Unsupervised anomaly detection method based on deep learning and support vector data description

Wei Ming Xu; Xue Min Li; Yi Zhang; Barasa Maulidi; Pei Ze Zhang; You Zhong Yi

doi:10.1117/12.2680413

31 May 2023 Unsupervised anomaly detection method based on deep learning and support vector data description

Wei Ming Xu, Xue Min Li, Yi Zhang, Barasa Maulidi, Pei Ze Zhang, You Zhong Yi

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 127042Z (2023) https://doi.org/10.1117/12.2680413
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

Anomaly detection in unlabelled and highly imbalanced high-dimensional monitoring data is one of the most urgent and challenging industry problems in the energy industry. Based on the powerful high-dimensional data analysis capabilities of autoencoders, the use of autoencoders for anomaly detection is becoming more and more popular. This paper proposes an anomaly detection method based on deep learning and support vector data description. First, feature engineering is built based on an optimized serial deep autoencoder; second, different feature combinations are studied and compared; finally, anomaly detection based on support vector data description. In this paper, experiments are carried out on the actual operating data of a real steam turbine to verify the effectiveness and accuracy of the proposed method.

Citation Download Citation

Wei Ming Xu, Xue Min Li, Yi Zhang, Barasa Maulidi, Pei Ze Zhang, and You Zhong Yi "Unsupervised anomaly detection method based on deep learning and support vector data description", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 127042Z (31 May 2023); https://doi.org/10.1117/12.2680413

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