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基于联邦学习的多线路高速列车转向架故障诊断

杜家豪 秦娜 贾鑫明 张一鸣 黄德青

杜家豪, 秦娜, 贾鑫明, 张一鸣, 黄德青. 基于联邦学习的多线路高速列车转向架故障诊断[J]. 西南交通大学学报, 2024, 59(1): 185-192. doi: 10.3969/j.issn.0258-2724.20220120
引用本文: 杜家豪, 秦娜, 贾鑫明, 张一鸣, 黄德青. 基于联邦学习的多线路高速列车转向架故障诊断[J]. 西南交通大学学报, 2024, 59(1): 185-192. doi: 10.3969/j.issn.0258-2724.20220120
DU Jiahao, QIN Na, JIA Xinming, ZHANG Yiming, HUANG Deqing. Fault Diagnosis of Multiple Railway High Speed Train Bogies Based on Federated Learning[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 185-192. doi: 10.3969/j.issn.0258-2724.20220120
Citation: DU Jiahao, QIN Na, JIA Xinming, ZHANG Yiming, HUANG Deqing. Fault Diagnosis of Multiple Railway High Speed Train Bogies Based on Federated Learning[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 185-192. doi: 10.3969/j.issn.0258-2724.20220120

基于联邦学习的多线路高速列车转向架故障诊断

doi: 10.3969/j.issn.0258-2724.20220120
基金项目: 国家自然科学基金(62173279,U1934221);四川省科技计划(2022YFG0247,2021JDJQ0012);中央高校基本科研业务费(2682021ZTPY027)
详细信息
    作者简介:

    杜家豪(1996—),男,博士研究生,研究方向为人工智能与模式识别,E-mail:djh@my.swjtu.edu.cn

    通讯作者:

    秦娜(1978—),女,副教授,博士,研究方向为智能信息处理、故障诊断、模式识别、联邦学习和智能系统,E-mail:qinna@swjtu.edu.cn

  • 中图分类号: TP391.41

Fault Diagnosis of Multiple Railway High Speed Train Bogies Based on Federated Learning

  • 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.

     

  • 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 3  1D-CNN基本结构

    Figure 3.  Basic structure of 1D-CNN

    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 5  本地转向架故障诊断模型网络结构

    Figure 5.  Network structure of local bogie fault diagnosis model

    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 8  SIMPACK转向架动力学仿真模型

    Figure 8.  Dynamic simulation model of bogie in SIMPACK

    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

    图 10  泛化能力检测结果

    Figure 10.  Results of generalization ability test

    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 × 16116
    卷积层40000 × 166116
    最大池化40000 × 162
    卷积层20000 × 166116
    多尺度卷积20000 × 163/4/5164
    卷积层20000 × 643/3/31128
    卷积拼接20000 × 128
    卷积层60000 × 12821128
    Dropout60000 × 128
    卷积层60000 × 12821256
    全局平均池化60000 × 2566000011
    全连接层256 × 1
    Softmax7
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    0123456
    武广线1D-CNN87.785.490.477.883.382.287.684.9
    SecureBoost95.395.796.687.592.486.988.591.8
    Multi-1D-CNN98.296.898.494.596.294.999.196.9
    郑西线1D-CNN81.283.384.573.276.375.481.779.4
    SecureBoost92.790.490.574.693.284.382.486.9
    Multi-1D-CNN94.395.596.387.494.891.395.593.6
    京津线1D-CNN81.783.484.475.873.374.281.679.2
    SecureBoost93.393.795.676.589.484.981.587.8
    Multi-1D-CNN98.597.297.393.497.695.297.796.7
    下载: 导出CSV

    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
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-02-22
  • 修回日期:  2022-05-19
  • 网络出版日期:  2023-03-27
  • 刊出日期:  2022-05-25

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