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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 nonlinear features can be extracted in non-linear non-Gaussian...
Data-driven fault detection technique has been widely applied for process monitoring, which can effectively detect faults happened in industrial processes. It is extremely significant for guaranteeing the normal operation of processes. Independent Component Analysis (ICA), a type of Data-driven fault detection technique, has been successfully applied to Blind Source Separation and process fault detection...
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