A Study of Complex Industrial Systems Based on Revised Kernel Principal Component Regression Method

Abstract

As a data-driven process monitoring method, multivariable statistics techniques have special potentials and advantages to handle the increasingly prominent “Big data challenge” in the complex industrial systems. However, the standard partial least square (PLS) method and the principal component regression (PCR) method cannot maintain stable function in the nonlinear operating environment. In order to capture the precise relation of process variables and product variables, an approach called the revised kernel PCR (RKPCR) method is proposed in this thesis to resolve the problems encountered in the traditional PCR method. In addition, a brief and effective diagnosis logic is designed to decrease the difficulty of fault diagnosis. Finally, the effectiveness of the RKPCR algorithm is illustrated utilizing the Tennessee Eastman case (TEC) platform.