Wavelet kernel entropy component analysis with application to industrial process monitoring

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 detection method based on wavelet transform and kernel entropy component analysis (WT-KECA) is proposed in this paper. Unlike other kernel feature extraction methods, this method chooses the best principal component vectors according to the maximal Renyi entropy rather than judging by the top eigenvalues and eigenvectors of the kernel matrix simply. Besides, it can denoise and anti-disturb due to the application of wavelet transform. The proposed method is applied to process monitoring in the Tennessee Eastman (TE) process and the fault identification is realized. The simulation results indicate that the proposed method is more feasible and efficient in comparing to KPCA method.