Failures detection and cascading analysis of manufacturing services collaboration toward industrial internet platforms

被引:20
作者
Li, Pei [1 ]
Cheng, Ying [1 ]
Tao, Fei [1 ]
机构
[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
基金
中国国家自然科学基金;
关键词
Manufacturing service; Manufacturing services collaboration network (MSC-Net); Propagation characteristics; Control strategy; Cascading failure; Industrial internet platform; MITIGATION STRATEGY; FAULT-DIAGNOSIS; NETWORK; PROPAGATION; MODEL; RISK; VULNERABILITY; ROBUSTNESS; SYSTEMS;
D O I
10.1016/j.jmsy.2020.08.012
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Industrial Internet or industrial cloud platforms based manufacturing collaboration in the form of services, namely manufacturing services collaboration (MSC), is being the new generation of networked manufacturing mode. In the process of MSC, a lot of failures would occur due to different reasons, and then propagate to cause other parts of failures. However, the classification of failures in MSC is unclear, the occurrence and propagation of failures are difficult to detect, and the control strategies for the failures and their corresponding propagation is insufficient, which lead to the lower users' collaboration participation in the platforms. In order to solve the above problems, the failures detection, failures cascading propagation analysis and specific control strategies for the platform-based MSC are investigated in this study. Firstly, the model of manufacturing services collaboration network (MSC-Net) is established by introducing the collaborative relations aware community of complex network. Then six types of failures occurred in MSC-Net are defined, corresponding detection methods for the defined failures are presented. The specific cascading propagation characteristics of different failures are revealed. The control strategies for different failures as well as their cascading propagation in MSC-Net are proposed. Finally, a 3D Printing case is given to illustrate the feasibility of the proposed methods.
引用
收藏
页码:169 / 181
页数:13
相关论文
共 48 条
[1]   Impacts of Wind Power Uncertainty on Grid Vulnerability to Cascading Overload Failures [J].
Athari, Mir Hadi ;
Wang, Zhifang .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2018, 9 (01) :128-137
[2]   Robustness of network controllability in cascading failure [J].
Chen, Shi-Ming ;
Xu, Yun-Fei ;
Nie, Sen .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2017, 471 :536-539
[3]   Risk Analysis of Coupling Fault Propagation Based on Meta-Action for Computerized Numerical Control (CNC) Machine Tool [J].
Chen, Yifan ;
Zhang, Genbao ;
Ran, Yan .
COMPLEXITY, 2019, 2019
[4]   Modeling and analyzing of an enterprise relationship network in the context of social manufacturing [J].
Ding, Kai ;
Jiang, Pingyu ;
Leng, Jiewu ;
Cao, Wei .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2016, 230 (04) :752-769
[5]   Analysis of the cascading failure for scale-free networks based on a multi-strategy evolutionary game [J].
Dui, Hongyan ;
Meng, Xueyu ;
Xiao, Hui ;
Guo, Jianjun .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2020, 199
[6]   Optimal supply chain resilience with consideration of failure propagation and repair logistics [J].
Goldbeck, Nils ;
Angeloudis, Panagiotis ;
Ochieng, Washington .
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2020, 133
[7]   Cascading failure and recovery of spatially interdependent networks [J].
Hong, Sheng ;
Zhu, Juxing ;
Braunstein, Lidia A. ;
Zhao, Tingdi ;
You, Qiuju .
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2017,
[8]   Cascading failure analysis and restoration strategy in an interdependent network [J].
Hong, Sheng ;
Lv, Chuan ;
Zhao, Tingdi ;
Wang, Baoqing ;
Wang, Jianghui ;
Zhu, Juxing .
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2016, 49 (19)
[9]   Big data analytics based fault prediction for shop floor scheduling [J].
Ji, Wei ;
Wang, Lihui .
JOURNAL OF MANUFACTURING SYSTEMS, 2017, 43 :187-194
[10]   Fault Detection and Diagnosis Using Self-Attentive Convolutional Neural Networks for Variable-Length Sensor Data in Semiconductor Manufacturing [J].
Kim, Eunji ;
Cho, Sungzoon ;
Lee, Byeongeon ;
Cho, Myoungsu .
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, 2019, 32 (03) :302-309