基于一维卷积神经网络的实时道岔故障诊断

Real-Time Turnout Fault Diagnosis Based on One-Dimensional Convolutional Neural Network

针对道岔故障诊断系统实时性要求高、特征提取严重依赖于先验知识的问题,提出了一种基于一维卷积神经网络(1D-CNN)的道岔实时故障诊断方法。以S700k转辙机的功率曲线为例,建立一维卷积神经网络的结构模型,该模型将特征提取与故障分类融合为一体,优化了网络参数,同时使用正则化Dropout提高模型的泛化能力,采用t-SNE可视化方法,来反映模型提取特征的有效性。仿真实验表明:卷积层和池化层对原始时域信号的自适应特征提取,能较好地捕捉信号空间维度信息,降低模型的计算量,提高模型的抗噪性能,实现了端到端的实时故障诊断,并有效地提高道岔故障实时诊断的准确率。

In view of the high real-time requirements of turnout fault diagnosis system and the serious dependence of fea-ture extraction on prior knowledge,a real-time fault diagnosis method for turnouts based on one-dimensional convolutional neural network(1D-CNN)is proposed.Taking the power curve of S700k switch machine as an example,a one-dimensional convolutional neural network structure model is established.The model integrates feature extraction and fault classifica-tion,optimizes network parameters,and improves the generalization ability of the model by using regularized dropout,uses t-SNE visualization method to reflect the effectiveness of model extraction features.Simulation results show that the adap-tive feature extraction of the original time domain signals by the convolutional layer and the pooling layer can better cap-ture the spatial dimension information of signal,reduce the calculation amount of the model,and improve the anti-noise performance of the model,achieving the end-to-end real-time fault diagnosis,and effectively improve the accuracy of the real-time fault diagnosis of the turnout.