Rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model

arXiv:2403.15483v1 Announce Type: cross
Abstract: In order to solve the problem that current convolutional neural networks can not capture the correlation features between the time domain signals of rolling bearings effectively, and the model accuracy is limited by the number and quality of samples, a rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model is proposed. Firstly, Gram angular field coding technique is used to encode the time domain signal of the rolling bearing …

abstract accuracy adversarial arxiv convolutional neural network convolutional neural networks correlation cs.lg current diagnosis domain eess.sp features generative model accuracy network networks neural network neural networks quality samples scale solve type