-ment of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial-temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model inter-pretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand data-sets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.
      原文链接:
    • 万方数据