Fault recognition of rolling bearing based on improved 1D convolutional neural network

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (3) : 216-223.

WANG Qi, DENG Linfeng, ZHAO Rongzhen

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Abstract

Fault recognition of rolling bearings is largely significant to prevent the deterioration of the rotating machinery system and to guarantee its safe operation. Aiming at the problem that the used intelligent diagnosis models usually have too many parameters and low recognition efficiency, a rolling bearing fault recognition method based on improved one-dimensional convolutional neural network(FRICNN–1D) is proposed in this paper. For one thing, the 1×1 convolution kernel is introduced to enhance the nonlinear expression ability of the one-dimensional convolutional neural network model; for another, the global average pooling layer is used to replace the fully connection layer in the traditional convolution neural network, so as to reduce the model parameters and the amount of calculation and prevent over fitting phenomenon. The experimental results show that the proposed method can accurately recognize different fault status of a real rolling bearing and has particular potential in engineering application.

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one-dimensional convolutional neural network / rolling bearing / fault recognition

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WANG Qi, DENG Linfeng, ZHAO Rongzhen. Fault recognition of rolling bearing based on improved 1D convolutional neural network[J]. Journal of Vibration and Shock, 2022, 41(3): 216-223

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