关键词: 机电伺服系统, 门控循环单元, 卷积注意力模块, 故障诊断, 辅助分类生成对抗网络

Abstract:

Due to the limited number of fault data samples in practical applications of Electro-Mechanical Actuator （EMA）， the classification performance of fault diagnosis methods can be affected. To address the fault diagnosis problem in EMA with missing fault data， an improved Auxiliary Classifier Generative Adversarial Network （ACGAN） fault diagnosis method based on Gated Recurrent Unit （GRU） and Convolutional Block Attention Module （CBAM） is designed， which can stably generate high-quality data for each fault category. First， Wasserstein distance and gradient penalty are introduced into the ACGAN to optimize the loss function and improve the stability of adversarial training. Second， GRU and CBAM are added to both the generator and discriminator to enhance the ability of network to extract key region features and temporal features. This overcomes the limitations of convolutional networks in handling sequential data， and improves the quality of generated samples. Finally， parameter sharing between the classifier and discriminator is implemented， and the balanced dataset is used to fine-tune the classifier and further improve the diagnostic performance of the model. Based on the EMA test rig， an unbalanced dataset consisting of a large amount of normal data and a small amount of fault data is obtained， and the effectiveness and superiority of the proposed method are verified through comparison and ablation experiments.

Key words: electro-mechanical actuator, gated recurrent unit, convolutional block attention module, fault diagnosis, auxiliary classifier generative adversarial network