KEYWORDS: Data modeling, Convolution, Statistical modeling, Teeth, Signal processing, 3D modeling, Data conversion, Convolutional neural networks, Neural networks, Gallium nitride
The existing gearbox fault diagnosis models often need a lot of fault data and corresponding tags to complete the model training, but the actual fault data is often less and unevenly distributed. Aiming at the situation that the gearbox fault data is scarce and unevenly distributed, this paper proposes a fault diagnosis method based on the Deep Convolution Generative Adversarial Network (DCGAN). First, the original vibration signals collected in the early stage are processed through data processing to form a sample data set and input into the DCGAN model for confrontation training, and virtual samples with real sample characteristics are generated to expand the sample data set. Then, the convolution neural network model is constructed to complete the fault diagnosis and get the results. The results show that the model trained by this method is more accurate when generating a large amount of data, and the diagnostic effect is significantly better.
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