This paper presents an algorithm for the fault detection process using correlation based clustering. Conventional clustering-based fault detection calculates the fault index through dimension reduction and clustering algorithm, and detects when the index exceeds the probabilistic limit. When an abnormality is detected through clustering through dimension reduction, it is difficult to perceive the physical meaning of the original data because the data is transformed. However, when detecting and analyzing anomalies in many engineering problems or data analysis, the physical meaning of the data is one of the important information. This paper proposes an anomaly detection process of correlation-based clustering which could recognize the relationship of data. The proposed anomaly detection algorithm selects highly correlated datasets, generates each clustering model, and calculates a fault index using stochastic distances. The fault detection performance was provided and verified using hydraulic test equipment data, and the results were compared with the conventional methods.

本文提出了一种基于相关聚类的故障检测过程算法。传统的基于聚类的故障检测通过降维和聚类算法来计算故障指数，并检测该指数何时超过概率极限。当通过降维聚类检测到异常时，由于数据已转换，因此难以感知原始数据的物理含义。但是，在许多工程问题或数据分析中检测和分析异常时，数据的物理意义是重要的信息之一。本文提出了一种基于相关性的聚类异常检测过程，该过程可以识别数据之间的关系。提出的异常检测算法选择高度相关的数据集，生成每个聚类模型，并使用随机距离计算故障指数。使用液压测试设备数据提供并验证了故障检测性能，并将结果与​​常规方法进行了比较。