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Research And Design On Poor Shunting Early Warning System For25Hz Phase Detecting Track Circuit

Posted on:2014-06-07Degree:MasterType:Thesis
Country:ChinaCandidate:Y K WangFull Text:PDF
GTID:2252330401976400Subject:Traffic Information Engineering & Control
Abstract/Summary:PDF Full Text Request
In our country, most of the goods loading areas exist the problem that handling lines’sections are inaccurate, especially for the station that transport the oil, coal, etc., and parts oftrack sections are under extremely bad working conditions, the leakage of the ballast isrelatively serious and the ballast resistance changes greatly, the number of "red-light-strap"failure and poor shunting failure is increasing, which influences the efficiency of railwaytransportation seriously, and brings the major hidden danger to the railway operation.Currently the microcomputer monitoring system is unable to early warning this kind of faultsaccurately that usually need to be repaired after failure, for the remote mountain areas and thelong tunnels where the signal maintenance conditions are poor, the maintenance haslimitations. Based on the problems faced in fields, to develop the fault early warning systemfor track circuit has the practical and immediate significance.The thesis mainly aims at the25Hz phase detecting track circuit to establish the poorshunting early warning system, and realizes the monitoring, diagnosis and early warning ofthe "red-light-strap" failure with poor shunting failure. The thesis mainly contains:Firstly,25Hz phase detecting track circuit is choosed as the research object, thecharacteristics of track circuit system are analyzed, and the main factors that affect the"red-light-strap" failure and poor shunting failure are selected as the early warning indicators.Combinning with the conditions of the fields, the warning deadlines are defined on the basisof the railway voltagemonitoring curves of microcomputer monitoring system. The varioustypes of track circuit equivalent link four-terminal network models are established, based on it,the Visual C++6.0development environment and the Matlab7.0software are used to compilethe analysis and calculation software for25Hz phase detecting track circuit system, using it todetermine the early warning deadlines data. The overall structure of early warning system isdesigned, and the implementation scheme is introduced from two aspects of modular andhierarchical.Then, the intelligence analysis and processing method is selected with the combinationof fuzzy pattern recognition and BP neural network algorithm. The track circuit poor shuntingearly warning models of algorithms are built and the correctness of the models is verified.According to the function demands of the data acquisition system, the reasonable dataacquisition circuit is designed. The hardware debugging results show that the circuit collectsthe track voltage data, trackside temperature and humidity values real-timely and quickly,then transmits them to the failure early warning computer.Finally, the thesis completes the designing of early warning system. The man-machineinterface of poor shunting early warning system for the25Hz phase detecting track circuit is carried out with Visual C++6.0development environment and the Access database, and thelaboratory debugging environment is built to adjust the system. The debugging results showthat the system can give a corresponding early warning signal timely and quickly when thetrack circuit is performed to have a tendency to failure, and notify the Signal&Telecommunication people to take some measures to eliminate the faults. The system not onlyincreases the reliability and security of the track circuit that worked in rainy or wet conditions,and lower labor strength of workers, but can be used as a powerful tool for ElectricityDepartment Dispatch and Command Center to process failures, and lays a foundation forfurther study on Dispatch and Command Decision-making System.
Keywords/Search Tags:25Hz phase detecting track circuit, Poor s hunting early warning, Fuzzypattern recognition, BP neural network, Data acquisition
PDF Full Text Request
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