A Novel Description Method for Track Irregularity Evolution

Abstract

Track Irregularity has a significant influence on the safety of train operation. Due to the fact that the extremely large number of factors affect track irregularity， it is challenging to find a concise yet effective mathematical method to describe the evolution of track irregularity. In this paper, inspection data generated by GJ-4 track inspection cars from Jinan Railway Bureau in China were analyzed to identify the characteristics of track irregularity changes common to different mileage points. Based on these characteristics, a multi-stage linear fitting model to describe the pattern of track irregularity evolution over time was developed. The availability of new inspection data will make the model revise itself. In this sense, the model is a machine learning model. Finally, inspection data from the Beijing-Shanghai Railway Line (Jing-Hu Line) were used to verify the model.

Track geometry degradation and maintenance modelling: A review

“…The proposed model proved more accurate than models in similar previous work. A machine learning model based using a multistage framework is developed by Xu et al 54 who predicted changes in track irregularity over time. They defined different stages of track irregularity changes based on maintenance thresholds and linear regression was used to predict track degradation in each stage.…”

“…To deal with the non-linear (mainly exponential) degradation path in a maintenance cycle Data mining Xu et al, 49,50 Liu et al, 51 Bai et al, 53 Xu et al, 54 and Berggren 55 To short-term prediction of track geometry condition Path analysis Lyngby, 9 Hamid and Gross, 13 Bing, 14 Guler et al, 36 He et al 48 Westgeest et al 47 To identify the influencing factors on track geometry degradation Models with random coefficient…”

Railroad Track Deterioration Characteristics Based Track Measurement Data Mining

“…Based on experience of experts in regard to railroad track deterioration, Meier-Hirmer, Riboulet, and Sourget of the SNCF and Roussignol of the Université Paris-Est Marne-la-Vallée of France employed gamma stochastic process to fit the deterioration rate of track surface over 1000 meters long sections of track [10]. According to track deterioration characteristics, Xu et al proposed a multistage linear method to describe track condition deterioration processes between two adjacent maintenance activities [11]. Based on the research results in [17], Xu et al employed piecewise linear regression to develop a method for predicting mean values and standard deviations of track condition over unit sections in the future two or three months [12].…”