Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes : The Dynamic T-PLS Approach

Gang LI*, Baosheng LIU, S. Joe QIN, Donghua ZHOU

*Corresponding author for this work

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

111 Citations (Scopus)

Abstract

In data-based monitoring field, the nonlinear iterative partial least squares procedure has been a useful tool for process data modeling, which is also the foundation of projection to latent structures (PLS) models. To describe the dynamic processes properly, a dynamic PLS algorithm is proposed in this paper for dynamic process modeling, which captures the dynamic correlation between the measurement block and quality data block. For the purpose of process monitoring, a dynamic total PLS (T-PLS) model is presented to decompose the measurement block into four subspaces. The new model is the dynamic extension of the T-PLS model, which is efficient for detecting quality-related abnormal situation. Several examples are given to show the effectiveness of dynamic T-PLS models and the corresponding fault detection methods.
Original languageEnglish
Article number6080734
Pages (from-to)2262 - 2271
Number of pages10
JournalIEEE Transactions on Neural Networks
Volume22
Issue number12
Early online date14 Nov 2011
DOIs
Publication statusPublished - Dec 2011
Externally publishedYes

Bibliographical note

This work was supported in part by the National 973 Project under Grant 2010CB731800 and Grant 2009CB32602 and the Natural Science Foundation of China under Grant 61020106003, Grant 61021063, Grant 61028010, and Grant 61074085.

Keywords

  • Data-based monitoring
  • dynamic total projection to latent structures
  • multivariate dynamic processes
  • quality-related monitoring

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