Fault detection based on Gaussian process latent variable models

Javier Serradilla

doi:10.1016/j.chemolab.2011.07.003

Fault detection based on Gaussian process latent variable models

2011-11-01

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AI

DOI：10.1016/j.chemolab.2011.07.003

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Gaussian processes, GPs, 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 GP will map a multivariate input into a univariate response. In this paper, however, we present an approach to process monitoring that combines several GPs 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. (C) 2011 Elsevier B.V. All rights reserved.

Keywords:

Latent variable models

Process monitoring

Multivariate statistical process control (MSPC)

Gaussian process

Fault detection based on Gaussian process latent variable models

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