Bidirectional Dynamic Latent Variable Analysis for Closed-Loop Process Monitoring

Xu CHEN, Xiao HE, Joe S. QIN

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Closed-loop data are widely encountered in modern industrial systems, which require special data analytics to gain insight for system monitoring. A closed-loop dynamic latent analysis scheme named closed-loop DiCCA (CL-DiCCA) is proposed in this article. Bidirectional dynamic latent variable relationships are proposed with a new objective to extract the closed-loop dynamic latent structure. An iterative algorithm is proposed to solve the constructed optimization problem for closed-loop processes. Four statistically independent residuals are generated, which monitor the dynamic and static variations of the process data. A process monitoring logic with the CL-DiCCA model is established, which offers further separation of faults into output-relevant and output-irrelevant ones. A numerical simulation and a case study on the thruster system of the Jiaolong deep-sea submersible are provided to illustrate the effectiveness of the proposed method.
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Early online date23 Oct 2023
DOIs
Publication statusE-pub ahead of print - 23 Oct 2023

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Bidirectional latent dynamic models
  • closed-loop process monitoring
  • Correlation
  • Data models
  • dynamic latent variable (DLV) analysis
  • Heuristic algorithms
  • Loading
  • Predictive models
  • Principal component analysis
  • Process monitoring

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