PDF(Pubmed)
Abstract：
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.
摘要：
作为铁路信号系统的三大户外部件之一,轨道电路在确保列车运行的安全性和效率方面起着重要作用。因此,当故障发生时，需要快速准确地发现故障原因并及时处理,避免影响列车运行效率和安全事故的发生。本文提出了一种基于多尺度注意力网络的故障诊断方法,它使用Gramian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地训练模型,完全提取不同尺度的特征，通过空间注意力机制融合空间特征信息。最后,实验是使用真实的轨道电路故障数据集进行的，故障诊断准确率达到99.36%,与经典和最先进的模型相比，我们的模型表现出更好的性能。并通过消融实验验证了所设计模型中的各个模块都起着关键作用。
