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Detects abnormal behavior using motor power consumption

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2018, 23(10), pp.65-72
  • DOI : 10.9708/jksci.2018.23.10.065
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 1, 2018
  • Accepted : October 22, 2018
  • Published : October 31, 2018

Ki-Hwan Kim 1 Su-Mi Tyu 2 Min-Kyu KIm 1 Youngjin Kang 1 HyunHo Kim 1 Lee, HoonJae 1 Jin-Heung Lee 3

1동서대학교
2
3다운정보통신(주)

Accredited

ABSTRACT

In this paper, we used LSTM as a method to detect abnormal behavior of motors. We fixed the high layout size to 1 and changed the range of the input values and the neural network structure to see what change in power consumption prediction. Now, as the fourth industrial revolution era, smart factories are attracting attention. All the physical actions of smart factories are done using motors. Continuous monitoring of motor malfunctions helps to detect malfunctions and efficient operation. However, it is difficult to acquire the power consumption constantly due to the influence of the noise. We have experimented with a simple experimental environment, a method of predicting similarity to input data by adjusting the range of the input data or by changing the neural network structure.

Citation status

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