The combination of kernel principal components analysis (KPCA) and control limits (CLS) based on Gaussian distribution will undermine the performance. The fault detection and identification method for nonlinear process based on kernel principal components analysis-kernel density estimation (KPCA-KDE) is proposed. kernel density estimation (KDE) technology is adopted to estimate the CLS based on KPCA for nonlinear process monitoring. According to the detection rate of all 20 faults in KPCA and KPCA-KDE, KDE has a higher fault detection rate than the corresponding method based on Gaussian distribution. In addition, KDE-based detection delay is equal to or lower than other methods. By changing the bandwidth and the number of reserved pivots during the fault detection, KPCA records a larger FAR while the KPCA-KDE does not record any false alarms. The application on the Tennessee Eastman (TE) process shows that KPCA-KDE has better monitoring performance in sensitivity and detection time than KPCA based on Gaussian CLS.