Multivariate industrial process monitoring based on the integration method of canonical variate analysis and independent component analysis

Tennessee Eastman (TE) process is a typical multivariate chemical process. It has some characteristics of complexity and nonlinearity. Therefore, it is an ideal research platform substituted for the real industrial process whose data is difficult to be achieved. Many scholars have done a lot of studies on monitoring approaches and applied these methods on the platform. However, it is not an easy work to obtain some ideal simulation results on detecting some special faults in TE process, such as the fault 3. In this paper, an integration of canonical variate analysis and independent component analysis method (CV-ICA) is proposed. It combines the advantages of canonical variate analysis (CVA) and independent component analysis (ICA) to solve these problems. CV-ICA applies CVA to calculate the canonical variates from the process data, and then employs ICA to extract independent components (ICs). The monitoring simulation demonstrates the availability of the proposed method.

► An integration method of CV-ICA is proposed for process monitoring.
► CV-ICA method provides a more efficient fault detection method for TE process.
► We achieve high fault detection rate for specific fault which is difficult to detect.