| 摘要 |
A fault recognition method and system based on neural network self-learning. The method comprises: 1) monitoring and collecting various set monitoring quantities of track traffic equipment, and converting the collected monitoring data into sample data applicable to neural network training; 2) classifying the sample data according to the types of faults, and obtaining a sample data set corresponding to each type of fault; 3) designing one neural network for each type of fault, then using a sample data set of the fault for training, and obtaining a recognition model of the type of fault; and 4) fusing recognition models of all the types of faults into one neutral network, and carrying out fault recognition on the monitoring data collected in real time. The method can calmly cope with complex equipment faults and train operation accidents. |
