Journal article
Fault Diagnosis of Chemical Processes based on Joint Recurrence Quantification Analysis
University of Tehran1
Department of Chemical and Biochemical Engineering, Technical University of Denmark2
KT Consortium, Department of Chemical and Biochemical Engineering, Technical University of Denmark3
PROSYS - Process and Systems Engineering Centre, Department of Chemical and Biochemical Engineering, Technical University of Denmark4
CHEC Research Centre, Department of Chemical and Biochemical Engineering, Technical University of Denmark5
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.
| Language: | English |
|---|---|
| Year: | 2021 |
| Pages: | 107549 |
| ISSN: | 18734375 and 00981354 |
| Types: | Journal article |
| DOI: | 10.1016/j.compchemeng.2021.107549 |
| ORCIDs: | Nazemzadeh, Nima , Gernaey, Krist V. , Andersson, Martin Peter and Mansouri, Seyed Soheil |
