Anomaly detection based on graph convolutional network–variational autoencoder model using time-series vibration and current data

HIGHLIGHTS

• What: This paper proposes a deep method using and which were obtained from endurance tests driving modules applied in industrial robots and machine systems. By leveraging this statistical baseline, this study evaluates the performance of individual deep learning models and assesses the suitability of hybrid deep learning models. To overcome these limitations, this study proposes a semi-supervised GCN-VAE model for detecting anomalies in time-series vibration and current data collected from durability tests on driving modules. To address this limitation, this study proposes the GCN-VAE model, which integrates GCN`s ability to capture structural . . .
  

   

  

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