Fault diagnosis of rotating machinery based on BN-1DCNN model

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (19) : 302-308.

FENG Haonan1, FU Sheng2, XU Yonggang1

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Abstract

Here, to adaptively extract fault features of rotating machinery and realize intelligent fault diagnosis, a 1D convolutional neural network (CNN) model based on batch normalization, i.e., BN-1DCNN model was proposed. Because CNN is usually used in fields of 2D image or 3D video, the convolution kernel was changed to 1D one to realize direct convolution for the collected 1D vibration data, and the batch normalization layer was used to prevent over-fitting. Finally, the data collected using HZXT-008 small rotor test platform was used to verify the proposed BN-1DCNN model. The test results showed that the average diagnostic accuracy of the proposed BN-1DCNN model can reach 98.43%, this model is more stable than other models; BN-1DCNN model can realize adaptive fault feature extraction and fault type identification of rotating machinery under a large number of samples.

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deep learning / convolutional neural networks (CNN) / rotating machinery / fault diagnosis

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FENG Haonan, FU Sheng, XU Yonggang. Fault diagnosis of rotating machinery based on BN-1DCNN model[J]. Journal of Vibration and Shock, 2021, 40(19): 302-308

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