Abstract One critical point to improve the economic and technical results of every industrial process lies on the fact of achieving a good optimization, and applying a smart maintenance plan. In this context, the tools development for detecting the appearance of any kind of anomaly represents an important challenge. For this reason, the implementation of classifiers for anomaly detection tasks has been a significant trend in the scientific community. However, since the behavior of the potential anomalies that may occur in a plant is unknown, it is necessary to generate artificial outliers to assess these classifiers. This paper proposes the performance checking of different intelligent one-class techniques to detect anomalies in an industrial plant, used to obtain the main material for wind generator blades production. These classifiers are tested using anomaly data generated, giving successful results.

中文翻译：

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双组分混合机监控的一类异常检测技术比较研究

摘要 提高每个工业过程的经济和技术成果的一个关键点在于实现良好的优化，并应用智能维护计划。在这种情况下，用于检测任何类型异常外观的工具开发代表了一项重要挑战。出于这个原因，异常检测任务的分类器的实现一直是科学界的一个重要趋势。然而，由于植物中可能发生的潜在异常的行为是未知的，因此有必要生成人工异常值来评估这些分类器。本文提出了不同智能一类技术的性能检查，以检测工业厂房中的异常情况，用于获取风力发电机叶片生产的主要材料。