Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data

Seung-Hwan Choi, Dawn An, Inho Lee and Suwoong Lee ()
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Seung-Hwan Choi: Advanced Mechatronics Research Group, Daegyeong Division, Korea Institute of Industrial Technology, Daegu 42994, Republic of Korea
Dawn An: Advanced Mechatronics Research Group, Daegyeong Division, Korea Institute of Industrial Technology, Daegu 42994, Republic of Korea
Inho Lee: Department of Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea
Suwoong Lee: Advanced Mechatronics Research Group, Daegyeong Division, Korea Institute of Industrial Technology, Daegu 42994, Republic of Korea

Mathematics, 2024, vol. 12, issue 23, 1-16

Abstract: This paper proposes a deep learning-based anomaly detection method using time-series vibration and current data, which were obtained from endurance tests on driving modules applied in industrial robots and machine systems. Unlike traditional classification models that depend on labeled fault data for detection, acquiring sufficient fault data in real industrial environments is highly challenging due to various conditions and constraints. To address this issue, we employ a semi-supervised learning approach that relies solely on normal data to effectively detect abnormal patterns, overcoming the limitations of conventional methods. The performance of semi-supervised models was first validated using a statistical feature-based anomaly detection approach, from which the GCN-VAE model was adopted. By combining the spatial feature extraction capability of Graph Convolutional Networks (GCNs) with the latent temporal feature modeling of Variational Autoencoders (VAEs), our method can effectively detect abnormal signs in the data, particularly in the lead-up to system failures. The experimental results confirmed that the proposed GCN-VAE model outperformed existing hybrid deep learning models in terms of anomaly detection performance in the pre-failure section.

Keywords: anomaly detection; graph convolutional network (GCN); variational autoencoder (VAE); statistical feature; probability-based anomaly score (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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