Total Projection to Latent Structures for Process Monitoring

Donghua ZHOU, Gang LI*, S. Joe QIN

*Corresponding author for this work

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

461 Citations (Scopus)

Abstract

Partial least squares or projection to latent structures (PLS) has been used in multivariate statistical process monitoring similar to principal component analysis. Standard PLS often requires many components or latent variables (LVs), which contain variations orthogonal to Y and useless for predicting Y. Further, the X-residual of PLS usually has quite large variations, thus is not proper to monitor with the Q-statistic. To reduce false alarm and missing alarm rates of faults related to Y, a total projection to latent structures (T-PLS) algorithm is proposed in this article. The new structure divides the X-space into four parts instead of two parts in standard PLS. The properties of T-PLS are studied in detail, including its relationship to the orthogonal PLS. Further study shows the space decomposition on X-space induced by T-PLS. Fault detection policy is developed based on the T-PLS. Case studies on two simulation examples show the effectiveness of the T-PLS based fault detection methods. © 2009 American Institute of Chemical Engineers (AIChE).
Original languageEnglish
Pages (from-to)168-178
Number of pages11
JournalAICHE Journal
Volume56
Issue number1
Early online date26 Aug 2009
DOIs
Publication statusPublished - Jan 2010
Externally publishedYes

Keywords

  • Fault detection
  • Orthogonal PLS
  • Partial least squares
  • Process monitoring
  • Total PLS

Fingerprint

Dive into the research topics of 'Total Projection to Latent Structures for Process Monitoring'. Together they form a unique fingerprint.

Cite this