Condition monitoring and fault diagnosis have been critical for the optimal scheduling of machines, improving the system reliability and the reducing maintenance cost. In recent years, various of methods based on the deep learning method have made the great progress in the field of the mechanical fault diagnosis. However, there is a conflict between the massive parameters of the fault diagnosis networks and the limited computing resource of the embedded platforms. It is difficult to deploy the trained network on the small scale embedded platforms (like field programmable gate array (FPGA)) in the actual industrial situations. This seriously hinders the practical process of the intelligent fault diagnosis method. To address this problem, a new neural network compression method based on knowledge-distillation (K-D) and parameter quantization is proposed in this paper. In the proposed method, a large scale deep neural network with multiple convolutional layers and fully-connected layers i
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