To solve the problem caused by kernel entropy component analysis (KECA) for selecting the same kernel parameters for different faults,a fault detection of industrial process based on ensemble kernel entropy component analysis (EKECA) was proposed.Firstly,a series of kernel functions with different width parameters were selected to project the nonlinear data into the kernel feature space.The eigenvalues and eigenvectors with large contribution to Rényi entropy were selected to obtain the transformed score matrix.The multiple KECAsubmodels were established.Secondly,the test data were projected onto each KECA submodel.The statistics of each KECA submodel were calculated to obtain the detection results.Finally,the detection results of each KECA submodel were turned into probability by Bayesian decision.The unified statistics were calculated by ensemble learning strategy and judged whether it exceeds the control limit.The algorithm was applied to a numerical example and the TE process.The simulation results show that the proposed algorithm can effectively improve the fault detection rate and reduce the false alarm rate compared with traditional EKPCA,KECA and other algorithms.This method solves the problem of selecting kernel parameters for different faults in the traditional KECA algorithm and provides a reference for improving the performance of KECA algorithm in fault detection of nonlinear industrial processes.