Anomaly Detection for Multivariate Industrial Sensor Data via Decoupled Generative Adversarial Network

Chien Wei-Chin, Wang Sheng-De

2023

Abstract

Industrial control systems often contain sensor and actuator devices, which provide monitoring data in the form of time series, such as bridge vibrations, water distribution systems, and human physiological data. This paper proposes an anomaly detection model based on autoencoders that can consider time-series relations of the data. Moreover, the quality of the decoder output is further improved by adding a residual produced by an extra generator and discriminator. The proposed autoencoder-GAN model and detection algorithm not only improved the performance but also made the training process of GAN easier. The proposed deep learning model with the anomaly detection algorithm has been shown to achieve better results on the SWaT, BATADAL, and Rare Event Classification datasets over existing methods.

Paper Citation

in Harvard Style

Wei-Chin C. and Sheng-De W. (2023). Anomaly Detection for Multivariate Industrial Sensor Data via Decoupled Generative Adversarial Network. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 1028-1035. DOI: 10.5220/0011894100003393

in Bibtex Style

@conference{icaart23,
author={Chien Wei-Chin and Wang Sheng-De},
title={Anomaly Detection for Multivariate Industrial Sensor Data via Decoupled Generative Adversarial Network},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={1028-1035},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011894100003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Anomaly Detection for Multivariate Industrial Sensor Data via Decoupled Generative Adversarial Network
SN - 978-989-758-623-1
AU - Wei-Chin C.
AU - Sheng-De W.
PY - 2023
SP - 1028
EP - 1035
DO - 10.5220/0011894100003393