Dynamic kernel independent component analysis approach for fault detection and diagnosis

In this paper, a novel kernel independent component analysis method which is named improved DKICA is proposed for dynamic industry processes' fault detection and fault diagnosis. The primary idea of this method is how to obtain an augmented measurement matrix in the data kernel space, the independent component analysis is used, so the dynamic and non-linear features can be extracted in non-linear non-Gaussian dynamic processes. Furthermore, a contribution plot of non-linear data is defined for the improved DKICA, with which the root caused for each individual fault can be detected and isolated accurately. Finally, compared with other existing statistical monitoring and fault detection methods, the Tennessee Eastman process proves the improved performance of our proposed method.