Abstract
It is difficult to apply deep learning theory to realize intelligent fault monitoring and diagnosis of rolling bearings. Therefore, a new method which is to combine a generative adversarial network (GAN) and a stacked sparse autoencoders (SSAE) is proposed in this paper. Specifically, the vibration signals of rolling bearings are through the fast Fourier transform (FFT) preprocessing so that those time domain data are transformed to frequency domain, then the GAN model is used for sample generation and expansion and a SSAE network completes automatic extraction of features and finally a softmax classification layer is added. The experimental results show that this proposed method can achieve excellent diagnostic accuracy.
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