Detects abnormal behavior using motor power consumption (모터 전력 소비를 이용한 비정상적인 동작 감지)

Ki-Hwan Kim (김기환); Su-Mi Tyu (류수미); Min-Kyu KIm (김민규); Youngjin Kang (강영진); HyunHo Kim (김현호); Lee, HoonJae (이훈재); Jin-Heung Lee (이진흥)

doi:10.9708/jksci.2018.23.10.065

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.

KEYWORDS

Motor, AI, Prediction, Intrusion Detection, Smart Factory

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* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Detects abnormal behavior using motor power consumption

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Detects abnormal behavior using motor power consumption

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.