Total Projection to Latent Structures for Process Monitoring

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

461 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)168-178
Journal / PublicationAICHE Journal
Volume56
Issue number1
Online published26 Aug 2009
Publication statusPublished - Jan 2010
Externally publishedYes

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).

Research Area(s)

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

Citation Format(s)

Total Projection to Latent Structures for Process Monitoring. / Zhou, Donghua; Li, Gang; Qin, S. Joe.
In: AICHE Journal, Vol. 56, No. 1, 01.2010, p. 168-178.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review