Steel rail profile periodic deviation characterization method based on profile fluctuation index

Abstract

The invention provides a profile fluctuation index-based steel rail profile periodical deviation characterization method, which comprises steel rail profile data acquisition and steel rail profile periodical deviation evaluation, and is characterized in that the steel rail profile data acquisition adopts continuous profile measurement equipment, profile data is longitudinally measured along a line, and the profile fluctuation index-based steel rail profile periodical deviation characterization is realized. According to the steel rail profile periodical deviation evaluation, profile data needs to be preprocessed through abnormal value recognition, and part of missing values, abnormal values and the like possibly existing in actual measurement data are removed to obtain a steel rail profile fluctuation index. According to the method, the single-point deviation value of the steel rail profile data and the spatial distribution characteristics along the line can be analyzed based on the continuous measurement data of the rail surface, and objective quantitative evaluation of the profile periodic deviation phenomenon is realized. The method provides a theoretical basis for controlling the periodic deviation phenomenon of the high-speed railway steel rail profile, and ensures stable and safe operation of the high-speed railway.

Description

Technical Field

[0001] The invention belongs to the technical field of railway rail detection and relates to a method for characterizing periodic deviation of rail profile based on a profile fluctuation index. Background Art

[0002] Rails are an important part of the track structure. They transmit train loads, provide support and guidance, and are key components to ensure the safe and smooth operation of high-speed EMUs. When the rail head profile is poor, it is easy to cause abnormal vibration of the EMU, affecting the comfort and safety of the high-speed EMU operation.

[0003] According to on-site investigations, with the increase in the service life of high-speed rails, periodic deviations in rail profile have appeared on some lines. The rail head profiles in these sections show periodic left-right asymmetry and front-to-back inconsistency along the longitudinal direction. When high-speed EMU trains pass through sections with periodic profile deviations, the periodic deviations in rail profile will act as an additional disturbance, making it easy for train shaking to occur, affecting the safe operation of high-speed railways.

[0004] At present, the evaluation of rail profile mainly uses vertical wear and side wear as the main basis for daily maintenance of rail profile. Conventional methods such as traditional manual inspection and single-point profiler inspection are unable to objectively evaluate the above-mentioned rail profile status. In addition, the lack of evaluation indicators for periodic deviation of high-speed railway rail profile has further affected the early identification and control of defects. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention proposes a method for characterizing the periodic deviation of rail profile based on the profile fluctuation index.

[0006] A method for characterizing periodic deviations of rail profile based on a profile fluctuation index, the method comprising rail profile data collection and rail profile periodic deviation evaluation, wherein the rail profile data collection adopts a continuous profile measurement device to measure profile data along the longitudinal direction of the line, and the rail profile periodic deviation evaluation requires preprocessing the profile data through outlier identification, removing some missing values, outliers, etc. that may exist in the actual measurement data to obtain the rail profile fluctuation index.

[0007] Wherein, the rail profile data collection collects the relative deviation data of the left and right rails {x i} and the profile data set of the lateral distance y between the working margins of the left and right rails and the center of the rail top in the evaluation section; when the profile measurement has the conditions for continuous measurement, the profile scattered point measurement accuracy shall not be less than 10μm, and the single measurement interval shall not be greater than 1m; when the profile measurement does not have the conditions for continuous measurement, manual single-point measurement shall be used for evaluation of the specific analysis section, with a single-point measurement accuracy of not less than 10μm and a measurement interval of not more than 2m.

[0008] The rail profile periodic deviation evaluation is performed by calculating the nominal fluctuation of the profile deviation. First, the relative deviation data {x i The sliding data window is used to calculate the root mean square value of the normal deviation of the profile using the profile data set of the horizontal distance y from the center of the rail top of the left and right rail working margins in the evaluation section. The calculation method is:

[0009]

[0010] Where N is the number of contours in the data window; s is the mileage of the current data window;

[0011] We further use a sliding data window of length N to calculate The standard deviation of the profile deviation is calculated, and the profile fluctuation at the horizontal distance y from the center of the rail top is P amp,y The calculation method of (s,N) is:

[0012]

[0013] Where μ is the average value of the sliding RMS value;

[0014] According to the actual contact situation of the rail surface, the profile fluctuation amount is weighted and the rail profile fluctuation index is obtained. The calculation method is:

[0015]

[0016] In the formula, k 0 is the benchmark value of the profile periodic deviation index, w y is the index weight at the lateral position y, a and b are the lateral boundary positions calculated by the profile periodic deviation.

[0017] Among them, the weight distribution w of each coordinate position on the rail surface is determined according to the actual wheel-rail light band distribution y According to the actual wheel-rail contact area, the wheel-rail contact area can be divided into three parts, namely, wheel-rail contact area A, non-working side non-contact area B, and working side non-contact area C. The weight w of each measuring point position is y Expressed as:

[0018]

[0019] Among them, n y1 n is the number of profile sampling points in the light band contact area; y2 is the number of sampling points of the non-contact area profile; ω 1 is the total contact area weight, ω 2 is the total weight of the non-contact area.

[0020] The present invention proposes a method for characterizing the periodic deviation of rail profile. Based on the continuous measurement data of the rail surface, the single-point deviation of the rail profile data and the spatial distribution characteristics along the line can be analyzed to achieve an objective quantitative evaluation of the periodic deviation phenomenon of the profile. This method will provide a theoretical basis for controlling the periodic deviation phenomenon of the rail profile of high-speed railways and ensure the smooth and safe operation of high-speed railways. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the technical route of the rail profile periodic deviation characterization method based on the profile fluctuation index.

[0022] Figure 2 Schematic diagram of sliding data window.

[0023] Figure 3 Schematic diagram of profile deviation calculation at different positions.

[0024] Figure 4 Schematic diagram of typical wheel-rail contact range.

[0025] Figure 5 This is a schematic diagram of the calculation results of the profile fluctuation index in the section with periodic deviation of rail profile.

[0026] Figure 6 This is a schematic diagram of the calculation results of the profile fluctuation index of a normal area. DETAILED DESCRIPTION

[0027] A method for characterizing periodic deviation of rail profile based on profile fluctuation index provided by the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0028] Figure 1 As shown, a method for characterizing periodic deviation of rail profile based on profile fluctuation index includes rail profile data collection and rail profile periodic deviation evaluation, wherein the rail profile data collection adopts continuous profile measurement equipment to measure profile data along the longitudinal direction of the line, and the rail profile periodic deviation evaluation needs to preprocess the profile data through outlier identification, and remove some missing values, outliers, etc. that may exist in the actual measurement data to obtain the rail profile fluctuation index.

[0029] Wherein, the rail profile data collection collects the relative deviation data of the left and right rails {x i} and the profile data set of the lateral distance y between the working margins of the left and right rails and the center of the rail top in the evaluation section; when the profile measurement has the conditions for continuous measurement, the profile scattered point measurement accuracy shall not be less than 10μm, and the single measurement interval shall not be greater than 1m; when the profile measurement does not have the conditions for continuous measurement, manual single-point measurement shall be used for evaluation of the specific analysis section, with a single-point measurement accuracy of not less than 10μm and a measurement interval of not more than 2m.

[0030] The rail profile periodic deviation evaluation is performed by calculating the nominal fluctuation of the profile deviation. First, the relative deviation data {x i} and the profile data set of the lateral distance y between the working margins of the left and right rails and the center of the rail top in the evaluation section, Figure 2 As shown, the sliding data window is used to calculate the root mean square value of the normal deviation of the profile. The calculation method is:

[0031]

[0032] Where N is the number of contours in the data window; s is the mileage of the current data window;

[0033] Figure 3 As shown, we further use a sliding data window of length N to calculate The standard deviation of the profile deviation is calculated, and the profile fluctuation at the horizontal distance y from the center of the rail top is P amp,y The calculation method of (s,N) is:

[0034]

[0035] Where μ is the average value of the sliding RMS value;

[0036] According to the actual contact situation of the rail surface, the profile fluctuation amount is weighted and the rail profile fluctuation index P is obtained. f , the calculation method is:

[0037]

[0038] In the formula, k 0 is the benchmark value of the profile periodic deviation index, w y is the index weight at the lateral position y, a and b are the lateral boundary positions calculated by the profile periodic deviation.

[0039] Among them, the weight distribution w of each coordinate position on the rail surface is determined according to the actual wheel-rail light band distribution y , Figure 4 As shown in Figure 2, according to the actual wheel-rail contact area, the wheel-rail contact area can be divided into three parts, namely, wheel-rail contact area A, non-working side non-contact area B, and working side non-contact area C. The weight w of each measuring point position is yExpressed as:

[0040]

[0041] Among them, n y1 n is the number of profile sampling points in the light band contact area; y2 is the number of sampling points of the non-contact area profile; ω 1 is the total contact area weight, ω 2 is the total weight of the non-contact area.

[0042] Figure 5 , Figure 6 The calculation results of the rail fluctuation index of the section with periodic deviation of rail profile and the normal section are shown respectively. It can be seen from the figure that the profile fluctuation index of the section with periodic deviation of rail profile is significantly greater than that of the normal section, which shows that the present invention can effectively quantify the phenomenon of periodic deviation of profile and improve the evaluation of rail profile in rail maintenance and repair.

[0043] The present invention proposes a method for characterizing the periodic deviation of rail profile based on the profile fluctuation index. The method is based on the characteristic data set of the three-dimensional geometry of the rail surface obtained by the continuous profile detection method, takes the deviation value of different rail surface characteristic positions as the evaluation object, and comprehensively considers the periodic characteristics of the rail profile; in the design process of the weight coefficient, the actual wheel-rail contact relationship is fully considered, and the actual rail surface contact area is introduced into the calculation process of the rail fluctuation index. The weight analysis method of the present invention has outstanding objectivity and scientificity, and fully reflects the actual rail surface contact state on site.

[0044] This method helps the railway engineering department to quantitatively evaluate the periodic deviation of the line rail profile, provide a basis for the formulation of rail maintenance strategies, and further improve the scientific maintenance evaluation system of my country's rails.

[0045] Finally, it should be noted that the above embodiments are only used to describe the technical solution of the present invention rather than to limit the technical method. The present invention can be extended to other modifications, changes, applications and embodiments in application, and therefore it is believed that all such modifications, changes, applications and embodiments are within the spirit and teaching scope of the present invention.

Claims

1. A method for characterizing periodic deviation of rail profile based on profile fluctuation index, characterized in that: The method includes rail profile data collection and rail profile periodic deviation assessment, wherein the rail profile data collection adopts continuous profile measurement equipment to measure profile data along the longitudinal direction of the line, and the rail profile periodic deviation assessment needs to pre-process the profile data through outlier recognition, and remove some missing values, outliers, etc. that may exist in the actual measurement data to obtain the rail profile fluctuation index.

2. The method for characterizing the periodic deviation of rail profile based on profile fluctuation index according to claim 1 is characterized in that: The rail profile data collection collects the relative deviation data of the left and right rails {x i } and the profile data set of the lateral distance y between the working margins of the left and right rails and the center of the rail top in the evaluation section; when the profile measurement has the conditions for continuous measurement, the profile scattered point measurement accuracy shall not be less than 10μm, and the single measurement interval shall not be greater than 1m; when the profile measurement does not have the conditions for continuous measurement, manual single-point measurement shall be used for evaluation of the specific analysis section, with a single-point measurement accuracy of not less than 10μm and a measurement interval of not more than 2m.

3. The method for characterizing the periodic deviation of rail profile based on profile fluctuation index according to claim 1 or 2, characterized in that: The rail profile periodic deviation evaluation is performed by calculating the nominal fluctuation of the profile deviation. First, the relative deviation data {x i The sliding data window is used to calculate the root mean square value of the normal deviation of the profile using the profile data set of the horizontal distance y from the center of the rail top of the left and right rail working margins in the evaluation section. The calculation method is: Where N is the number of contours in the data window; s is the mileage of the current data window; We further use a sliding data window of length N to calculate The standard deviation of the profile deviation is calculated, and the profile fluctuation at the horizontal distance y from the center of the rail top is P amp,y The calculation method of (s,N) is: Where μ is the average value of the sliding RMS value; According to the actual contact situation of the rail surface, the profile fluctuation amount is weighted and the rail profile fluctuation index P is obtained. f , the calculation method is: Where k0 is the reference value of the profile periodic deviation index, w y is the index weight at the lateral position y, a and b are the lateral boundary positions calculated by the profile periodic deviation.

4. The method for characterizing the periodic deviation of rail profile based on profile fluctuation index according to claim 3 is characterized in that: Determine the weight distribution w of each coordinate position on the rail surface according to the actual wheel-rail light band distribution y According to the actual wheel-rail contact area, the wheel-rail contact area can be divided into three parts, namely, wheel-rail contact area A, non-working side non-contact area B, and working side non-contact area C. The weight w of each measuring point position is y Expressed as: Among them, n y1 n is the number of profile sampling points in the light band contact area; y2 is the number of sampling points of the non-contact area contour; ω1 is the total weight of the contact area, and ω2 is the total weight of the non-contact area.

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