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As widely used process monitoring techniques, principal component analysis(PCA) and kernel PCA are limited to the application in time-invariant systems. To handle the time-varying and nonlinear characteristics of real processes, an improved KPCA based on block was proposed(BKPCA). It using a time-varying kernel matrix instead of a fixed kernel matrix to establish the principal component model, which was suitable for online model updating and much lower computational complexity. When applied to TE process monitoring, computer simulation demonstrates the effectiveness and efficiency of the proposed method.