An improved fault detection algorithm based on wavelet analysis and kernel principal component analysis

摘要

Original signal is decomposed by wavelet in different scales, the wavelet decomposition coefficients of the real signal are held, and the wavelet decomposition coefficients of the noise are eliminated, then the signal is reconstructed by inverse wavelet transform. Kernel PCA can eliminate the relativity of variables and extract the fault information better, the feature information of the pretreatment datum is obtained by KPCA, and the performance of fault detection is improved.