Canonical Variate Analysis Method Research Articles

Nonlinear Dynamic Process Monitoring Using Canonical Variate Kernel Analysis

Most industrial systems today are nonlinear and dynamic. Traditional fault detection techniques show their limits because they can hardly extract both nonlinear and dynamic features simultaneously. Canonical variate analysis (CVA) shows its excellent monitoring performance in fault detection for dynamic processes but is not applicable to nonlinear processes. Inspired by the CVA method, a novel nonlinear dynamic process monitoring method, namely, the “canonical variate kernel analysis” (CVKA), is proposed in this work. The way to extract nonlinear features is different from a traditional kernel canonical variate analysis (KCVA). In a sequential structure, the new approach firstly extracts the linear dynamic features from the data through the CVA method, followed by a kernel principal component analysis to extract nonlinear features from the CVA residual space. The new CVKA method is then applied to a TE process case study, proving the excellent performance of CVKA compared to other common approaches in dynamic nonlinear process monitoring for TE-like processes.

Variable Sub-Region Canonical Variate Analysis for Dynamic Process Monitoring

When all process variables are used to establish one data-based model, some variables have little contributions to the model. When these variables are grouped into a small local variable set to develop a data-based model, their contributions will become large. It is called local variable characteristic. Standard canonical variate analysis (CVA) based process monitoring method doesn’t consider local process variable characteristic. To solve this problem, a variable sub-region based canonical variate analysis (V-CVA) is proposed for dynamic process monitoring by enhancing the information of local variables. The proposed method combines variable sub-region division and Bayesian fusion. First, standard CVA is performed by using all process variables. Then, K-means clustering is adopted to divide process variables into sub-regions based on the mutual information of process variables and canonical variables. For each variable sub-region, CVA is conducted to compute local process monitoring statistics. Finally, local statistics are fused to build ensemble statistics based on the idea of Bayesian inference. Compared with the standard CVA method, the proposed V-CVA method can emphasize the information of local variables, and has better monitoring performance in the canonical variable feature subspace and the prediction residual subspace. Two examples demonstrate the effectiveness of the proposed method.