Efficient batch process monitoring is a challenging task because of the large data scale, the strong process nonlinearity, and the complicated local behaviours. To handle this issue, this paper presents a random nonlinear feature analysis method, called two-level localized ensemble random Fourier feature analysis (TLERFFA), for effective batch process fault detection. Considering the large data scale of the nonlinear batch process, the random Fourier feature analysis (RFFA) is introduced to develop the statistical model, which is more efficient than the traditional kernel modelling method in terms of computation loads. Furthermore, for overcoming the model uncertainty resulting from the random mapping weights, the ensemble learning based on Bayesian inference is used to integrate multiply RFFA models with different weight settings. To further mine the rich local information of batch process data, a two-level localization strategy is designed from the perspectives of the variables and the statistics. At the first level, the monitored variables are divided into several subgroups according to their maximal information coefficients, while at the second level, the local monitoring statistics are constructed based on the temporal-spatial neighbour analysis. Lastly, one case study on the benchmark process is given to demonstrate the effectiveness of the proposed methods.

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