TY - JOUR
T1 - Total Projection to Latent Structures for Process Monitoring
AU - Zhou, Donghua
AU - Li, Gang
AU - Qin, S. Joe
PY - 2010/1
Y1 - 2010/1
N2 - 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).
AB - 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).
KW - Fault detection
KW - Orthogonal PLS
KW - Partial least squares
KW - Process monitoring
KW - Total PLS
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-77957599856&origin=recordpage
U2 - 10.1002/aic.11977
DO - 10.1002/aic.11977
M3 - RGC 21 - Publication in refereed journal
VL - 56
SP - 168
EP - 178
JO - AICHE Journal
JF - AICHE Journal
SN - 0001-1541
IS - 1
ER -