[article] 
inIndustrial & engineering chemistry research > Vol. 48 N° 10 (Mai 2009) . - pp. 4892–4898

Titre :	A novel statistical-based monitoring approach for complex multivariate processes
Type de document :	texte imprimé
Auteurs :	Zhiqiang Ge, Auteur ; Lei Xie, Auteur
Année de publication :	2009
Article en page(s) :	pp. 4892–4898
Note générale :	Chemical engineering
Langues :	Anglais (eng)
Mots-clés :	Non-Gaussian variables Gaussian essential Independent component analysis Factor
Résumé :	Conventional methods are under the assumption that a process is driven by either non-Gaussian or Gaussian essential variables. However, many complex processes may be simultaneously driven by these two types of essential source. This paper proposes a novel independent component analysis and factor analysis (ICA-FA) method to capture the non-Gaussian and Gaussian essential variables. The non-Gaussian part is first extracted by ICA and support vector data description is utilized to obtain tight confidence limit. A probabilistic approach is subsequently incorporated to separate the residual Gaussian part into latent influential factors and unmodeled uncertainty. By retrieving the underlying process data generating structure, ICA-FA facilitates the diagnosis of process faults that occur in different sources. A further contribution of this paper is the definition of a new similarity factor based on the ICA-FA for fault identification. The efficiency of the proposed method is shown by a case study on the TE benchmark process.
En ligne :	http://pubs.acs.org/doi/abs/10.1021/ie800935e

[article] A novel statistical-based monitoring approach for complex multivariate processes [texte imprimé] / Zhiqiang Ge, Auteur ; Lei Xie, Auteur . - 2009 . - pp. 4892–4898.
Chemical engineering
Langues : Anglais (eng)
in Industrial & engineering chemistry research > Vol. 48 N° 10 (Mai 2009) . - pp. 4892–4898

Mots-clés :	Non-Gaussian variables Gaussian essential Independent component analysis Factor
Résumé :	Conventional methods are under the assumption that a process is driven by either non-Gaussian or Gaussian essential variables. However, many complex processes may be simultaneously driven by these two types of essential source. This paper proposes a novel independent component analysis and factor analysis (ICA-FA) method to capture the non-Gaussian and Gaussian essential variables. The non-Gaussian part is first extracted by ICA and support vector data description is utilized to obtain tight confidence limit. A probabilistic approach is subsequently incorporated to separate the residual Gaussian part into latent influential factors and unmodeled uncertainty. By retrieving the underlying process data generating structure, ICA-FA facilitates the diagnosis of process faults that occur in different sources. A further contribution of this paper is the definition of a new similarity factor based on the ICA-FA for fault identification. The efficiency of the proposed method is shown by a case study on the TE benchmark process.
En ligne :	http://pubs.acs.org/doi/abs/10.1021/ie800935e