基于<bold>KPCA</bold>-<bold>SVM</bold>的相关和独立变量故障检测方法

Abstract

In the real industrial process, some process variables are independent of other variables, a fault detection method of related and independent variable based on kernel principal component analysis and support vector machine (KPCA-SVM) is proposed to detect these independent variables separately from related variables. Firstly, a variable division strategy based on mutual information is applied to divide the process variables into related variables and independent variables by calculating the mutual information between variables. Then, KPCA and SVM models are established in the related variable space and the independent variable space to monitor the test data. Compared with the traditional KPCA and SVM methods, the KPCA-SVM method combines the advantages of KPCA in detecting related variables and SVM methods in detecting independent variables, and improves the fault detection performance of KPCA and SVM methods. Finally, the KPCA-SVM method is applied to the Tennessee-Eastman (TE) industrial process for fault detection, and compared with KPCA, kernel entropy component analysis (KECA) and SVM methods. The results show that the proposed KPCA-SVM method has a good detection effect and improves the detection effect of multiple faults, among which the detection effect of minor fault 5 is significantly improved, which further verifies the effectiveness of the KPCA-SVM method.