Multivariate fault detection and classification using interval principal component analysis

The results show that IPCA methods have a higher detection rate than classical PCA, for the same false alarm rate. Moreover, IPCA methods are capable of differentiating the type of fault to a high degree of accuracy, unlike classical PCA. Interval centers were capable of detecting changes in mean, while interval radii were capable of detecting changes in variance. Furthermore, for data classification, the results show that MRIPCA had a higher classification precision than other IPCA methods and classical PCA.