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

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

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 fo...

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Bibliographic Details
Main Authors: Seung-Hwan Choi, Dawn An, Inho Lee, Suwoong Lee
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Mathematics
Subjects:
anomaly detection
graph convolutional network (GCN)
variational autoencoder (VAE)
statistical feature
probability-based anomaly score
Online Access:https://www.mdpi.com/2227-7390/12/23/3750
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