Gearbox fault diagnosis based on feature learning of multi-channel one-dimensional convolutional neural network

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (20) : 55-66.

YE Zhuang，YU Jianbo

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Abstract

A new DNN model, called multi-channel one-dimensional convolutional neural network (MC-1DCNN) was proposed in order to solve the problem of using single-channel signal images as input, which can not express the fault characteristics hidden in the vibration signals effectively. Firstly, empirical mode decomposition (EMD) was used to obtain the multi-channel one-dimensional signals. Secondly, MC-1DCNN was constructed to perform the feature extraction of the multi-channel one-dimensional signals. Finally, stacked denoised autoencoder (SDAE) was embedded after the fully connected layer for further feature extraction and classification. The effectiveness of the proposed method was verified on the gearbox test rig. The experimental results show that the proposed method has better performance on feature extraction and fault diagnosis than typical DNNs and other regular classifiers.

Key words

gearbox fault diagnosis / multi-channel signal / convolutional neural network / stacked denoised autoencoder / feature learning

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YE Zhuang，YU Jianbo. Gearbox fault diagnosis based on feature learning of multi-channel one-dimensional convolutional neural network[J]. Journal of Vibration and Shock, 2020, 39(20): 55-66

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