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Conference Paper
An Approach for Utilizing Correlation among Sensors for Unsupervised Anomaly Detection of Wind Turbine System
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- Authors
- HyunYong Lee, Nac-Woo Kim, Jun-Gi Lee, Byung-Tak Lee
- Issue Date
- 2021-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2021, pp.104-109
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- An anomaly detection model needs to be built by considering the characteristics of a target system. In this paper, we study unsupervised anomaly detection for a wind turbine system. A wind turbine system has many components and sensors of the same components are highly correlated. Considering these characteristics of a wind turbine system, our approach for unsupervised anomaly detection is to utilize correlation among sensors. We first generate a standard correlation matrix (i.e., the most representative normal correlation matrix) from the given normal data. Then, we measure a distance between the standard matrix and a correlation matrix of a target test data using mean squared error as an anomaly score. Through experiments using real wind turbine system data, we show that our unsupervised approach achieves a maximum of 0.928 ROC AUC (0.894 on average),
- KSP Keywords
- Detection model, Test data, Unsupervised Approach, Wind Turbine System, anomaly score, correlation matrix, mean square error(MSE), unsupervised anomaly detection, wind turbine(WT)