Data-driven fault detection process using correlation based clustering

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. (C) 2020 Elsevier B.V. All rights reserved.