Research On Fault Diagnosis Of Auxiliary Inverter For Urban Rail Trains

With the rapidly development of our country’s urban modernization,the number of population is increasing,and the various problems that the city is facing are increasing.Among them,the traffic problem is a very important issue,and it is also a hot issue that the whole society is most concerned about.Accelerating the pace of urbanization led to the influx of people which moved to the center of the city and go to the other city,so the city traffic pressure is increasing.In order to sustain the development strategy,the construction and development of modern transportation has low carbon pollution standards,so city rail train is one of the important means of transportation.The development of urban rail transit in China has begun to take shape now.At the same time,the rapidly development of urban rail transit inevitably brings about some safety problems.The original fault diagnosis technology has been unable to meet the current needs.Based on the fault diagnosis of auxiliary inverter for urban rail transit,three main fault types,such as frequency conversion,pulse transients and voltage sag,are studied.Therefore,how to establish a fast and accurate new fault diagnosis model has become a hot research field.In this thesis,based on the fault diagnosis of auxiliary inverter for urban rail train,three kinds of main fault types,including frequency conversion,pulse transient and voltage sag,are diagnosed and studied.In this paper,a fault diagnosis model of auxiliary inverter for urban rail trains is established by using MATLAB.The LMD algorithm is used to extract feature vectors from the fault samples of the auxiliary inverter for urban rail trains.The fault samples are classified by using the PNN neural network and the SOM neural network.The experimental results show that the fault classification algorithm based on LMD method and PNN neural network,as well as the fault classification algorithm based on LMD and SOM neural network,is very accurate and fast.The diagnosis effect meets the requirements for the fault diagnosis of the urban rail train auxiliary inverter.