Fault diagnosis for rolling bearing of swashplate based on DCAE-CNN

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (11) : 273-279.

WAN Qiyang1, XIONG Bangshu1, LI Xinmin2， SUN Wei2

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Abstract

In order to solve the problem of large noise interference and poor fault diagnosis of the helicopter swashplate rolling bearing, we proposed a fault diagnosis method based on deep convolutional autoencoder (DCAE) and convolutional neural network (CNN). Firstly, the wavelet transform method is used to construct the time-frequency diagram of the vibration signals in different states. Then, the image denoising is performed on the time-frequency map using DCAE. Finally, the time-frequency map after denoising is classified by CNN. Diagnostic experiments were carried out using the bearing fault data of the research team and Case Western Reserve University, and the proposed method is compared with the CNN and the stacked denoise autoencoder (SDAE). The results show that the proposed method has higher fault recognition rate in high noise environment.

Key words

fault diagnosis / wavelet time-frequency diagram / deep learning / swashplate

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WAN Qiyang1, XIONG Bangshu1, LI Xinmin2， SUN Wei2. Fault diagnosis for rolling bearing of swashplate based on DCAE-CNN[J]. Journal of Vibration and Shock, 2020, 39(11): 273-279

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