In this section, the performance of KICA is illustrated through a simple example and Tennessee Eastman process case study.
When designing the PCA, modified ICA, KPCA and KICA models, we must determine the number of components.
In comparison to PCA, KPCA, and modified ICA, the KICA is able to recover the original source signal patterns with changing the sign only from the mixed signals (Figure 1(f)).
The KICA monitoring approach proposed here was tested for its ability to detect various faults in simulated data obtained from a well-known benchmark process, the Tennessee Eastman industrial process.
Among 30 whitened vectors, 11 KPCA and KICA components are selected from average eigenvalue criterion, respectively.
The fault detection rates of the four multivariate methods, PCA, ICA, KPCA, and KICA for all 21 faults were computed and summarized in Table 4.
KICA can efficiently compute independent components in a high-dimensional feature space by means of non-linear kernel functions.