TY - JOUR
T1 - Fault Detection using Empirical Mode Decomposition based PCA and CUSUM with Application to the Tennessee Eastman Process
AU - Du, Yuncheng
AU - Du, Dongping
N1 - Publisher Copyright:
© 2018
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In this work, a new algorithm is developed to identify stochastic faults in the Tennessee Eastman (TE) process, which integrates Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA), Cumulative Sum (CUSUM), and half-normal probability plot to detect three particular faults that could not be properly detected with previously reported techniques. This algorithm includes three steps: measurements pre-filtering, sensitivity analysis, and fault detection. Measured variables are first decomposed into different scales using the EEMD-based PCA for extracting fault signatures, from which a subset of variables that are sensitive to faults are selected with the half-normal probability plot. Based on the specific variables, CUSUM-based statistics are further used for improved fault detection. The algorithm can successfully identify three particular faults in the TE process with small time delay.
AB - In this work, a new algorithm is developed to identify stochastic faults in the Tennessee Eastman (TE) process, which integrates Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA), Cumulative Sum (CUSUM), and half-normal probability plot to detect three particular faults that could not be properly detected with previously reported techniques. This algorithm includes three steps: measurements pre-filtering, sensitivity analysis, and fault detection. Measured variables are first decomposed into different scales using the EEMD-based PCA for extracting fault signatures, from which a subset of variables that are sensitive to faults are selected with the half-normal probability plot. Based on the specific variables, CUSUM-based statistics are further used for improved fault detection. The algorithm can successfully identify three particular faults in the TE process with small time delay.
KW - Process monitoring
KW - control
KW - process data analytics
KW - sensitivity analysis
KW - stochastic faults
UR - http://www.scopus.com/inward/record.url?scp=85054454600&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2018.09.377
DO - 10.1016/j.ifacol.2018.09.377
M3 - Article
AN - SCOPUS:85054454600
VL - 51
SP - 488
EP - 493
JO - 10th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2018: Shenyang, China, 25-27 July 2018
JF - 10th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2018: Shenyang, China, 25-27 July 2018
SN - 2405-8963
IS - 18
ER -