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Authors: Chien Wei-Chin and Wang Sheng-De

Affiliation: Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

Keyword(s): Anomaly Detection, Time Series, Deep Learning, Autoencoder, Generative Adversarial Network.

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.

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 1028-1035. DOI: 10.5220/0011894100003393

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Wei-Chin, C.
AU - Sheng-De, W.
PY - 2023
SP - 1028
EP - 1035
DO - 10.5220/0011894100003393
PB - SciTePress

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