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Total projection to latent structures for process monitoring
Author(s) -
Zhou Donghua,
Li Gang,
Qin S. Joe
Publication year - 2010
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.11977
Subject(s) - partial least squares regression , principal component analysis , projection (relational algebra) , latent variable , statistic , fault detection and isolation , process (computing) , orthographic projection , pattern recognition (psychology) , fault (geology) , alarm , mathematics , false alarm , artificial intelligence , residual , computer science , statistics , algorithm , engineering , aerospace engineering , seismology , actuator , geology , operating system
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 J, 2010