Nonlinear Process Monitors Method Based on Kernel Function and PNN

Kernel PCA can efficiently compute principal components in high-dimensional feature spaces by means of nonlinear kernel functions. Therefore, the nonlinear problems are translated into the linear ones in the space high-dimension feature space. Although it has been proved that KPCA is superior to linear PCA for fault detection, the problem of fault identification theoretically has yet been a puzzle. A new fault detection and identification method based on the gradient arithmetic of kernel function and probabilistic neural network (PNN) for nonlinear system is developed. The gradient arithmetic of kernel function is used to extract the main features of faults firstly. Then, probabilistic neural network is used to identify the fault variables. To demonstrate the performance, the proposed method is applied to Tennessee Eastman processes. The simulation results under 15 fault modes of TE process show that the proposed method effectively identifies the source of various types of faults.