Combining KPCA with Sparse SVM for Nonlinear Process Monitoring

A new method for nonlinear process monitoring based on kernel principal analysis and sparse support vector machines is proposed. The data is analyzed using KPCA. T
²
and SPE are constructed in the future space. If the T
²
and SPE exceed the predefined control limit, a fault may have occurred . Then the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults. The proposed method is applied to the simulation of Tennessee Eastman (TE) chemical process .The simulation results show that the proposed method can identify various types of faults accurately and rapidly.