Abstract
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.
Highlights
Novelty detection or outlier/anomaly detection techniques have been applied in many practical domains, such as fraud detection for credit cards, intrusion detection for cyber-security, faulty detection for industrial systems, to name but a few.[1,2,3] developing dedicated novelty detectors for industrial control systems has rarely been taken into account, let alone for an electric arc furnace (EAF) control system
We prefer to use a clustering algorithm to determine the validation set for each test point
Our selective mechanism can further improve the performance of feature bagging (FB)
Summary
Novelty detection or outlier/anomaly detection techniques have been applied in many practical domains, such as fraud detection for credit cards, intrusion detection for cyber-security, faulty detection for industrial systems, to name but a few.[1,2,3] developing dedicated novelty detectors for industrial control systems has rarely been taken into account, let alone for an electric arc furnace (EAF) control system. Novelty detection is drawing increasing attentions in industrial systems, because anomalous observations have adverse impact on both the modeling and the control process that use any data-driven technique. Supervised detectors use labeled training data to learn conventional classifiers, such as support vector machine (SVM) and decision tree, that can separate normal observations from anomalous ones. Unsupervised detectors can use some similarity criteria like distance and density to mine potential outliers in databases These detectors are usually used in off-line ways and most presentative methods should be distance-based detector and local outlier factor (LOF).[9,10] Semi-supervised detectors are referred to as one-class (OC) classifiers and data description techniques. We propose a dynamic selective model that uses SVDD as base detectors. Some related works and preliminaries will be presented in section ‘‘Related works and preliminaries.’’ The proposed method will be introduced in section ‘‘Methodology,’’ followed by the experiments in section ‘‘Experiments and analysis.’’ some conclusions will be drawn in section ‘‘Conclusion.’’
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