Application Of Integrating Factor Method Of Section Track Irregularity In Prediction Of Rail Track Irregularity

ABSTRACT:In recent years Chinese railway transportation system has obtained great development and china’s High-speed rail network is gradually forming, but the passenger and cargo transportation pressure on every main trunk hasn’t eased yet. Some methods, such as higher density, and higher speed and heavier haul, which could tap the rail line transportation potential take great challenges to rail-track engineering department who keep the rail network operating safety, smooth and efficient. This article hopes to improve the quality and efficiency of the rail-track engineering department repairing and maintenance work by using the track irregularity data which is detected by the dynamic track inspection car. According to the ideal repair and maintenance mode as status overhauling and preventive maintenance, to seek a better track irregularity management mode which is suitable the needs of rail-track engineering department. This article main content is as follows:Followed to investigate an average of railway track irregularity data which is detected from some of high-speed railway, speed railway, normal line and feeder railway in our country, and then preproccessed this track irregularity data.Put forward four requirements to track irregularity prediction methods. After comprehensive contrasting, we selected the railway section track irregularity integrating factor prediction method be the railway track geometry irregularity prediction model in this paper.Edited program TIP.1.0in FORTRAN language, the program can implement track inspection data preprocessing and model operation. Then we selected1.2km long sections to verify the prediction model and program on five typical railway lines which include high-speed ballastless track railway, high-speed ballast track railway, speed railway, normal lines and feeder railway. Five instances prove that the forecast model has high accuracy and practicability. At the same time according to the predication results we could more fully handle the change of track irregularity state and provide data to guide railway maintenance and duties.