The integration of data-driven modeling techniques in machine learning applications, such
as multiclass classification, has resulted in robust classifier designs. However, one of the …

Technological advances in the process industries during the past decade have resulted in
increasingly complicated processes, systems and products. Therefore, recent researches …

The principal component analysis (PCA) is a linear technique widely used to retrieve a
subspace that maximizes the variance of the data, making the presence of a fault easy to …

The principal component analysis (PCA) is a well-known technique to detect, isolate and
estimate faults affecting a system. However, PCA identifies only linear structures in a given …

L'analyse en composantes principales (ACP) permet de simplifier l'analyse des données
généralement corrélées issues de capteurs par une restriction de ces données en un …

The Modular Multilevel Converter is gaining wide acceptance in both industrial and research
communities for medium and high power applications. However, the increasing need for a …

▶ Rakotomamonjy, 2003 and Cho et al.,(2005) proposed sensitivity based techniques. A
key disadvantage: need to analytically calculate the gradient for each statistics with respect …