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
Published in:Mathematics (Basel) Vol. 12; no. 23; p. 3750
Main Authors: Choi, Seung-Hwan, An, Dawn, Lee, Inho, Lee, Suwoong
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2024
Subjects:
Anomalies
anomaly detection
Artificial intelligence
Artificial neural networks
Data acquisition
Datasets
Deep learning
Fatigue tests
Fault diagnosis
Feature extraction
graph convolutional network (GCN)
Industrial robots
Innovations
Machine learning
probability-based anomaly score
Robotics
Robotics industry
Robots
Semi-supervised learning
statistical feature
Statistical methods
System failures
Time series
variational autoencoder (VAE)
Vibration
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12233750