Method based on principal component analysis and support vector machine and its application to process monitoring and fault diagnosis for lead-zinc smelting furnace

Based on the high performance of support vector machine (SVM) in tackling small sample size, high dimension and its good generalization, a process monitoring method based on principal component analysis (PCA) and SVM is proposed. Firstly, the PCA approach is adopted to extract the feature and reduce the dimension of data by getting rid of the correlation among them, and then it is applied to statistical process control of the imperial smelting furnace (ISF), with the change trend of expectations of T² and SPE statistics of the data, the ISF manufacture states are tested. Finally, the SVM combined with the nearest neighbor method is used for classification. The experiment result shows that the method is effective.