Fault Identification of Railway Freight Train Bearings Based on One-dimensional AlexNet Convolution Neural Network
Railway freight trains are an important means of transportation for freight transportation in China.Wheel sets and rolling bear-ings are one of the key components of railway freight trains.Compared with other ordinary bearings,railway freight trains bearings are dif-ficult to calculate accurately due to their complex structure,large bearing capacity and fault feature frequency.Generally,the traditional intelligent diagnosis algorithm based on signal processing feature extraction and classifier requires high expert experience.Based on the AlexNet neural network,an intelligent diagnosis model based on the improved one-dimensional convolution neural network is proposed.The improved convolution kernel is used to enhance the nonlinear expression ability of the neural network.Taking the 353130B railway freight trains bearing as the research object,the laboratory bearing experimental machine was used to collect and identify four types of sig-nals,namely,normal bearing,outer ring fault,inner ring fault and roller fault.The research results showed that the recognition rate of the network for bearing fault could reach 99%,and it has excellent fault identification characteristics,indicating that the model has good gen-eralization value,and has important research value for railway freight trains bearing fault identification and detection.
万方数据
维普
