TY - GEN
T1 - Deep causal mining for plant-wide oscillations with multilevel Granger causality analysis
AU - YUAN, Tao
AU - LI, Gang
AU - ZHANG, Zhaohui
AU - QIN, S. Joe
N1 - The authors are grateful for the financial support from the China Scholarship Council and the Texas-Wisconsin-California Control Consortium, and for the industrial data provided by the Eastman Chemical Company
PY - 2016/7
Y1 - 2016/7
N2 - Plant-wide disturbance such as oscillations are common in large-scale complex controlled processes whose effects propagate to many units and may deteriorate overall control performance. It is important to capture the major causal relationship within the plant and diagnose the root cause along with complete propagation paths. This paper presents a novel multilevel Granger causality framework for root cause diagnosis. The high level is regarded as a group-wise analysis, which is clustered by dynamic time warping-based K-means method and investigated using group Granger causality. The low level is individual causal reasoning within each group where a partial least squares modified Granger causal test is developed to overcome multicollinearity issue. The proposed causality analysis framework is validated through a benchmark industrial case study to show its effectiveness and superiority.
AB - Plant-wide disturbance such as oscillations are common in large-scale complex controlled processes whose effects propagate to many units and may deteriorate overall control performance. It is important to capture the major causal relationship within the plant and diagnose the root cause along with complete propagation paths. This paper presents a novel multilevel Granger causality framework for root cause diagnosis. The high level is regarded as a group-wise analysis, which is clustered by dynamic time warping-based K-means method and investigated using group Granger causality. The low level is individual causal reasoning within each group where a partial least squares modified Granger causal test is developed to overcome multicollinearity issue. The proposed causality analysis framework is validated through a benchmark industrial case study to show its effectiveness and superiority.
UR - http://www.scopus.com/inward/record.url?scp=84992028273&partnerID=8YFLogxK
U2 - 10.1109/ACC.2016.7526155
DO - 10.1109/ACC.2016.7526155
M3 - Conference paper (refereed)
SN - 9781467386838
T3 - Proceedings of the American Control Conference
SP - 5056
EP - 5061
BT - 2016 American Control Conference (ACC)
PB - Institute of Electrical and Electronics Engineers
T2 - 2016 American Control Conference, ACC 2016
Y2 - 6 July 2016 through 8 July 2016
ER -