Artificial Neural Correlation Analysis for Performance-Indicator-Related Nonlinear Process Monitoring

In this article, a novel fault detection and process monitoring method referred to as artificial neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are common in complex industrial processes, the classic canonical correlation analysis (CCA) always perform poorly. Many scholars have noticed the nonlinear problem of the process and have also proposed some improved schemes, such as the kernel method. However, the selection of suitable parameters in the kernel method is extremely difficult, so most of the kernel learning methods are slightly unsatisfactory. Considering that the artificial neural network (ANN) can well extract the required feature components from the nonlinear data, we combined ANN and CCA from their respective principles, and proposed a new nonlinear monitoring method and the detailed gradient descent method derivation for the ANCA network is presented. In addition, we have designed two indices to monitor the changes of process variables and performance indicators. Finally, a numerical example, the Tennessee Eastman benchmark, and the Zhoushan thermal power plant process illustrate the superiority of the proposed method.

Automated summary

This article proposes a novel fault detection and process monitoring method called artificial neural correlation analysis (ANCA). By combining artificial neural networks (ANN) and canonical correlation analysis (CCA), this method is able to effectively handle the nonlinear characteristics commonly found in complex industrial processes.