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Regression‐based analysis of multivariate non‐Gaussian datasets for diagnosing abnormal situations in chemical processes
Author(s) -
Zeng Jiusun,
Xie Lei,
Kruger Uwe,
Gao Chuanhou
Publication year - 2014
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.14230
Subject(s) - gaussian process , kriging , gaussian , multivariate statistics , latent variable , parametric statistics , computer science , regression , regression analysis , chemical process , data mining , algorithm , statistics , machine learning , artificial intelligence , mathematics , engineering , chemistry , computational chemistry , chemical engineering
This article presents a regression‐based monitoring approach for diagnosing abnormal conditions in complex chemical process systems. Such systems typically yield process variables that may be both Gaussian and non‐Gaussian distributed. The proposed approach utilizes the statistical local approach to monitor parametric changes of the latent variable model that is identified by a revised non‐Gaussian regression algorithm. Based on a numerical example and recorded data from a fluidized bed reactor, the article shows that the proposed approach is more sensitive when compared to existing work in this area. A detailed analysis of both application studies highlights that the introduced non‐Gaussian monitoring scheme extracts latent components that provide a better approximation of non‐Gaussian source signal and/or is more sensitive in detecting process abnormities. © 2013 American Institute of Chemical Engineers AIChE J , 60: 148–159, 2014