TY - GEN
T1 - Granger Causality Models with Gaussian Kernel Functions for Industrial Alarm Correlation Analysis
AU - Wenduo, Wang
AU - Jince, Li
AU - Hongguang, Li
AU - Bo, Yang
AU - Chu, Qi
AU - Yuehan, Wang
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - The alarm analysis between different variables is recognized as a complex and important subject in the process industry. However, the discrete type of alarm data and the unclear time-delay between alarm data of different variables discourage the use of traditional causality analysis methods. In this paper, a Granger causality model based on Gaussian kernel functions is used to analyze the correlation of industrial alarms. Firstly, aiming at the reduction of false alarms caused by chattering alarms and binary operating data associated with switching valves, the Gaussian kernel function method is used to convert binary data into continuous sequences. Secondly, Dynamic Time Warping approaches are used to perform similarity matching on continuous sequences, screening and correlating alarms. Finally, the Granger causality model is used to obtain the correlation between different variable alarms.
AB - The alarm analysis between different variables is recognized as a complex and important subject in the process industry. However, the discrete type of alarm data and the unclear time-delay between alarm data of different variables discourage the use of traditional causality analysis methods. In this paper, a Granger causality model based on Gaussian kernel functions is used to analyze the correlation of industrial alarms. Firstly, aiming at the reduction of false alarms caused by chattering alarms and binary operating data associated with switching valves, the Gaussian kernel function method is used to convert binary data into continuous sequences. Secondly, Dynamic Time Warping approaches are used to perform similarity matching on continuous sequences, screening and correlating alarms. Finally, the Granger causality model is used to obtain the correlation between different variable alarms.
KW - Alarm prediction
KW - Gaussian kernel
KW - Granger causality
UR - https://www.scopus.com/pages/publications/85128106409
U2 - 10.1109/CAC53003.2021.9728358
DO - 10.1109/CAC53003.2021.9728358
M3 - 会议稿件
AN - SCOPUS:85128106409
T3 - Proceeding - 2021 China Automation Congress, CAC 2021
SP - 4156
EP - 4161
BT - Proceeding - 2021 China Automation Congress, CAC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 China Automation Congress, CAC 2021
Y2 - 22 October 2021 through 24 October 2021
ER -