Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Detects abnormal behavior using motor power consumption

  • Kim, KiHwan (Dept. of Computer Science, Dongseo University) ;
  • Ryu, Su-Mi (Dept. of Computer Science, Dongseo University) ;
  • Kim, Min-Kyu (Dept. of Computer Science, Dongseo University) ;
  • Kang, Young-Jin (Dept. of Ubiquitous IT, Dongseo University) ;
  • Kim, HyunHo (Dept. of Ubiquitous IT, Dongseo University) ;
  • Lee, HoonJae (Dept of Information and Communication Engineering, Dongseo University) ;
  • Lee, Jin-Heung (Daun Information & Communication Co)
  • Received : 2018.10.01
  • Accepted : 2018.10.22
  • Published : 2018.10.31
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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.

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References

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