基于GRU和卷积注意力的改进ACGAN故障诊断方法

doi:10.7527/S1000-6893.2025.31929

Abstract

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

CLC Number:

V448.25⁺1

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 Aeronautica et Astronautica Sinica, 2026, 47(2): 431929.

Figures/Tables 22

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网络层	关键参数
全连接层	输入特征维度64，输出维度128
反卷积层1	卷积核4，步长2
ReLU层1
反卷积层2	卷积核4，步长2，填充1
ReLU层2
CBAM层	输入通道16，缩放因子4，卷积核7，填充3
反卷积层3	卷积核（4，8），步长（2，8），填充（1，0）
Tanh层

网络层	关键参数
GRU层	输入特征维度64 输出维度128 层数1
CBAM层	输入通道4 缩放因子4 卷积核7 填充3
卷积层1	卷积核（4，8）步长（2，8）填充（1，0）
ReLU层1
正则化层1	丢弃概率0.4
卷积层2	卷积核4 步长2 填充1
ReLU层2
正则化层2	丢弃概率0.4
卷积层3	卷积核（4，8）步长（4，8）
ReLU层3
正则化层3	丢弃概率0.4
展平层
全连接层1	输出维度1
全连接层2	输出维度3

数据集	不平衡比例	正常	磨损	故障
训练集	5∶1	270	54	54
	10∶1	270	27	27
	20∶1	270	13	13
	50∶1	270	5	5
测试集	1∶1	36	36	36

方法	ED	WD	MMD	PCC
WGACGAN	2.510	0.178	0.103	0.281
ACGAN	3.790	0.272	0.137	0.092
WGAN+GP	2.537	0.198	0.200	0.103

方法	准确率/%
方法	5∶1	10∶1	20∶1	50∶1
WGACGAN	81.48	74.07	62.96	54.63
ACGAN	75.00	68.52	58.33	5 370
WGAN+GP	74.07	66.67	56.48	53.70
NoneGAN	67.59	62.04	52.78	40.74

Improved ACGAN for fault diagnosis based on GRU and convolutional block attention module

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消融实验	准确率/%
消融实验	5∶1	10∶1	20∶1	50∶1
实验1	81.48	74.07	62.96	54.63
实验2	75.93	68.52	59.26	51.85
实验3	72.22	64.81	54.63	50.93
实验4	71.30	62.04	53.70	50.00

GRU层数	准确率/%	训练时间/s	测试时间/s
1	78.70	61.38	0.023 9
2	75.93	69.01	0.023 6
3	74.07	72.08	0.024 3
4	76.82	81.68	0.026 1

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