Communication Fault Prediction Method for Braking System of Urban Rail Vehicles Based on ST-CNN
In order to improve the driving safety of the braking system of urban rail vehicles and reduce the probability of traffic accidents,a communication fault prediction method for the braking system of urban rail vehicles based on ST-CNN is proposed.The paper analyzes the framework structure of the spatiotemporal convolutional neural prediction model,establishes a spatio-temporal sequence matrix,and obtains the normal patterns and changing trends of communication data.It establishes a convo-lutional neural network for communication feature extraction,extracts communication features for alarm types,and implements dimensionality reduction processing.Communication features of different alarm types are quantified through pooling operations,and a fully connected layer is used to convert different alarm feature vector values to complete communication fault prediction for the braking system.The experimental results show that the overall stability of the F1 value of the proposed method is above 0.97,and the prediction time is below 0.5 s.The predicted value is highly similar to the actual value,which can meet the actu-al needs of urban rail transit.
urban rail vehiclespatiotemporal matrixconvolutional neural networkempirical mode decompositionmapping function
万方数据
维普
