Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks

Abstract

Bogie is the most important component in a running gear of a high-speed train. Advantages and disadvantages of its mechanical performance can directly influence safety and comfort of train running. Failure of key components on the bogie always leads to serious vibration and performance decrease of each train component, and can even cause serious accidents such as de-railing and overturning. Therefore, it is very necessary to adopt an efficient and accurate intelligent diagnosis method. Based on the previous researches, the paper proposed a deep neural network method to make systematic and complete diagnosis of bogie faults. Firstly, a deep neural network was used to diagnose standard bearing system faults, and the results were compared with those obtained by traditional neural networks such as FANN (Firefly Artificial Neural Network), PSONN (Particle Swarm Optimization Neural Network) and GANN (Genetic Artificial Neural Network) to highlight advantages of the deep neural network. Then, based on vibration data extracted by a multi-body dynamic model of the high-speed train, the deep neural network was used to diagnose 8 conditions of the bogie systematically. The fault diagnosis was repeated for 10 times. Results obtained for the 10 times present obvious advantages of the deep neural network which could obtain the average diagnosis rate of 98.3% and had the diagnosis standard deviation of 0.71. Finally, training process of fault diagnosis was extracted. Results show that the deep neural network could converge to a critical value at the quickest speed. All the above analysis results indicate that the deep neural network has irreplaceable advantages in intelligent diagnosis of bogies.