Data-driven Modeling and coordination of large process structures

被引:0
|
作者
Mueller, Dominic [1 ,2 ]
Reichert, Manfred [1 ]
Herbst, Joachim [2 ]
机构
[1] Univ Twente, Informat Syst Grp, Enschede, Netherlands
[2] Daimler Chrysler AG, Grp Res & Adv Engn, Dept GR EPD, Stuttgart, Germany
关键词
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In the engineering domain, the development of complex products (e.g., cars) necessitates the coordination of thousands of (sub-) processes. One of the biggest challenges for process management systems is to support the modeling, monitoring and maintenance of the many interdependencies between these sub-processes. The resulting process structures are large and can be characterized by a strong relationship with the assembly of the product; i.e., the sub-processes to be coordinated can be related to the different product components. So far, sub-process coordination has been mainly accomplished manually, resulting in high efforts and inconsistencies. IT support is required to utilize the information about the product and its structure for deriving, coordinating and maintaining such data-driven process structures. In this paper, we introduce the COREPRO framework for the data-driven modeling of large process structures. The approach reduces modeling efforts significantly and provides mechanisms for maintaining data-driven process structures.
引用
收藏
页码:131 / +
页数:3
相关论文
共 50 条
  • [41] A priori assessment of nonlocal data-driven wall modeling in large eddy simulation
    Jamaat, Golsa Tabe
    Hattori, Yuji
    [J]. PHYSICS OF FLUIDS, 2023, 35 (05)
  • [42] DATA-DRIVEN RELIABILITY MODELING OF SMART MANUFACTURING SYSTEMS USING PROCESS MINING
    Friederich, Jonas
    Lazarova-Molnar, Sanja
    [J]. 2022 WINTER SIMULATION CONFERENCE (WSC), 2022, : 2534 - 2545
  • [43] A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process
    Xiaofeng Yuan
    Weiwei Xu
    Yalin Wang
    Chunhua Yang
    Weihua Gui
    [J]. IEEE/CAAJournalofAutomaticaSinica., 2024, 11 (08) - 1785
  • [44] A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process
    Yuan, Xiaofeng
    Xu, Weiwei
    Wang, Yalin
    Yang, Chunhua
    Gui, Weihua
    [J]. IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2024, 11 (08) : 1777 - 1785
  • [45] Knowledge and data-driven hybrid system for modeling fuzzy wastewater treatment process
    Cheng, Xuhong
    Guo, Zhiwei
    Shen, Yu
    Yu, Keping
    Gao, Xu
    [J]. NEURAL COMPUTING & APPLICATIONS, 2023, 35 (10): : 7185 - 7206
  • [46] Data-driven battery electrode production process modeling enabled by machine learning
    Tan, Changbai
    Ardanese, Raffaello
    Huemiller, Erik
    Cai, Wayne
    Yang, Houssen
    Bracey, Jennifer
    Pozzato, Gabriele
    [J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2023, 316
  • [47] Knowledge and data-driven hybrid system for modeling fuzzy wastewater treatment process
    Xuhong Cheng
    Zhiwei Guo
    Yu Shen
    Keping Yu
    Xu Gao
    [J]. Neural Computing and Applications, 2023, 35 : 7185 - 7206
  • [48] Data-Driven Modeling for PDF Shaping of Fiber Length Distribution in Refining Process
    Li, Mingjie
    Zhou, Ping
    [J]. 2018 IEEE 8TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (IEEE-CYBER), 2018, : 1467 - 1471
  • [49] Variational Bayesian probabilistic modeling framework for data-driven distributed process monitoring
    Jiang, Jiashi
    Jiang, Qingchao
    [J]. CONTROL ENGINEERING PRACTICE, 2021, 110
  • [50] Data-Driven Multitarget Online Modeling of the Municipal Solid Waste Incineration Process
    Yan, Aijun
    Hu, Kaicheng
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2024, : 14124 - 14133