This paper describes a new methodology that aims to cover a gap detected in the area of detection of anomalies and diagnosis of industrial component behaviors: there is a need of robust procedures compatible with dynamic behaviors and degradations that evolve over time. The method proposed is based on the creation of behavior patterns of industrial components using well-known unsupervised machine learning algorithms such as K-means and Self-Organizing maps (SOMs) as a starting point. An algorithm based on local Probability Density Distributions (PDD) of the clusters obtained is used to enhance the characterization of patterns. The joint use of these algorithms facilitates a new way to detect anomalies and the surveillance of their progress. The paper includes an example of an application of the method proposed for monitoring the bearing temperature of a turbine in a hydropower plant showing how this method can be applied in behavior and maintenance assessment applications. The results obtained prove the advantages and possibilities that the proposed methodology has on real world applications.

本文介绍了一种旨在弥补在异常检测和工业组件行为诊断领域中发现的空白的新方法：需要与动态行为和随时间推移而退化的过程兼容的健壮程序。所提出的方法是基于使用K-means和自组织图（SOM）等众所周知的无监督机器学习算法创建工业组件行为模式的基础。基于获得的聚类的局部概率密度分布（PDD）的算法用于增强模式的特征。这些算法的联合使用提供了一种检测异常和监视其进度的新方法。本文包括一个建议的方法示例的应用实例，该方法用于监控水力发电厂中的涡轮轴承温度，该方法说明了该方法如何应用于行为和维护评估应用。获得的结果证明了所提出的方法在实际应用中的优势和可能性。