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.

基于生成对抗增强多尺度卷积神经网络模型的滚动轴承故障诊断方法，通过编码滚动轴承的时间域信号并生成特征图，扩展训练样本集并提取故障特征，应用注意力机制进行特征加权和深度特征提取，通过 softmax 分类器完成故障诊断，具有更好的泛化性能和抗噪性能。

基于生成对抗增强的多尺度卷积神经网络模型的滚动轴承故障诊断方法

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

Rolling bearing fault diagnosis has garnered increased attention in recent
years owing to its presence in rotating machinery across various industries,
and an ever increasing demand for efficient operations. Prompt detection and
accurate prediction of bearing failures can help reduce the likelihood of
unexpected machine downtime and enhance maintenance schedules, averting lost
productivity. Recent technological advances have enabled monitoring the health
of these assets at scale using a variety of sensors, and predicting the
failures using modern Machine Learning (ML) approaches including deep learning
architectures. Vibration data has been collected using accelerated
run-to-failure of overloaded bearings, or by introducing known failure in
bearings, under a variety of operating conditions such as rotating speed, load
on the bearing, type of bearing fault, and data acquisition frequency. However,
in the development of bearing failure classification models using vibration
data there is a lack of consensus in the metrics used to evaluate the models,
data partitions used to evaluate models, and methods used to generate failure
labels in run-to-failure experiments. An understanding of the impact of these
choices is important to reliably develop models, and deploy them in practical
settings. In this work, we demonstrate the significance of these choices on the
performance of the models using publicly-available vibration datasets, and
suggest model development considerations for real world scenarios. Our
experimental findings demonstrate that assigning vibration data from a given
bearing across training and evaluation splits leads to over-optimistic
performance estimates, PCA-based approach is able to robustly generate labels
for failure classification in run-to-failure experiments, and $F$ scores are
more insightful to evaluate the models with unbalanced real-world failure data.

滚动轴承故障诊断是近年来备受关注的课题，该研究探讨了振动数据在故障分类模型开发中的重要性，并提出了真实场景中的模型开发考虑因素。