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Smart Machinery Monitoring System with Reduced Information Transmission and Fault Prediction Methods Using Industrial Internet of Things

Author

Listed:
  • Ming-Fong Tsai

    (Department of Electronic Engineering, National United University, Miaoli 360, Taiwan)

  • Yen-Ching Chu

    (Department Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan)

  • Min-Hao Li

    (Department of Electronic Engineering, National United University, Miaoli 360, Taiwan)

  • Lien-Wu Chen

    (Department Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan)

Abstract

A monitoring system for smart machinery has been considered to be one of the most important goals in recent enterprises. This monitoring system will encounter huge difficulties, such as more data uploaded by smart machines, and the available internet bandwidth will influence the transmission speed of data and the reliability of the equipment monitoring platform. This paper proposes reducing the periodical information that has been uploaded to the monitoring platform by setting an upload event through the traits of production data from machines. The proposed methods reduce bandwidth and power consumption. The monitoring information is reconstructed by the proposed methods, so history data will not reduce storage in the cloud server database. In order to reduce the halt time caused by machine error, the proposed system uses machine-learning technology to model the operating status of machinery for fault prediction. In the experimental results, the smart machinery monitoring system using the Industrial Internet of Things reduces the volume of information uploaded by 54.57% and obtains a 98% prediction accuracy.

Suggested Citation

  • Ming-Fong Tsai & Yen-Ching Chu & Min-Hao Li & Lien-Wu Chen, 2020. "Smart Machinery Monitoring System with Reduced Information Transmission and Fault Prediction Methods Using Industrial Internet of Things," Mathematics, MDPI, vol. 9(1), pages 1-14, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2020:i:1:p:3-:d:466185
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    References listed on IDEAS

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    1. Dong-Hoon Kim & Eun-Kyu Lee & Naik Bakht Sania Qureshi, 2020. "Peak-Load Forecasting for Small Industries: A Machine Learning Approach," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
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