Track quality index threshold reasonability analysis method based on quantile regression

Abstract

The invention relates to the technical field of track analysis, in particular to a track quality index threshold reasonability analysis method based on quantile regression, which comprises: 1, a step of determining a reasonable standard value; 2, a step of preprocessing the data of a track inspection vehicle, comprising detection data selection, mileage correction and time sequence-based abnormal peak value processing; 3, a step of analysis of detection data: acquiring a multiple relationship between a half peak value and a standard deviation corresponding to peak value management and mean value management through linear regression of different quantiles. Accordingly, the reasonability of TQI management values and various standard deviations in mean value management of lines where different operation speeds per hour and different track board types are located is analyzed, and the analysis specifically comprises quantile regression statistical analysis, index fitting analysis and mean value suggestion management value analysis. According to the method, the track quality state can be better analyzed.

Description

technical field [0001] The invention relates to the technical field of track analysis, in particular to a method for analyzing the rationality of a track quality index threshold based on quantile regression. Background technique [0002] With the continuous acceleration of modern railways and the increase of traffic density, line equipment has been running at full capacity for a long time. Under such circumstances, the "planned maintenance" of regular inspection of track equipment can no longer meet the requirements fundamentally, and the "conditional maintenance" based on the track status and maintenance history has emerged as the times require, and is considered an advanced maintenance system. However, the rationality of the track quality index management value under different speeds and different states directly determines whether it can be used to evaluate the track state scientifically and accurately, which in turn affects the line maintenance and repair operations. ...

AI Technical Summary

Problems solved by technology

[0004] 1. There is no reasonable comprehensive research and application of the TQI management values ​​corresponding to different operating speeds, and most of them use simulation rather than actual measurement of track irregularities, which is difficult to reflect the data distribution under actual operating conditions;

[0005] 2. The impact of different sub-rail structures (such as track slab types) on track dynamic irregularity is ignored, and there is a lack of comparative analysis of track irregularity between different track slab types;

[0006] 3. Ignore the internal relationship between peak value management and mean value management in dynamic irregularity management, and lack the relationship between the half-peak value and standard deviation of the actual measurement data of each individual item of irregularity.

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