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Multi-Column AutoEncoder를 사용한 One-Class 분류 진동 이상 탐지 기법
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논문 기본 정보

Type
Academic journal
Author
Sang-Min Kim (Epozen’s research institute) Jung-Mo Sohn (Epozen’s research institute)
Journal
The Korean Society Of Computer And Information Journal of the Korea Society of Computer and Information Vol.28 No.2(Wn.227) KCI Accredited Journals
Published
2023.2
Pages
9 - 17 (9page)

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Multi-Column AutoEncoder를 사용한 One-Class 분류 진동 이상 탐지 기법
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Abstract· Keywords

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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.
#Vibration #Anomaly Detection #Deep Learning #AutoEncoder #진동 #이상 탐지 #딥러닝

Contents

Abstract
요약
Ⅰ. Introduction
Ⅱ. Preliminaries
Ⅲ. The Proposed Scheme
Ⅳ. Conclusions
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