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Semi‐supervised kernel partial least squares fault detection and identification approach with application to HGPWLTP
Author(s) -
Jia Qilong,
Du Wenyou,
Zhang Yingwei
Publication year - 2016
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2803
Subject(s) - regularization (linguistics) , outlier , tikhonov regularization , partial least squares regression , kernel (algebra) , nonlinear system , computer science , fault detection and isolation , identification (biology) , pattern recognition (psychology) , artificial intelligence , mathematics , machine learning , inverse problem , mathematical analysis , physics , botany , combinatorics , quantum mechanics , actuator , biology
In this paper, fault detection and identification methods based on semi‐supervised Laplacian regularization kernel partial least squares (LRKPLS) are proposed. In Laplacian regularization learning framework, unlabeled and labeled samples are used to improve estimate of data manifold so that one can establish a more robust data model. We show that LRKPLS can avoid the over‐fitting problem which may be caused by sample insufficient and outliers present. Moreover, the proposed LRKPLS approach has no special restriction on data distribution, in other words, it can be used in the case of nonlinear or non‐Gaussian data. On the basis of LRKPLS, corresponding fault detection and identification methods are proposed. Those methods are used to monitor a numerical example and Hot Galvanizing Pickling Waste Liquor Treatment Process (HGPWLTP), and the cases study show effeteness of the proposed approaches. Copyright © 2016 John Wiley & Sons, Ltd.