TY - JOUR
T1 - Fault Diagnosis of Chemical Processes based on Joint Recurrence Quantification Analysis
AU - Ziaei-Halimejani, Hooman
AU - Nazemzadeh, Nima
AU - Zarghami, Reza
AU - Gernaey, Krist V.
AU - Andersson, Martin Peter
AU - Mansouri, Seyed Soheil
AU - Mostoufi, Navid
PY - 2021
Y1 - 2021
N2 - An unsupervised learning method is developed for fault detection and diagnosis with missing data for chemical processes based on the multivariate extension of joint recurrence quantification analysis (JRQA) and clustering. The application of the proposed method is assessed in the presence and absence of imputation methods. To provide a comprehensive scheme, three different processes were utilized including, silica particle flocculation (SFP) as an unstable batch process, a chemical looping combustion (CLC) process, and the Tennessee Eastman process (TEP) as the control system design benchmark. The application of the developed method demonstrated that the JRQA method has the best performance in fault diagnosis of the complete dataset in all three processes compared to previously developed methods. Moreover, in the case of missing data, the sensitivity of the results can be adjusted by changing the length of the sub-series. The sensitivity of the proposed method is 33% lower for SFP, 30% for CLC and 32% for TEP, compared to the probabilistic kernel principal components analysis (PKPCA)-based method.
AB - An unsupervised learning method is developed for fault detection and diagnosis with missing data for chemical processes based on the multivariate extension of joint recurrence quantification analysis (JRQA) and clustering. The application of the proposed method is assessed in the presence and absence of imputation methods. To provide a comprehensive scheme, three different processes were utilized including, silica particle flocculation (SFP) as an unstable batch process, a chemical looping combustion (CLC) process, and the Tennessee Eastman process (TEP) as the control system design benchmark. The application of the developed method demonstrated that the JRQA method has the best performance in fault diagnosis of the complete dataset in all three processes compared to previously developed methods. Moreover, in the case of missing data, the sensitivity of the results can be adjusted by changing the length of the sub-series. The sensitivity of the proposed method is 33% lower for SFP, 30% for CLC and 32% for TEP, compared to the probabilistic kernel principal components analysis (PKPCA)-based method.
KW - Missing data
KW - Fault diagnosis
KW - Joint recurrence plot
KW - DBSCAN
KW - Unsupervised learning
U2 - 10.1016/j.compchemeng.2021.107549
DO - 10.1016/j.compchemeng.2021.107549
M3 - Journal article
SN - 0098-1354
VL - 155
JO - Computers & Chemical Engineering
JF - Computers & Chemical Engineering
M1 - 107549
ER -