Kernel principal component analysis (KPCA) has shown excellent performance in monitoring nonlinear industrial processes. However, model building, updating, and online monitoring using KPCA are generally time-consuming when massive data are obtained under the normal operation condition (NOC). The main reason is that the eigen-decomposition of a high-dimensional kernel matrix constructed from massive NOC samples is computationally complex. Many studies have been devoted to solving this problem through reducing the NOC samples, but a KPCA model constructed from the reduced sample set cannot ensure good performance in monitoring nonlinear industrial processes. The performance of a KPCA model depends on whether the results of the eigen-decomposition of the reduced kernel matrix can well approximate that of the original kernel matrix. To improve the efficiency of KPCA-based process monitoring, this paper proposes randomized KPCA for monitoring nonlinear industrial processes with massive data. The proposed metho...