A new fault detection method for nonlinear process monitoring

Abstract

Kernel Principal Component Analysis (KPCA) is a nonlinear extension of Principal Component Analysis (PCA). Recently, it is the most popular technique for monitoring nonlinear processes. However, the time-varying property of the industrial processes requires the adaptive ability of the KPCA. Therefore, in this paper, a Variable Moving Window Kernel PCA (VMWKPCA) method is proposed to update the KPCA model. The concept of this method consists of varying the size of the moving window according to the change of the normal process. To evaluate the performance of the proposed method, the VMWKPCA is applied for monitoring a Continuous Stirred Tank Reactor (CSTR) and a Tennessee Eastman process (TE). The results are satisfactory compared to the conventional Moving Window Kernel PCA (MWKPCA) and the Adaptive Kernel PCA (AKPCA).