2.2 Independent component analysis

Stefatos and Hamza [14] and Hsu et al. [15] have introduced diagnostic methods using an approach based on dynamic independent component analysis capable of accurately detecting and isolating the root causes of individual faults. Nonlinear variants of these approaches have been investigated by Cai et al. [16], who have integrated the kernel FastICA algorithm with a manifold learning method known as locality preserving projection. Moreover, kernel FastICA was used to integrate FastICA and kernel PCA to exploit the advantages of both algorithms, as indicated by Zhang and Qin [17], Zhang [18], and Zhang et al. [19].
