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Diagnosis of process faults in chemical systems using a local partial least squares approach
Author(s) -
Kruger Uwe,
Dimitriadis Grigorios
Publication year - 2008
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.11576
Subject(s) - partial least squares regression , process (computing) , computer science , gaussian , gaussian process , chemical process , fault (geology) , kriging , relation (database) , fault detection and isolation , analogy , algorithm , data mining , artificial intelligence , engineering , machine learning , chemistry , linguistics , philosophy , computational chemistry , chemical engineering , seismology , actuator , geology , operating system
This article discusses the application of partial least squares (PLS) for monitoring complex chemical systems. In relation to existing work, this article proposes the integration of the statistical local approach into the PLS framework to monitor changes in the underlying model rather than analyzing the recorded input/output data directly. As discussed in the literature, monitoring changes in model parameters addresses the problems of nonstationary behavior and presents an analogy to model‐based approaches. The benefits of the proposed technique are that (i) a detailed mechanistic plant model is not required, (ii) nonstationary process behavior does not produce false alarms, (iii) parameter changes can be non‐Gaussian, (iv) Gaussian monitoring statistics can be established to simplify the monitoring task, and (v) fault magnitude and signatures can be estimated. This is demonstrated by a simulation example and the analysis of recorded data from two chemical processes. © 2008 American Institute of Chemical Engineers AIChE J, 2008