Outlier Generation and Anomaly Detection Based on Intelligent One-Class Techniques over a Bicomponent Mixing System
Outlier Generation and Anomaly Detection Based on Intelligent One-Class Techniques over a Bicomponent Mixing System
复制标题
双组分混合系统上基于智能一级技术的异常值生成和异常检测
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
J. Calvo
中科院分区：
文献类型：
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作者：
Esteban Jove;J. Casteleiro;Héctor Quintián;J. A. M. Pérez;J. Calvo
提高工业过程利润的最重要的一点在于实现良好的优化和应用智能维护计划。在这种情况下，早期异常起着重要作用。然后，异常检测分类器的实现是一个重要的挑战。由于工厂中可能发生的许多异常都具有未知的行为，因此有必要生成人工异常值来检查这些分类器。这项工作提出了不同的一类智能技术，在工业设施中进行异常检测，用于获得风力发电机叶片生产的主要材料。此外，人工异常数据生成，以检查每种技术的性能。总体而言，最终取得的成果是成功的。
One of the most important points to improve the profits in an industrial process lies on the fact of achieving a good optimisation and applying a smart maintenance plan. Under this circumstances an early anomaly plays an important role. Then, the implementation of classifiers for anomaly detection is an important challenge. As many of the anomalies that can occur in a plant have an unknown behaviour, it is necessary to generate artificial outliers to check these classifiers. This work presents different one-class intelligent techniques to perform anomaly detection in an industrial facility, used to obtain the main material for wind generator blades production. Furthermore, artificial anomaly data are generated to check the performance of each technique. The final results achieved are successful in general terms.