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Quality‐relevant and process‐relevant fault monitoring with concurrent projection to latent structures
Author(s) -
Qin S. Joe,
Zheng Yingying
Publication year - 2013
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.13959
Subject(s) - subspace topology , linear subspace , residual , fault detection and isolation , projection (relational algebra) , process (computing) , computer science , fault (geology) , algorithm , quality (philosophy) , principal component analysis , control theory (sociology) , data mining , artificial intelligence , mathematics , philosophy , geometry , control (management) , epistemology , seismology , actuator , geology , operating system
This paper proposes a new concurrent projection to latent structures is proposed in this paper for the monitoring of output‐relevant faults that affect the quality and input‐relevant process faults. The input and output data spaces are concurrently projected to five subspaces, a joint input‐output subspace that captures covariations between input and output, an output‐principal subspace, an output‐residual subspace, an input‐principal subspace, and an input‐residual subspace. Fault detection indices are developed based on these subspaces for various fault detection alarms. The proposed monitoring method offers complete monitoring of faults that happen in the predictable output subspace and the unpredictable output residual subspace, as well as faults that affect the input spaces only. Numerical simulation examples and the Tennessee Eastman challenge problem are used to illustrate the effectiveness of the proposed method. © 2012 American Institute of Chemical Engineers AIChE J, 59: 496–504, 2013