MATEC Web of Conferences
Volume 35, 20152015 4th International Conference on Mechanics and Control Engineering (ICMCE 2015)
|Number of page(s)||4|
|Section||Sensor design and application of technology|
|Published online||16 December 2015|
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: firstname.lastname@example.org
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.
© Owned by the authors, published by EDP Sciences, 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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