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.

改善每个工业过程的经济和技术成果的关键点在于，要实现良好的优化并应用明智的维护计划。在这种情况下，用于检测任何种类异常现象的工具开发是一个重要的挑战。由于这个原因，用于异常检测任务的分类器的实施已成为科学界的重要趋势。但是，由于尚不清楚植物中可能发生的潜在异常的行为，因此有必要生成人为异常值来评估这些分类器。本文提出了对不同智能一类技术的性能检查，以检测工厂中的异常情况，以获取用于生产风力发电机叶片的主要材料。