Related and independent variable fault detection based on KPCA and SVDD

This paper proposes a new independent and related variable monitoring based on kernel principal component analysis (KPCA) and support vector data description (SVDD) algorithm. Some process variables are considered independent from other variables and the monitoring of independent and related variables should be performed separately. First, an independent variable division strategy based on mutual information is presented. Second, SVDD and KPCA methods are adopted to monitor independent variable space and related variable space, respectively. Finally, a general statistic is built according to the monitoring results of SVDD and KPCA. The proposed KPCA-SVDD method considers the related and independent characters of variables. This method combines the advantages of KPCA in managing nonlinear related variables and those of SVDD in handling independent variables. A numerical system and the Tennessee Eastman process are used to examine the efficiency of the proposed method. Simulation results have proved the superiority of KPCA-SVDD method.