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@article{ART002934273},
author={Sang-Min Kim and Jung-Mo Sohn},
title={Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={2},
pages={9-17},
doi={10.9708/jksci.2023.28.02.009}

TY - JOUR
AU - Sang-Min Kim
AU - Jung-Mo Sohn
TI - Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 2
PB - The Korean Society Of Computer And Information
SP - 9
EP - 17
SN - 1598-849X
AB - 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.
KW - Vibration;Anomaly Detection;Deep Learning;AutoEncoder
DO - 10.9708/jksci.2023.28.02.009
ER -

Sang-Min Kim and Jung-Mo Sohn. (2023). Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder. Journal of The Korea Society of Computer and Information, 28(2), 9-17.

Sang-Min Kim and Jung-Mo Sohn. 2023, "Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder", Journal of The Korea Society of Computer and Information, vol.28, no.2 pp.9-17. Available from: doi:10.9708/jksci.2023.28.02.009

Sang-Min Kim, Jung-Mo Sohn "Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder" Journal of The Korea Society of Computer and Information 28.2 pp.9-17 (2023) : 9.

Sang-Min Kim, Jung-Mo Sohn. Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder. 2023; 28(2), 9-17. Available from: doi:10.9708/jksci.2023.28.02.009

Sang-Min Kim and Jung-Mo Sohn. "Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder" Journal of The Korea Society of Computer and Information 28, no.2 (2023) : 9-17.doi: 10.9708/jksci.2023.28.02.009

Sang-Min Kim; Jung-Mo Sohn. Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder. Journal of The Korea Society of Computer and Information, 28(2), 9-17. doi: 10.9708/jksci.2023.28.02.009

Sang-Min Kim; Jung-Mo Sohn. Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder. Journal of The Korea Society of Computer and Information. 2023; 28(2) 9-17. doi: 10.9708/jksci.2023.28.02.009

Sang-Min Kim, Jung-Mo Sohn. Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder. 2023; 28(2), 9-17. Available from: doi:10.9708/jksci.2023.28.02.009

Sang-Min Kim and Jung-Mo Sohn. "Vibration Anomaly Detection of One-Class Classification using Multi-Column AutoEncoder" Journal of The Korea Society of Computer and Information 28, no.2 (2023) : 9-17.doi: 10.9708/jksci.2023.28.02.009