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Knowledge-based power monitoring and fault prediction system for smart factories

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Abstract

With the recent spread of the 4th Industrial Revolution, the intellectualization of industry is progressing rapidly. In particular, companies in various field are interested in converting existing factories into smart factory, and the number of cases where the smart factory template is applied is increasing. In this paper, we design and implement an IoT-based power monitoring and data collection system that enables monitoring of power consumption as well as the detection of abnormal power consumption in a smart factory. The system consists of power measurement devices, data analysis servers, and knowledge-based web and smartphone applications. The power measurement device uses IoT sensors to measure power consumption and sends collected data to the server. The server analyzes the data collected from the device using R and exploits the analysis results to provide predictions about the failure of equipment and facilities in the smart factory. From this point of view, we can expect improvement in not only cost-efficiency but also product quality.

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Funding

This research was financially supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03032777), and this work was supported by the Soonchunhyang University Research Fund.

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Authors and Affiliations

  1. MSC Education Department, Kyungnam University, Changwon, Korea

    Eun Kim

  2. Electrical Engineering Department, Moonsung University, Changwon, Korea

    Duck-Haing Huh

  3. Department of Computer Software Engineering, Soonchunhyang University, Asan, Korea

    Seokhoon Kim

Authors
  1. Eun Kim
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  2. Duck-Haing Huh
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  3. Seokhoon Kim
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Correspondence to Seokhoon Kim.

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Kim, E., Huh, DH. & Kim, S. Knowledge-based power monitoring and fault prediction system for smart factories. Pers Ubiquit Comput 26, 307–318 (2022). https://doi.org/10.1007/s00779-019-01348-4

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  • DOI: https://doi.org/10.1007/s00779-019-01348-4

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