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Fault isolation in nonlinear systems with structured partial principal component analysis and clustering analysis
Author(s) -
Huang Yunbing,
Mcavoy Thomas J.,
Gertler Janos
Publication year - 2000
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450780316
Subject(s) - principal component analysis , cluster analysis , nonlinear system , computer science , pattern recognition (psychology) , data mining , set (abstract data type) , fault detection and isolation , component (thermodynamics) , component analysis , artificial intelligence , mathematics , algorithm , physics , quantum mechanics , actuator , thermodynamics , programming language
Partial principal component analysis (PCA) and parity relations are proven to be useful methods in fault isolation. To overcome the limitation of applying partial PCA to nonlinear problems, a new approach utilizing clustering analysis is proposed. By dividing a partial data set into smaller subsets, one can build more accurate PCA models with fewer principal components, and isolate faults with higher precision. Simulations on a 2 × 2 nonlinear system and the Tennessee Eastman (TE) process show the advantages of using the clustered partial PCA method over other nonlinear approaches.