The Research Of Principle Analysis Of Beijing-Tianjin Intercity Rail Switch Control Circuit And Fault Diagnosis Technology

Railway switch equipment, as the main equipment of signaling equipment, plays an important role in ensuring railway safety, which is significant for railway transport. However, the using environment of railway switch equipment is complex and the faults often become various, which will lead to a great threat to traffic safety if the faults cannot be handled in time. Therefore, the traditional fault diagnosis technology has been unable to meet the demand of current switch equipment diagnosis.Instead, diagnosing the faults of railway switch control circuit with new technology and new method has become a key issue to ensure traffic safety.Based on the project of Beijing Railway Bureau "Beijing-Tianjin Intercity Railway signal equipment scientific research project", this paper studied the German Siemens company of Beijing Tianjin Intercity Siwes2switch control circuit structure and working principle, which has practical value for normal use and maintenance of switch equipment. This paper mainly completed the following work:Firstly, the composition of the Beijing-Tianjin intercity railway Siwes2switch control circuit and the working principle of the various units are analyzed. Then the working principle of the action circuit and the showing circuit is analyzed, and the railway switch control circuit fault characteristics and corresponding treatment measures is summarized.Secondly, in view of the complexity of fault switch control circuit, this paper is based on the national and international research status of intelligent fault diagnosis technology, uses the strong ability of pattern recognition of BP neural network, choses the BP neural network technology to diagnosis fault to complete the design of diagnostic model.Finally, the railway switch control circuit fault sample data is used to verify the proposed model. And we improved and optimized the neural network model to achieve the diagnosis accuracy and diagnosis effect of higher by adjusting the training function and the number of neurons in the hidden layer nodes. By constructing a BP neural network model, we realized the intelligent fault diagnosis switch control circuit, and improved the efficiency and accuracy of fault diagnosis.