Fault Diagnosis of Chemical Processes based on Joint Recurrence Quantification Analysis

Hooman Ziaei-Halimejani, Nima Nazemzadeh, Reza Zarghami*, Krist V. Gernaey, Martin Peter Andersson, Seyed Soheil Mansouri, Navid Mostoufi

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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.
Original languageEnglish
Article number107549
JournalComputers and Chemical Engineering
Number of pages19
Publication statusPublished - 2021


  • Missing data
  • Fault diagnosis
  • Joint recurrence plot
  • Unsupervised learning


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