基于联邦图网络的转子系统故障诊断方法

doi:10.7527/S1000-6893.2024.30611

Abstract

Abstract:

Considerable harsh operating environments of rotor systems， combined with the difficulty in fusing monitoring data from multiple sources and a tendency to form data islands， present substantial challenges to the health monitoring of rotor systems. This paper proposes a rotor system fault diagnosis method utilizing Federated Graph Convolutional neural Networks （FGCN） based on a genetic evolutionary composition. First， a federated migration learning framework， employing federated learning and graph neural networks， is established. The global model is derived by training local models on individual clients and aggregating them via a federated weighted average algorithm. This method facilitates data localization while securing the privacy and integrity of model parameters. Furthermore， to address the challenge of inadequate adaptive integration of multi-source sensor data， a genetic evolution composition method is introduced. This method dynamically adjusts the connectivity and weights among graph nodes during training， emulating the mechanisms of natural selection and genetic variation found in biological evolution. This approach significantly enhances the adaptability and flexibility of the multi-source sensor composition， thereby improving the accuracy of fault diagnosis. In conclusion， experimental validation on the rotor failure testbed dataset demonstrates that the proposed method effectively utilizes limited target domain data， achieving over 95% accuracy in fault diagnosis scenarios where the clients contain different number of faults classes.

Key words: graph neural networks, federated learning, fault diagnosis, genetic evolutionary graph construction, privacy preservation

CLC Number:

V267

Hui LI, Yinchao CHEN, Shaoshan SUN, Zhaoxin LIANG, Gang MAO, Bin QIAO, Yongbo LI. Fault diagnosis method of rotor system based on federated graph network[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(17): 530611-530611.

Figures/Tables 13

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References 24

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轴承工作状态	训练集样本个数	状态标签
正常状态	48	1
内圈点蚀1 mm	48	2
内圈点蚀8 mm	48	3
外圈点蚀1 mm	48	4
外圈点蚀8 mm	48	5
内圈裂纹0.2 mm	48	6
内圈裂纹16 mm	48	7
外圈裂纹0.2 mm	48	8
外圈裂纹16 mm	48	9

客户端	各客户端故障模式拥有情况
A	1，2，3，4，5，6，7，8
B	1，2，3，4，5，6，7
C	2，3，4，5，6，7，8，9
D	3，4，5，6，7，8，9
E	4，5，6，7，8，9

参数	数值
学习率/%	0.04
全局迭代次数	300
本地模型迭代次数	3
批次大小	36

评价指标	遗传进化构集中式训练	ResNet 18	VGG16
平均准确率/%	96.85	73.61	78.25
迭代一次运行时间/s	3.1	5.6	5.2
模型收敛所需迭代次数	25	70	74

方法	多传感器集中式训练准确率/%	多传感器联邦训练准确率/%	单传感器集中式训练准确率/%	单传感器联邦训练准确率/%
遗传进化构图	96.85	95.56	90.93	87.59
KNN构图	91.48	89.26	85.56	83.70
余弦相似度构图	92.37	92.22	88.15	87.04
有向路径构图	76.11	74.81	87.59	86.30

Fault diagnosis method of rotor system based on federated graph network

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