Research On State Comprehensive Monitoring And Rail Track Irregularity Prediction Of Low Speed Maglev Train

State monitoring is an important part for maglev train to function safely. However, maglev train system is a complicated system involving many technology fields which not only contains multitudinous train based equipments, but also includes corresponding ground based establishments such as rail track, railroad switch and transformer substation. Therefore, in order to realize full scale monitoring of maglev system, a state comprehensive monitoring system must be built to monitor train based equipments and corresponding ground based establishment simultaneously. The state comprehensive monitoring system of maglev train consists of two subsystems: train based monitoring system and ground based monitoring system. This thesis illuminates the pattern of the two subsystems'communications and develops corresponding software.Because maglev train is a kind of rail vehicle, the status of the rail track is always an important factor which relates to the safety of the operation of maglev train. Based on the influence analysis of the rail track irregularity on the status of maglev suspension system, the influence of the rail track irregularity is predicted and estimated preliminarily by analyzing maglev train state data provided by the state comprehensive monitoring system.The main jobs done in this thesis are as follows:(1) The general structure of train based monitoring system is introduced. Using embedded Linux as the software platform, the software of train based monitoring display is developed with Object-Oriented methods.(2) Considering the special situations of different monitored objects in the ground based monitoring system, the data communication of its three subsystems is studied seperately. The status of train system, ground based transformer substation and railroad switch are monitored through CDMA net, MODBUS and Profibus-DP.(3) Through the modeling and simulation of maglev suspension system, the influence of several kinds of rail track irregularity on the status of maglev train suspension system is analyzed.(4) By monitoring and recording the suspension gap and current data when the train passes the same position several times, the extent of the gap fluctuation can be predicted with the predict method based on recurrent composite BP neural networks when the train will pass this position in the future. Further more, the influence of the irregularity of this position on the safety of maglev train is evaluated.