In this article, the nonlinear dynamic process monitoring method based on kernel independent component analysis (KICA) is developed. Compared to the Support Vector Machine (SVM) method, KICA is unsupervised and available for fault detection. Hence, in this article, KICA is used to detect faults. Because the dimension of the feature space is far less than the rank of kernel matrix, a basis in feature space is selected. Specifically, the basis in feature space is first constructed based on the similarity factor of data in one group in this article. A contribution plot is impossible, because the nonlinear mapping function from input space into feature space is unknown. Therefore, KICA is difficult for nonlinear fault diagnosis. In this article, once a fault is detected, the kernel-transformed scores from improved KICA will be directly introduced as the inputs of SVM to diagnose the fault. The classification rate of SVM plus improved KICA is higher than the classification rate of SVM plus KICA when the same number of independent components (nICs) is selected. The reason is that the negentropy in improved KICA plus SVM could take into account the more-useful information of original inputs than that of original KICA plus SVM. The training time of SVM plus improved KICA is shorter than that of SVM plus KICA, because the former attenuates the expensive computation load. The proposed approach is applied to the fault detection and diagnosis in the Tennessee Eastman process and a wastewater treatment process (WWTP). Applications indicate that the proposed approach effectively captures the nonlinear dynamic in the process variables and shows superior fault detectability, compared to conventional methods.