Anomaly Detection Based on Regularized Vector Auto Regression in Thermal Power Plant

¹ School of Electronic Information Engineering, Tianjin University, Tianjin, 300072, China
² Tianjin Key Laboratory of Cognitive Computing and Application
³ School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China
⁴ School of Science and Institute of TV and Image Information, Tianjin University, Tianjin, 300072 China

^a Corresponding author: yhs@tju.edu.cn

Abstract

Anomaly detection has gained widespread interest especially in the industrial conditions. Contextual anomalies means that sensors of industrial equipment are interrelated and a sensor data instance called anomalous should be in a specific context. In this paper we propose a scheme for temporal sensor data monitor and anomaly detection in thermal power plant. The scheme is based on Regularized Vector Auto Regression, which is used to capture the linear interdependencies among multiple time series. The advantage is that the RVAR model does not require too much knowledge about the forces influencing a variable. The only prior knowledge needed is a list of variables which can be hypothesized to affect each other. Experimental results show that the proposed scheme is efficient compared with other methods such as SVM, BPNN and PCA.