A study on rolling bearing fault diagnosis based on convolution capsule network

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (4) : 55-62.

YANG Ping，SU Yanchen，ZHANG Zhen

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Abstract

Aiming at the phenomenon that the diagnostic performance of many rolling bearing fault diagnosis methods based on deep learning will be degraded when the noise-containing signal was detected and load variation, a fault diagnosis method based on convolution capsule network was proposed.The model uses a convolutional network with two layers of large convolution kernels to extract features from the original one-dimensional time domain signal and send it to the capsule network.The output of the model was the diagnostic results for each type of fault.In order to verify the diagnostic performance of the model, the Case Western Reserve University bearing database was selected for experiment, and compared the model with common convolutional neural network and deep neural network.The experimental results show that this method still has superior diagnostic performance in the test signals with serious noise and load variation compared with other deep learning methods.

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convolution network / capsule network / fault diagnosis / rolling bearing

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YANG Ping，SU Yanchen，ZHANG Zhen. A study on rolling bearing fault diagnosis based on convolution capsule network[J]. Journal of Vibration and Shock, 2020, 39(4): 55-62

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