Tao Weijie, Li Xiaowei, Liu Jianlei, Li Zheng
Department of Rail Transportation, Shandong Jiaotong University, Jinan, 250357, China.
Department of Cyberspace Security, Qufu Normal University, Jinan, 273165, China.
Sci Rep. 2024 Apr 17;14(1):8886. doi: 10.1038/s41598-024-59711-2.
As one of the three major outdoor components of the railroad signal system, the track circuit plays an important role in ensuring the safety and efficiency of train operation. Therefore, when a fault occurs, the cause of the fault needs to be found quickly and accurately and dealt with in a timely manner to avoid affecting the efficiency of train operation and the occurrence of safety accidents. This article proposes a fault diagnosis method based on multi-scale attention network, which uses Gramian Angular Field (GAF) to transform one-dimensional time series into two-dimensional images, making full use of the advantages of convolutional networks in processing image data. A new feature fusion training structure is designed to effectively train the model, fully extract features at different scales, and fusing spatial feature information through spatial attention mechanisms. Finally, experiments are conducted using real track circuit fault datasets, and the accuracy of fault diagnosis reaches 99.36%, and our model demonstrates better performance compared to classical and state-of-the-art models. And the ablation experiments verified that each module in the designed model plays a key role.
轨道电路作为铁路信号系统三大室外组成部分之一，在确保列车运行安全和效率方面发挥着重要作用。因此，当故障发生时，需要快速准确地找出故障原因并及时处理，以避免影响列车运行效率和发生安全事故。本文提出一种基于多尺度注意力网络的故障诊断方法，该方法利用格拉姆角场（GAF）将一维时间序列转换为二维图像，充分利用卷积网络在处理图像数据方面的优势。设计了一种新的特征融合训练结构来有效训练模型，充分提取不同尺度的特征，并通过空间注意力机制融合空间特征信息。最后，使用真实的轨道电路故障数据集进行实验，故障诊断准确率达到99.36%，与经典模型和当前先进模型相比，我们的模型表现出更好的性能。消融实验验证了所设计模型中的每个模块都起着关键作用。
