Fault diagnosis of chemical processes based on joint recurrence quantification analysis
•An unsupervised JRQA-based fault diagnosis scheme was proposed for handling of missing data.•Three nonlinear, unstable and nonstationary multivariate chemical process were analyzed.•Compatibility and impact of various imputation methods were assessed.•JRQA showed lower sensitivity and higher robust...
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Published in | Computers & chemical engineering Vol. 155; p. 107549 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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Elsevier Ltd
01.12.2021
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Abstract | •An unsupervised JRQA-based fault diagnosis scheme was proposed for handling of missing data.•Three nonlinear, unstable and nonstationary multivariate chemical process were analyzed.•Compatibility and impact of various imputation methods were assessed.•JRQA showed lower sensitivity and higher robustness in case of missing data.•JRQA method can be tuned for different frequency and scale of missing data.
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.
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AbstractList | •An unsupervised JRQA-based fault diagnosis scheme was proposed for handling of missing data.•Three nonlinear, unstable and nonstationary multivariate chemical process were analyzed.•Compatibility and impact of various imputation methods were assessed.•JRQA showed lower sensitivity and higher robustness in case of missing data.•JRQA method can be tuned for different frequency and scale of missing data.
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.
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ArticleNumber | 107549 |
Author | Nazemzadeh, Nima Zarghami, Reza Andersson, Martin Peter Ziaei-Halimejani, Hooman Gernaey, Krist V. Mostoufi, Navid Mansouri, Seyed Soheil |
Author_xml | – sequence: 1 givenname: Hooman surname: Ziaei-Halimejani fullname: Ziaei-Halimejani, Hooman organization: Multiphase Systems Research Lab, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran 11155/4563, Iran – sequence: 2 givenname: Nima surname: Nazemzadeh fullname: Nazemzadeh, Nima organization: Department of Chemical and Biochemical Engineering, Søltofts Plads, Building 228A, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark – sequence: 3 givenname: Reza surname: Zarghami fullname: Zarghami, Reza email: rzarghami@ut.ac.ir organization: Multiphase Systems Research Lab, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran 11155/4563, Iran – sequence: 4 givenname: Krist V. surname: Gernaey fullname: Gernaey, Krist V. organization: Department of Chemical and Biochemical Engineering, Søltofts Plads, Building 228A, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark – sequence: 5 givenname: Martin Peter surname: Andersson fullname: Andersson, Martin Peter organization: Department of Chemical and Biochemical Engineering, Søltofts Plads, Building 228A, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark – sequence: 6 givenname: Seyed Soheil surname: Mansouri fullname: Mansouri, Seyed Soheil organization: Department of Chemical and Biochemical Engineering, Søltofts Plads, Building 228A, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark – sequence: 7 givenname: Navid surname: Mostoufi fullname: Mostoufi, Navid organization: Multiphase Systems Research Lab, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran 11155/4563, Iran |
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