Fault detection and isolation with robust principal component analysis
Résumé
Principal component analysis (PCA) is a powerful fault detection technique which has been widely used in process industries. However, a main drawback of PCA is that it is based on least squares estimation techniques and hence fails to account for outliers which are common in physical processes. This paper is concerned with the fault detection and isolation problem. The proposed method does not require a data matrix without outliers for a PCA model design. Indeed, the approach directly uses the eventually corrupt database to elaborate a robust PCA model allowing fault detection. Then reconstruction principle and fault signatures analysis are used for fault isolation.
Origine : Fichiers produits par l'(les) auteur(s)
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