Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder (Multi-Column AutoEncoder를 사용한 One-Class 분류 진동 이상 탐지 기법)

Sang-Min Kim (김상민); Jung-Mo Sohn (손정모)

doi:10.9708/jksci.2023.28.02.009

Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2023, 28(2), pp.9-17
DOI : 10.9708/jksci.2023.28.02.009
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : December 28, 2022
Accepted : January 31, 2023
Published : February 28, 2023

Sang-Min Kim ¹, Jung-Mo Sohn ¹

¹이포즌

Accredited

ABSTRACT

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

KEYWORDS

Vibration, Anomaly Detection, Deep Learning, AutoEncoder

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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

Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder

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* 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

Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder

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