TY - GEN
T1 - Multi-directional reconstruction based contributions for root-cause diagnosis of dynamic processes
AU - LI, Gang
AU - QIN, S. Joe
AU - CHAI, Tianyou
PY - 2014/6
Y1 - 2014/6
N2 - Dynamic principal component analysis (DPCA) is required for the modeling and monitoring of dynamic processes. However, the root cause identification of faulty variables is quite desired after a fault is detected. As DPCA based methods construct detection indices in augmented variable space, it is difficult to use contribution analysis for diagnosis in a common way. In recent literature, reconstruction based contribution (RBC) is proposed, which is more efficient to diagnose sensors responsible for a fault than traditional contribution analysis. However, they both suffer from smearing effect. In this paper, an extended method of RBC based on DPCA is proposed to select multiple faulty variables in the sense of reconstruction, which is called multi-directional RBC. The case study on continuous stirred tank reactor (CSTR) process is used to demonstrate the effectiveness of the proposed approach. © 2014 American Automatic Control Council.
AB - Dynamic principal component analysis (DPCA) is required for the modeling and monitoring of dynamic processes. However, the root cause identification of faulty variables is quite desired after a fault is detected. As DPCA based methods construct detection indices in augmented variable space, it is difficult to use contribution analysis for diagnosis in a common way. In recent literature, reconstruction based contribution (RBC) is proposed, which is more efficient to diagnose sensors responsible for a fault than traditional contribution analysis. However, they both suffer from smearing effect. In this paper, an extended method of RBC based on DPCA is proposed to select multiple faulty variables in the sense of reconstruction, which is called multi-directional RBC. The case study on continuous stirred tank reactor (CSTR) process is used to demonstrate the effectiveness of the proposed approach. © 2014 American Automatic Control Council.
KW - dynamic principal component analysis
KW - multi-directional reconstruction based contribution
KW - Root cause diagnosis
KW - smearing effect
UR - http://www.scopus.com/inward/record.url?scp=84905715757&partnerID=8YFLogxK
U2 - 10.1109/ACC.2014.6859002
DO - 10.1109/ACC.2014.6859002
M3 - Conference paper (refereed)
SN - 9781479932726
T3 - Proceedings of the American Control Conference
SP - 3500
EP - 3505
BT - 2014 American Control Conference (ACC)
PB - Institute of Electrical and Electronics Engineers
T2 - 2014 American Control Conference, ACC 2014
Y2 - 4 June 2014 through 6 June 2014
ER -