Rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model
<|reference_start|>Rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model: 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 and generate the feature map to retain the complete information of the vibration signal. Then, the re-sulting data is divided into a training set, a validation set, and a test set. Among them, the training set is input into the gradient penalty Wasserstein distance generation adversarial network to complete the training, and a new sample with similar features to the training sample is obtained, and then the original training set is expanded. Next, multi-scale convolution is used to extract the fault features of the extended training set, and the feature graph is normalized by example to overcome the influence of the difference in feature distribution. Finally, the attention mechanism is applied to the adaptive weighting of normalized features and the extraction of deep features, and the fault diagnosis is completed by the softmax classifier. Compared with ResNet method, the experimental results show that the proposed method has better generalization performance and anti-noise performance.<|reference_end|>
@article{zhou2024rolling,
    title={Rolling bearing fault diagnosis method based on generative adversarial
  enhanced multi-scale convolutional neural network model},
    author={Maoxuan Zhou, Wei Kang and Kun He},
    journal={arXiv preprint arXiv:2403.15483},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15483},
    primaryClass={eess.SP cs.LG}
}