Plant-wide troubleshooting and diagnosis using dynamic embedded latent feature analysis

S. Joe QIN*, Yingxiang LIU, Yining DONG

*Corresponding author for this work

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

12 Citations (Scopus)

Abstract

Plant-wide process data are usually high dimensional with dynamics residing in a reduced dimensional latent space. In this paper, we propose a novel procedure for diagnosing and troubleshooting plant-wide process anomalies using dynamic embedded latent feature analysis (DELFA). To remove the impact of external disturbances or exogenous variables, a dynamic inner canonical correlation analysis algorithm with exogenous variables is proposed. Composite loadings and composite weights are derived and applied for diagnosing a feature that is contained in several latent variables. The dynamic embedded latent features are usually related to poor control performance or malfunctioning control instrumentation. The proposed DELFA procedure with dynamic latent scores and composite loadings is applied to two industrial datasets of a chemical plant before and after a troubled control valve was fixed. The case study demonstrates convincingly that latent dynamic features are powerful for troubleshooting of process anomalies and diagnosing their causes in a plant-wide setting.

Original languageEnglish
Article number107392
JournalComputers and Chemical Engineering
Volume152
Early online date3 Jun 2021
DOIs
Publication statusPublished - Sept 2021
Externally publishedYes

Bibliographical note

Financial support for this work from the Natural Science Foundation of China grant, Big data-driven abnormal situation intelligent diagnosis and self-healing control for process industries (U20A201398), and the City University of Hong Kong under Project (9380123) is gratefully acknowledged. The authors are grateful to Eastman Chemical Company and the Texas-Wisconsin-California Control Consortium for providing the industrial data.

Keywords

  • Dynamic latent variable modeling
  • Latent feature learning
  • Latent variable composite loadings
  • Plant-wide troubleshooting
  • Reduced dimensional time series

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