：With the rapid development of scientific technological progress and industrial scale, modern industrial monitoring field has entered the era of big data. It is an important task to automatically extract fault features from large scale raw vibration data and make fault diagnosis. In order to further improve the ability of the deep auto-encoder network to deal with the nonlinear problem, a deep neural network method based on kernel function and denoising auto-encoder (DAE) is proposed. The traditional denoising auto-encoder is improved by radial basis kernel function, and the kernel denoising auto-encoder (KDAE) is proposed. A deep neural network consisting of one KDAE layer and multiple AE layers is constructed to extract the fault features, and the softmax classification layer is added as classifier layer. The error back propagation algorithm is used to fine-tune the network parameters, and chaos firefly algorithm is used to optimize the undetermined parameters of the kernel parameters, then the fault diagnosis model is obtained. In response to the problem of poor generalization of traditional auto-encoder, L2 penalty items are added to the target function. It is verified that the proposed method is more accurate than the traditional denoising auto-encoder network through the typical failure test data of aero-engine intermediate bearing.

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