Novel reduced kernel independent component analysis for process monitoring

Abstract

Kernel independent component analysis (KICA), as a nonlinear extension monitoring method of independent component analysis (ICA), has attracted significant attention. To accomplish different monitoring tasks for nonlinear systems with non-Gaussian data distribution, many modified algorithms based on KICA have also been designed. However, most of the existing methods suffer from defects; for example, the computation time increases with the number of training samples and the models are insensitive to minor faults. Nevertheless, there is currently limited research on addressing these defects, which greatly limits their application in industrial processes. To fill these gaps, a novel reduced kernel independent component analysis (NRKICA) method is proposed to reduce the computation complexity and improve the ability of minor fault detection at the same time. In this approach, an important factor is defined to measure the ability of the samples to represent the properties of the system. In addition, then the top-n important observations are selected to build a data dictionary. To improve the sensitivity to minor faults, the $I^2$ and $\mathrm{SPE}$ statistics are redesigned by introducing information from past observations. Besides, the kernel parameter is optimized by the tabu search algorithm. The proposed method is applied to fault detection with a numerical example and the Tennessee Eastman process (TEP), and the experimental results verify the effectiveness and sensitivity of the proposed method.