Integrating Canonical Variate Analysis and Kernel Independent Component Analysis for Tennessee Eastman Process Monitoring

Abstract

Constructing an efficient and stable fault diagnosis method is crucial for industrial production. Most classical methods are inadequate in process diagnosis, as their assumptions are difficult to satisfy in real production processes. For instance, a Gaussian distribution is required for principal component analysis (PCA), canonical analysis (CVA) is only compatible with linear industrial processes, and independent component analysis (ICA) is not useful for dynamic processes. In this paper, we have proposed a novel method, Canonical Variate and Kernel Independent Component Analysis (CV–KICA), which combines the advantages of CVA and KICA, and tested this method with the Tennessee Eastman (TE) process. We first use ICA to suppress the impacts of noises in industrial production, and introduce kernel to ICA for adapting to non-linearity, then integrate the resulting components into the CVA method for dynamic processes. Simulations and experimental results with the TE process indicate that the CV–KICA method outperform other classical diagnosis methods such as CVA, KICA and DKICA in fault diagnose, providing a novel approach that could handle dynamic and nonlinear situations in real production processes.