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Fault detection based on Gaussian process latent variable models

Lookup NU author(s): Dr Javier Serradilla, Dr Jian Shi, Emeritus Professor Julian Morris

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Abstract

Gaussian processes, View the MathML source, can be used to approximate complex non-linear functions with relative simplicity. Their regression performance is, at least, comparable to that achieved via artificial neural networks (ANN) and, in fact, both methods are intrinsically related. They are both non-parametric and, as Neal (1994) [1] has shown, when the number of nodes in the hidden layer of a neural network tends to infinity the ANN converge to a Gaussian process.In most of the cases, the View the MathML source will map a multivariate input into a univariate response. In this paper, however, we present an approach to process monitoring that combines several View the MathML source so that multivariate responses can be appropriately modeled. We review a similar approach recently proposed in the literature and highlight some concerns related to it that needs to be taken into consideration. Additionally, we propose an alternative procedure to the way in which new observations are mapped into the non-linear model. A simulation study is provided that will help understand the method flexibility. Furthermore, results from a real example are also discussed.

Publication metadata

Author(s): Serradilla J, Shi JQ, Morris AJ

Publication type: Article

Publication status: Published

Journal: Chemometrics and Intelligent Laboratory Systems

Year: 2011

Volume: 109

Issue: 1

Pages: 9-21

Print publication date: 23/07/2011

Date deposited: 04/01/2012

ISSN (print): 0169-7439

ISSN (electronic): 1873-3239

Publisher: Elsevier BV

URL: http://dx.doi.org/10.1016/j.chemolab.2011.07.003

DOI: 10.1016/j.chemolab.2011.07.003

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