A systematic-theoretic analysis of data-driven throughput bottleneck detection of production systems

被引:24
|
作者
Li, Lin [1 ]
机构
[1] Univ Illinois, Dept Mech & Ind Engn, Chicago, IL 60607 USA
基金
美国国家科学基金会;
关键词
Systematic-theoretic analysis; Data-driven; Throughput bottleneck detection; Production system; MANUFACTURING SYSTEMS; PRODUCTION LINES;
D O I
10.1016/j.jmsy.2018.03.001
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Throughput is one of the most critical performance indices for design, control, and operation management of production systems. Throughput bottleneck greatly impedes the overall performance of modern production systems. However, detecting throughput bottlenecks of production systems is a complicated task due to the complexity of production system dynamics. In this paper, a new data-driven bottleneck detection method is proposed based on rigorous mathematical proof for general serial production systems, which uses the routinely available industrial data on the plant floor to identify the throughput bottleneck location within production systems in both the short-term (transient) and long-term (steady-state) periods. Case studies are conducted to illustrate the effectiveness of the proposed method. The research outcomes will enhance the intelligent decision-making and real-time operation management capabilities in the modern manufacturing enterprises. (C) 2018 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:43 / 52
页数:10
相关论文
共 50 条
  • [21] A data-driven method for performance analysis and improvement in production systems with quality inspection
    Wang, Jun-Qiang
    Song, Yun-Lei
    Cui, Peng-Hao
    Li, Yang
    JOURNAL OF INTELLIGENT MANUFACTURING, 2023, 34 (02) : 455 - 469
  • [22] Enabling data-driven anomaly detection by design in cyber-physical production systems
    Rui Pinto
    Gil Gonçalves
    Jerker Delsing
    Eduardo Tovar
    Cybersecurity, 5
  • [23] Enabling data-driven anomaly detection by design in cyber-physical production systems
    Pinto, Rui
    Goncalves, Gil
    Delsing, Jerker
    Tovar, Eduardo
    CYBERSECURITY, 2022, 5 (01)
  • [24] Event-Triggered Data-Driven Predictive Control for Multirate Systems: Theoretic Analysis and Experimental Results
    Yang, Yi
    Shi, Dawei
    Yu, Hao
    Shi, Ling
    IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2024,
  • [25] Online Data-Driven Fault Detection for Robotic Systems
    Golombek, Raphael
    Wrede, Sebastian
    Hanheide, Marc
    Heckmann, Martin
    2011 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, 2011, : 3011 - 3016
  • [26] Data-Driven Method of Fault Detection in Technical Systems
    Zhirabok, Alexey
    Pavlov, Sergey
    25TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION, 2014, 2015, 100 : 242 - 248
  • [27] Data-Driven Incident Detection in Power Distribution Systems
    Aguiar, Nayara
    Gupta, Vijay
    Trevizan, Rodrigo D.
    Chalamala, Babu R.
    Byrne, Raymond H.
    2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2021,
  • [28] A Data-Driven Approach of Fault Detection for LTI Systems
    Chen Zhaoxu
    Fang Huajing
    2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 6174 - 6179
  • [29] Data-Driven Reachability Analysis for Nonlinear Systems
    Park, Hyunsang
    Vijay, Vishnu
    Hwang, Inseok
    IEEE Control Systems Letters, 2024, 8 : 2661 - 2666
  • [30] A Systematic Review of Data-Driven Attack Detection Trends in IoT
    Haque, Safwana
    El-Moussa, Fadi
    Komninos, Nikos
    Muttukrishnan, Rajarajan
    SENSORS, 2023, 23 (16)