Zitrón, S.A., 33211 Gijón, Spain.
Department of Computer Science, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain.
Sensors (Basel). 2021 Apr 4;21(7):2532. doi: 10.3390/s21072532.
Anomaly Detection research is focused on the development and application of methods that allow for the identification of data that are different enough-compared with the rest of the data set that is being analyzed-and considered anomalies (or, as they are more commonly called, outliers). These values mainly originate from two sources: they may be errors introduced during the collection or handling of the data, or they can be correct, but very different from the rest of the values. It is essential to correctly identify each type as, in the first case, they must be removed from the data set but, in the second case, they must be carefully analyzed and taken into account. The correct selection and use of the model to be applied to a specific problem is fundamental for the success of the anomaly detection study and, in many cases, the use of only one model cannot provide sufficient results, which can be only reached by using a mixture model resulting from the integration of existing and/or ad hoc-developed models. This is the kind of model that is developed and applied to solve the problem presented in this paper. This study deals with the definition and application of an anomaly detection model that combines statistical models and a new method defined by the authors, the Local Transilience Outlier Identification Method, in order to improve the identification of outliers in the sensor-obtained values of variables that affect the operations of wind tunnels. The correct detection of outliers for the variables involved in wind tunnel operations is very important for the industrial ventilation systems industry, especially for vertical wind tunnels, which are used as training facilities for indoor skydiving, as the incorrect performance of such devices may put human lives at risk. In consequence, the use of the presented model for outlier detection may have a high impact in this industrial sector. In this research work, a proof-of-concept is carried out using data from a real installation, in order to test the proposed anomaly analysis method and its application to control the correct performance of wind tunnels.
异常检测研究专注于开发和应用方法，这些方法允许识别与正在分析的数据集的其余部分相比足够不同的数据，并将其视为异常（或者，更常见的说法是，离群值）。这些值主要来自两个来源：它们可能是在数据收集或处理过程中引入的错误，或者它们可能是正确的，但与其余值非常不同。正确识别每种类型是至关重要的，因为在第一种情况下，它们必须从数据集中删除，但在第二种情况下，它们必须仔细分析并考虑在内。正确选择和使用要应用于特定问题的模型是异常检测研究成功的基础，在许多情况下，仅使用一种模型无法提供足够的结果，只有通过使用集成现有和/或专门开发的模型的混合模型才能实现。这就是为解决本文提出的问题而开发和应用的模型。本研究涉及定义和应用一种异常检测模型，该模型结合了统计模型和作者定义的新方法，即局部瞬变离群点识别方法，以提高对影响风洞运行的传感器获得的变量的离群值的识别能力。正确检测风洞运行中涉及的变量的异常值对于工业通风系统行业非常重要，特别是对于垂直风洞，因为这些风洞被用作室内跳伞的培训设施，如果这些设备的性能不正确，可能会危及人类生命。因此，所提出的模型在该工业领域中的异常检测可能具有重大影响。在这项研究工作中，使用实际安装的数据进行了概念验证，以测试所提出的异常分析方法及其在控制风洞正确性能中的应用。
