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Nonlinear fault diagnosis methods based on kernel function have great computation complexity for all training samples are introduced in model training. This paper proposes a novel nonlinear fault diagnosis method based on multiple sparse kernel classifiers (MSKC). In the proposed method, fault diagnosis is viewed as a nonlinear classification problem between normal data and fault data. Kernel trick is applied to construct multiple nonlinear classifiers for different fault scenes. In order to reduce the complexity of kernel classifier and improve classifier generalization capability, a forward orthogonal selection procedure is applied to minimize the leave one out classification error. Lastly, multiple sparse kernel classifiers are combined by weight voting technique to build a monitoring statistic. Simulation of a continuous stirred tank reactor system shows that the proposed method performs better compared with kernel principal component analysis method in terms of fault detection performance and computation efficiency.