基于自注意力机制辅助分类生成对抗网络的轴承故障诊断

针对传统判别式轴承故障诊断算法在复杂工况下依赖人工特征提取、诊断效果不佳的问题，提出将生成式模型辅助分类生成对抗网络(auxiliary classifier generative adversarial network，ACGAN)用于轴承故障诊断研究。首先，通过快速傅里叶变换将轴承振动信号转为2维频域特征灰度图，设计卷积网络作为模型主体结构，添加批量归一化和LeakyReLU激活函数缓解梯度消失问题；其次，引入自注意力机制(self-attention mechanism，SA)，将数据中相距较远的特征相互关联建立新的SA-ACGAN模型，实现多分类场景下对原始数据分布特征的有效学习；最后，将模型应用于电机轴承进行对比验证，结果表明所提方法故障诊断准确率高达99.7％，并具有良好的鲁棒性和泛化性。

Bearing Fault Diagnosis Based on Self-attention Mechanism ACGAN

In this study, a generative model-auxiliary classifier generative adversarial network (ACGAN) for bearing fault diagnosis is proposed, as the traditional discriminative bearing fault diagnosis algorithm relies on artificial feature extraction and has poor diagnostic effects in complex working conditions. First, the bearing vibration signal is converted into a two-dimensional frequency-domain feature grayscale image using a fast Fourier transform. Subsequently, the convolution neural network is designed as the main structure of the model, and batch normalization and LeakyReLU activation functions are added to mitigate the gradient disappearance issue. Second, the self-attention (SA) mechanism is introduced to correlate the features far away from each other in the data. A new SA-ACGAN model is designed to realize the effective learning of the original data distribution features in the multi-classification scene. Finally, the model is applied to the motor bearing for comparison and verification. The results show that the fault diagnostic accuracy of the proposed method is as high as 99.7%, with considerable robustness and generalization.