Kernel entropy component analysis based process monitoring method with process subsystem division

Aiming at the features that modem industrial processes always have some characteristics of complexity and nonlinearity and the process data usually contain both Gaussion and non-Gaussion information at the same time, a new process performance monitoring and fault diagnosis method based on subsystem division and kernel entropy component analysis (Sub-KECA) is proposed in this paper. KECA as a new method for data transformation and dimensionality reduction, which chooses the best principal component vector according to the maximal Renyi entropy rather than judging by the top eigenvalue and eigenvector of the kernel matrix simply. Besides, it can be optimized and anti-disturb due to the application of subsystem division. The proposed method is applied to process monitoring of the Tennessee Eastman(TE) process. The positive simulation results indicate that this method is more feasible and efficient when comparing with KPCA method and original KECA method.