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- Title
基于经验耦合函数的动态运行参数异常检测.
- Authors
宋柯; 钱唐江; 武彬; 陈勇旭; 钟婷; 周帆
- Abstract
As a result of the development of industrial intelligence, intelligent control systems have been equipped in industrial production systems, and one of the important requirements is intelligent anomaly detection. However, achieving intelligent anomaly detection is often difficult due to the streaming data format of dynamic runtime parameters and the coupling of high-dimensional data, which make it challenging to perform reliable and efficient anomaly detection. To this end, this paper proposes an anomaly detection method based on joint distribution. Firstly, the dynamic operating data was sampled from both real-time detection and overall detection perspectives. Then, the joint distribution was modeled using empirical Copula. Finally, the anomaly score was determined based on the model. The dataset of the drainage system in a hydropower station on Dadu River was used to investigate the effectiveness of the proposed method, and the results show that this method is more efficient than traditional anomaly detection methods and improves area under curve(AUC) value and average precision. Meanwhile, the interpretability of this method also provides a reliable basis for subsequent troubleshooting and maintenance of the drainage system.
- Publication
Science Technology & Engineering, 2023, Vol 23, Issue 33, p14256
- ISSN
1671-1815
- Publication type
Article