Abstract:Gearbox fault diagnosis methods based on deep learning usually require a large amount of sample data for training, and these data are usually ideal experimental data without noise. However, due to the influence of complex environmental factors, a large number of effective fault samples may not be available and the sample data can be interfered with by noise, which affects the identification accuracy of fault diagnosis methods and the stability of diagnosis results. To improve the resistance to noise while achi… Show more
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