  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.
