SVM Based on Gaussian and Non-Gaussian Double Subspace for Fault Detection

In industrial production processes, the data usually have high-dimensional characteristics. When a support vector machine (SVM) is used for fault detection, it takes a long time to run. For high-dimensional data, principal component analysis (PCA) and independent component analysis (ICA) are very effective dimensionality reduction algorithms. Since the data follow different distributions in industrial processes, PCA is commonly used to process Gaussian-distributed data, and ICA is used to process non-Gaussian distributed data. Addressing these limitations, a novel fault detection method of SVM based on Gaussian and non-Gaussian double subspace (DSSVM) is proposed. The Kolmogorov-Smirnov (KS) test is used to determine the normal distribution characteristics of the process variables in the original data. The process variables are divided into a Gaussian subspace and a non-Gaussian subspace. Fault detection models of the Gaussian subspace are established based on PCA, and models of the non-Gaussian subspace are based on ICA. To reduce the effect of autocorrelation among process variables on the SVM, the delay and time difference input characteristics are introduced into the principal components obtained in the Gaussian subspace and independent components obtained in the non-Gaussian subspace. Finally, the time delay matrix and time difference matrix are combined, and the SVM model is used for fault detection and monitoring. The DSSVM method reduces the data dimensions and eliminates the effect of autocorrelation among process variables on the detection results. The proposed method is applied to a multivariable numerical simulation and the Tennessee-Eastman industrial process. Comparisons with simulation results for the kernel principal component analysis (KPCA), kernel independent component analysis (KICA), SVM and the statistical process monitoring method based on variable distribution characteristics (VDSPM) further verify the effectiveness of the algorithm.