%0 Journal Article %A CHI Yi %A CHEN Guangwu %T Real-Time Turnout Fault Diagnosis Based on One-Dimensional Convolutional Neural Network %D 2022 %R 10.3778/j.issn.1002-8331.2103-0348 %J Computer Engineering and Applications %P 293-299 %V 58 %N 20 %X In view of the high real-time requirements of turnout fault diagnosis system and the serious dependence of feature 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 classification, 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 adaptive feature extraction of the original time domain signals by the convolutional layer and the pooling layer can better capture 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. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2103-0348