TE process fault diagnosis based on KPCA-RF

For the safety of automatic industry processing, the prediction and analysis of abnormal conditions of field equipment fast and accurately is of great significance. The KPCA-RF fault diagnosis approach, which is based on the kernel principal component analysis (KPCA) and the random forest (RF), is developed in this study to address the issues with the mainstream fault diagnosis method in the Tennessee-Eastman (TE) process. High-dimensional raw data must be feature extracted with KPCA before time series based nonlinear feature data can be obtained. The RF method is then used to determine the fault type of the feature data. The accuracy of this diagnosis method has been proved through comparative experiments.