Fault Diagnosis of Multiple Railway High Speed Train Bogies Based on Federated Learning
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摘要:
单一线路高速列车转向架缺少足量故障数据特征，导致故障诊断模型泛化能力有限，为实现诊断多条线路高速列车的转向架故障，提出一种基于联邦学习的转向架全局故障诊断方法. 针对每条线路各自的转向架振动信号，在本地使用多尺度卷积融合算法，提取不同尺度下的故障特征并融合，在本地建立局部转向架故障诊断模型；在不泄露数据隐私的前提下，所有线路的故障诊断模型通过第三方聚合，调整模型参数权重，对故障诊断模型进行优化，最终实现多方联合训练转向架全局故障诊断模型. 实验表明：在联邦学习框架下，转向架全局故障诊断模型不仅对参与联邦建模的线路转向架故障诊断准确率达到93%以上，而且对于未参与联邦建模的线路转向架故障诊断率也可达到75%以上，给轨道交通中的“数据孤岛”问题提供了一种切实可行的方案.
Abstract:To solve the problem of limited generalization ability of fault diagnosis model caused by the lack of sufficient fault data characteristics of single railway high-speed train bogie, and to realize the diagnosis of bogie faults of multiple railway high-speed trains, a global bogie fault diagnosis method based on federated learning is proposed in this work. Firstly, according to the bogie vibration signals of each railway, the multi-scale convolution fusion algorithm is conducted locally to extract and fuse the fault features at different scales, and the bogie fault diagnosis model is established locally. On the premise of not divulging data privacy, the fault diagnosis models of all railways are aggregated by the third party, the weights of model parameter are adjusted, the fault diagnosis models are optimized, and finally the global fault diagnosis model of bogie is jointly trained by multiple railways. The experiments show that under the federated learning framework, the fault diagnosis accuracy of the global bogie fault diagnosis model is reach more than 93% for the railway participating in federated modeling, and more than 75% for the railway not participating in, which provides a practical scheme for the ‘data island’ problem in railway transportation.
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Key words:
- federated learning /
- fault diagnosis /
- bogie /
- high speed train
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图 1 武广线上转向架不同故障的振动信号时、频域分布
Figure 1. Time and frequency domain distribution of vibration signals from different faults of bogies on Wuhan−Guangzhou Railway
图 2 武广线上转向架不同运行工况的小波能量矩对比
Figure 2. Comparison of wavelet energy moment under different operation status of bogies on Wuhan−Guangzhou Railway
图 4 5条线路在发生AS + AD故障时的转向架振动信号波形
Figure 4. Waveforms of bogie vibration signal in case of combined faults of AS + AD on five railways
图 6 基于联邦学习的转向架故障诊断方法系统结构
Figure 6. Structure of bogie fault diagnosis method based on federated learning
图 7 基于改进联邦学习的转向架故障诊断方法流程
Figure 7. Flowchart of bogie fault diagnosis method based on improved federated learning
图 9 AS故障与AS + LD故障转向架振动信号波形比较
Figure 9. Comparison of bogie vibration signal waveforms between AS fault and AS + LD fault
表 1 转向架本地故障诊断模型结构及参数
Table 1. Structure and parameters of local bogie fault diagnosis model
结构类型 输入尺寸 卷积核尺寸 步长/步 通道数/个 卷积层 40000 × 1 6 1 16 卷积层 40000 × 16 6 1 16 最大池化 40000 × 16 2 卷积层 20000 × 16 6 1 16 多尺度卷积 20000 × 16 3/4/5 1 64 卷积层 20000 × 64 3/3/3 1 128 卷积拼接 20000 × 128 卷积层 60000 × 128 2 1 128 Dropout 60000 × 128 卷积层 60000 × 128 2 1 256 全局平均池化 60000 × 256 60000 1 1 全连接层 256 × 1 Softmax 7 
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表 2 转向架工况
Table 2. Operation status of bogie
运行工况 标签 正常运行 0 LD 故障 1 AD 故障 2 AS 故障 3 AD + LD 故障 4 AS + LD 故障 5 AS + AD 故障 6
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表 3 转向架故障诊断准确率
Table 3. Accuracy of bogie fault diagnosis
% 实验线路 训练模型 标签 平均 0 1 2 3 4 5 6 武广线 1D-CNN 87.7 85.4 90.4 77.8 83.3 82.2 87.6 84.9 SecureBoost 95.3 95.7 96.6 87.5 92.4 86.9 88.5 91.8 Multi-1D-CNN 98.2 96.8 98.4 94.5 96.2 94.9 99.1 96.9 郑西线 1D-CNN 81.2 83.3 84.5 73.2 76.3 75.4 81.7 79.4 SecureBoost 92.7 90.4 90.5 74.6 93.2 84.3 82.4 86.9 Multi-1D-CNN 94.3 95.5 96.3 87.4 94.8 91.3 95.5 93.6 京津线 1D-CNN 81.7 83.4 84.4 75.8 73.3 74.2 81.6 79.2 SecureBoost 93.3 93.7 95.6 76.5 89.4 84.9 81.5 87.8 Multi-1D-CNN 98.5 97.2 97.3 93.4 97.6 95.2 97.7 96.7
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表 4 泛化能力检测
Table 4. Generalization ability test
泛化实验 参与线路 检测线路 1 武广 胶济 2 武广 + 郑西 胶济 3 武广 + 郑西 + 京津 胶济 4 武广 + 郑西 + 京津 + 金山 胶济
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