As increasing data-driven control strategies are applied in electric arc furnace systems, the problem of novelty detection has drawn more attentions than before. The presence of outliers should be the main obstacle in practical applications for these advanced control techniques. To this end, this paper proposes a dynamically selective support vector data description model to discover novelties in electric arc furnace. In this model, support vector data description plays the role of base detector. Artificial outliers are generated with two objectives, one is to assist the dynamic selection, and the other is to optimize two parameters of support vector data description. Then clustering technique is used to determine the validation set for each test point. Finally, a probabilistic method is used to compute the competence of base detectors. In contrast to other novelty ensembles that have parallel structures, our ensemble model has a dynamic selection mechanism that could facilitate the mining of the potential of base detectors. Three synthetic and three real-world datasets are used to validate the effectiveness of the proposed detection model. Experimental results have approved our method by comparing it with several competitors.

中文翻译：

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使用动态选择支持向量数据描述模型发现电弧炉控制系统中的新颖性

随着越来越多的数据驱动控制策略应用于电弧炉系统，新奇检测问题比以前更加受到关注。异常值的存在应该是这些先进控制技术在实际应用中的主要障碍。为此，本文提出了一种动态选择性支持向量数据描述模型，以发现电弧炉的新颖性。在该模型中，支持向量数据描述起到了基检测器的作用。人工异常值的产生有两个目的，一是辅助动态选择，二是优化支持向量数据描述的两个参数。然后使用聚类技术来确定每个测试点的验证集。最后，使用概率方法计算基本检测器的能力。与其他具有平行结构的新奇集成相比，我们的集成模型具有动态选择机制，可以促进基础检测器潜力的挖掘。三个合成数据集和三个真实数据集用于验证所提出的检测模型的有效性。通过与几个竞争对手的比较，实验结果证实了我们的方法。