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Improved ACGAN for fault diagnosis based on GRU and convolutional block attention module
Received date: 2025-03-03
Revised date: 2025-03-26
Accepted date: 2025-05-03
Online published: 2025-07-03
Supported by
National Natural Science Foundation of China(62373029)
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.
Zhaoqin PENG , Qicong LI , Haini ZHANG , Hong WU , Yunpeng MA . Improved ACGAN for fault diagnosis based on GRU and convolutional block attention module[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2026 , 47(2) : 431929 -431929 . DOI: 10.7527/S1000-6893.2025.31929
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