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12 January 2023 Anomaly detection method of segment-powered linear induction motor based on AE-GAN
J. J. Huang, X. H. Xu, X. P. Cui, H. X. Yang
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Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125090P (2023) https://doi.org/10.1117/12.2655838
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China
Abstract
Segment-powered linear motors (SLM) have high reliability, unbalanced operating data samples and few abnormal samples, making it difficult to detect anomalies through supervised classification models. Therefore, we propose an anomaly detection method based on the reconstruction model, which uses the difference between small reconstruction error of normal samples and large reconstruction error of abnormal samples to detect anomalies. Meanwhile, in order to make the reconstruction model better reconstruct the input samples, this paper introduces adversarial regularization to improve the training effect. The proposed model uses normal samples for training, and unknown state samples for testing. The anomaly score is obtained through the reconstruction error between the input sample and the reconstructed sample, so as to judge whether the input sample is abnormal. Experiments are carried out on the actual operation data of the SLM, which proves the effectiveness of the proposed method in the anomaly detection task of the SLM.
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J. J. Huang, X. H. Xu, X. P. Cui, and H. X. Yang "Anomaly detection method of segment-powered linear induction motor based on AE-GAN", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125090P (12 January 2023); https://doi.org/10.1117/12.2655838
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KEYWORDS
Detection and tracking algorithms
Artificial intelligence
Artificial neural networks
Defect detection
Electrical system failure
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