IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i7p3816-d778280.html
   My bibliography  Save this article

Operation-Driven Power Analysis of Discrete Process in a Cyber-Physical System Based on a Modularized Factory

Author

Listed:
  • Jumyung Um

    (Department of Industrial & Management Systems Engineering, Kyung Hee University, Yongin 17104, Korea)

  • Taebyeong Park

    (Department of Industrial & Management Systems Engineering, Kyung Hee University, Yongin 17104, Korea
    Vision AI Business Team, LG CNS, Seoul 07795, Korea)

  • Hae-Won Cho

    (Department of Applied Systems, Hanyang University, Seoul 04763, Korea)

  • Seung-Jun Shin

    (School of Interdisciplinary Industrial Studies, Hanyang University, Seoul 04763, Korea)

Abstract

As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In previous studies, a machine, conducting a single continuous operation, has been mainly observed for power estimation. However, a modularized production line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine. During the information collection of such production lines, it is important to interpret and distinguish mixed signals from multiple machines to ensure that there is no reduction in the information quality due to noise and signal fusion and discrete events. A data pipeline from data collection from different sources to pre-processing, data conversion, synchronization, and deep learning classification to estimate the total power use of the future process plan is proposed herein. The pipeline also establishes an auto-labeled data set of individual operations that contributes to building power estimation models without manual data pre-processing. The proposed system is applied to a modular factory connected with machine controllers using standardized protocols individually and linked to a centralized power monitoring system. Specifically, a robot arm cell was investigated to evaluate the pipeline with the result of the power profile synchronized with the robot program.

Suggested Citation

  • Jumyung Um & Taebyeong Park & Hae-Won Cho & Seung-Jun Shin, 2022. "Operation-Driven Power Analysis of Discrete Process in a Cyber-Physical System Based on a Modularized Factory," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3816-:d:778280
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/7/3816/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/7/3816/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jumyung Um & Ian Anthony Stroud & Yong-keun Park, 2019. "Deep Learning Approach of Energy Estimation Model of Remote Laser Welding," Energies, MDPI, vol. 12(9), pages 1-19, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Konstantinos Salonitis, 2020. "Energy Efficiency of Manufacturing Processes and Systems—An Introduction," Energies, MDPI, vol. 13(11), pages 1-5, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3816-:d:778280. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.