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检索条件"主题词=Anomaly Detection of Copper Melting Process Data"

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anomaly detection of copper melting process data Based on anomaly Transformer 43

Anomaly Detection of Copper Melting Process Data Based on An...

引用

43rd Chinese Control Conference (CCC)

作者： Zhang, Junjia Ma, Jun Li, Xiang Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Intelligent Control & Applicat Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9798350366907;9789887581581

In this paper, aiming at the copper melting process, the relevant process variable parameters are coupled with each other, complicated and change with the production cycle, which leads to the anomaly detection of copper melting data. low accuracy of copper melting data, we propose an anomaly Transformer model based on the Transformer model, which replaces the original Self-Attention mechanism with anomaly-Attention mechanism, to realize accurate and reliable copper melting process data anomaly detection. In order to realize accurate and reliable anomaly detection of copper melting process data. Firstly, the degree of anomaly of copper melting process data at each time point is measured by the correlation dispersion, and the KL dispersion distance value is utilized to quantify the degree of anomaly. Distance value of KL dispersion to quantify the change of the value of each neighboring time point, and secondly, the above quantized distance value is transform the its attention mechanism into the anomaly attention mechanism form., in order to present the anomaly deviation and weight of each time point, and distinguish normal points from anomaly points, and finally, the trained anomaly Transformer model is analyzed on the test set for anomaly detection. Finally, the trained anomaly Transformer model is analyzed on the test set for anomaly detection, and the results are analyzed and interpreted using F1 evaluation indexes. The experimental results show that in the production process dataset of copper melting. The application of correlation-based discretization brings a certain 3.7% improvement in the F1 score. The addition of the anomalous attention module achieved 43.4% performance improvement, and the final combination of the combination of the combined KL dispersion and the anomaly attention module yielded an F1 score of 71.31, achieving a better anomaly detection result.

关键词： anomaly detection of copper melting process data KL dispersion distance anomaly-Attention Transformer

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anomaly detection of copper melting process data Based on anomaly Transformer

Anomaly Detection of Copper Melting Process Data Based on An...

引用

第43届中国控制会议

作者： Junjia Zhang Jun Ma Xiang Li Faculty of Information Engineering And Automation Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In this paper,aiming at the copper melting process,the relevant process variable parameters are coupled with each other,complicated and change with the production cycle,which leads to the anomaly detection of copper melting *** accuracy of copper melting data,we propose an anomaly Transformer model based on the Transformer model,which replaces the original Self-Attention mechanism with anomaly-Attention mechanism,to realize accurate and reliable copper melting process data anomaly *** order to realize accurate and reliable anomaly detection of copper melting process ***,the degree of anomaly of copper melting process data at each time point is measured by the correlation dispersion,and the KL dispersion distance value is utilized to quantify the degree of *** value of KL dispersion to quantify the change of the value of each neighboring time point,and secondly,the above quantized distance value is transform the its attention mechanism into the anomaly attention mechanism form.,in order to present the anomaly deviation and weight of each time point,and distinguish normal points from anomaly points,and finally,the trained anomaly Transformer model is analyzed on the test set for anomaly ***,the trained anomaly Transformer model is analyzed on the test set for anomaly detection,and the results are analyzed and interpreted using F1 evaluation *** experimental results show that in the production process dataset of copper *** application of correlation-based discretization brings a certain 3.7%improvement in the F1 *** addition of the anomalous attention module achieved 43.4% performance improvement,and the final combination of the combination of the combined KL dispersion and the anomaly attention module yielded an F1 score of 71.31,achieving a better anomaly detection result.

关键词： anomaly detection of copper melting process data KL dispersion distance anomaly-Attention Transformer

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