| Titre : | A novel statistical-based monitoring approach for complex multivariate processes (2009) |
| Auteurs : | Zhiqiang Ge, Auteur ; Lei Xie, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Industrial & engineering chemistry research (Vol. 48 N° 10, Mai 2009) |
| Article en page(s) : | pp. 4892–4898 |
| Note générale : | Chemical engineering |
| Langues : | Anglais |
| Tags : | 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 |
