CN111307078A - Track irregularity detection method and system based on four-point chord measurement method - Google Patents
Track irregularity detection method and system based on four-point chord measurement method Download PDFInfo
- Publication number
- CN111307078A CN111307078A CN202010106317.9A CN202010106317A CN111307078A CN 111307078 A CN111307078 A CN 111307078A CN 202010106317 A CN202010106317 A CN 202010106317A CN 111307078 A CN111307078 A CN 111307078A
- Authority
- CN
- China
- Prior art keywords
- track
- point
- rail
- points
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/30—Measuring arrangements characterised by the use of optical techniques for measuring roughness or irregularity of surfaces
- G01B11/303—Measuring arrangements characterised by the use of optical techniques for measuring roughness or irregularity of surfaces using photoelectric detection means
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses a track irregularity detection method and system based on a four-point chord measuring method, wherein the method comprises the following steps: scanning and imaging the groove rail by a two-dimensional laser sensor, processing data by adopting a comprehensive filtering method, and extracting characteristic points of the rail surface of the groove rail; the microcontroller controls the two-dimensional laser sensor to synchronously trigger the coding wheel, and the mileage acquired by the coding wheel and the track gauge point coordinate calculated in the upper computer are stored in real time; the height irregularity and the rail irregularity of the rail are detected by a four-point chord measuring method. The invention combines the laser triangulation method and the four-point chord measuring method, solves the problem of detection errors caused by snake-shaped movement and vibration of the trolley in the detection process, realizes non-contact real-time dynamic accurate measurement of geometric parameters of the grooved rail, and improves the accuracy of data acquisition and the detection precision.
Description
Technical Field
The invention relates to the technical field of rail detection, in particular to a rail irregularity detection method and system based on a four-point chord measurement method.
Background
For rail transit, a rail is a running base and a running carrier, and the performance state of the rail is directly related to the running safety of a train. Tramcars adopt trough rails, but most rail detection equipment on the market aims at I-shaped rails, and rail detection equipment aiming at the trough rails is lacked.
The track irregularity refers to the geometric dimension deviation between two steel rails in the height direction and the left-right direction and the ideal positions of the steel rails, the existing chord measuring method is used for measuring the track irregularity, the deviation of the measured track direction value on a circular track is large, a non-contact measuring method is not used, a contact measuring method is adopted, a track gauge wheel is contacted with the inner wall of a groove-shaped track to measure the track gauge, the measurement is realized only by the contact of the track gauge wheel, and the detection precision and the detection speed are not ideal.
The existing four-point chord measuring method measures the value of the track irregularity parameter, directly determines the point measured by the laser distance measuring sensor as the track surface measuring point, and cannot avoid the problem that the accuracy and precision of the detected data are inaccurate due to the fact that the points taken by the laser distance measuring sensor are not on the same straight line caused by snake-shaped movement and vibration of the vehicle body; secondly, if the synchronous measurement of each fixed-point parameter of the left rail and the right rail is to be realized in real time, the number of the required sensors is large in terms of the rail direction and the height, which causes the difficulty in the installation and the distribution of the sensors; in the existing method for measuring the track irregularity by the four-point chord measuring method, two parameters of the track direction and the height in the track detection parameters are discussed, the subsequent development potential of the system is not considered, and other parameters such as abrasion, track distance and the like cannot be added to the original system.
Disclosure of Invention
In order to overcome the defects and shortcomings in the prior art, the invention provides a track irregularity detection method and system based on a four-point chord measuring method, the invention realizes the track irregularity detection of a groove-shaped track by combining a laser triangulation method and the four-point chord measuring method, acquires the whole track surface data by the laser triangulation method, and performs data processing by using a comprehensive filtering algorithm and a correction algorithm to obtain track surface measuring points, thereby improving the accuracy of the acquired data, avoiding the detection errors caused by snake-shaped motion and vibration of a trolley in the detection process, and improving the detection precision; laser sensor utilizes the less laser of coherence as the light source, adopts direct injection formula illumination direct mount and then scans whole rail surface directly over the rail surface for whole rail surface is all in the data acquisition scanning range of this sensor, and consequently a 2D laser sensor just can be complete with orbital data acquisition, and the sensor quantity of use is less, and the installation is simple and easy, and expansibility is strong, can realize the detection of other parameters of cell type rail to the further processing of detection data.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a track irregularity detection system based on a four-point chord measuring method, which comprises the following steps: the system comprises a two-dimensional laser sensor, a coding wheel, a rail inspection vehicle, a microcontroller, an upper computer and a power supply module;
the two-dimensional laser sensor, the microcontroller, the upper computer and the power supply module are all arranged on the rail inspection vehicle, and the coding wheel is arranged on a wheel of the rail inspection vehicle;
the two-dimensional laser sensor is used for scanning and imaging the groove-shaped rail and collecting the rail surface data of the groove-shaped rail;
the coding wheel is used for collecting the driving mileage of the rail inspection vehicle;
the microcontroller is used for controlling the coding wheel and the two-dimensional laser sensor to synchronously trigger, and transmitting the mileage data acquired by the coding wheel and the grooved rail surface data acquired by the two-dimensional laser sensor to the upper computer in real time;
the upper computer is used for receiving data uploaded by the microcontroller, calculating to obtain track gauge point coordinates, and detecting the irregularity of the track by adopting a four-point chord measuring method;
the power supply module is used for providing power for the two-dimensional laser sensor, the coding wheel, the rail inspection vehicle, the microcontroller and the upper computer.
As a preferred technical solution, the two-dimensional laser sensor is connected to an ethernet switch or a router, and the two-dimensional laser sensor transmits data through the ethernet.
The invention also provides a track irregularity detection method based on the four-point chord measuring method, which comprises the following steps:
scanning and imaging the groove rail by a two-dimensional laser sensor, processing data by adopting a comprehensive filtering method, and extracting characteristic points of the rail surface of the groove rail;
the microcontroller controls the two-dimensional laser sensor to synchronously trigger the coding wheel, and the mileage acquired by the coding wheel and the track gauge point coordinate calculated in the upper computer are stored in real time;
and detecting the unevenness of the track by adopting a four-point chord measuring method, wherein the unevenness of the track comprises the height unevenness and the rail direction unevenness of the track.
As a preferred technical solution, the data processing is performed by using an integrated filtering method, the integrated filtering method includes an amplitude limiting filtering method and a segment mean filtering method, and the amplitude limiting filtering specifically includes the following steps: setting a standard value range, comparing the measured data value with a specified standard value, and filtering the data value exceeding the standard value range;
the segmented mean filtering method comprises the following specific steps: the method comprises the steps of segmenting track data values according to a track profile of the groove-type track, and taking an average value of horizontal and vertical coordinates of points with the same number before and after each calculated point of the track profile as a value of the point according to the number and coordinates of sampling points of a two-dimensional laser sensor on the segmented track, wherein the number of the points before and after the same point in the same track segment is the same, and the points in different track segments are determined according to sampling point proportions.
As a preferred technical scheme, the two-dimensional laser sensor adopts a laser triangulation-based method for measurement, calculates to obtain a distance value corresponding to each point of a laser contour line on the grooved rail, and integrates the coordinates of the left rail and the right rail into the same coordinate system.
And as a preferred technical scheme, extracting the characteristic points of the rail surface of the groove-shaped rail, wherein the characteristic points of the rail surface of the groove-shaped rail adopt the characteristic points of the rail gauge points.
As a preferred technical scheme, the specific calculation steps for extracting the characteristic points of the gauge points are as follows:
acquiring the highest point of the rail top of the groove rail, selecting a plurality of sampling point values to perform data average calculation on the left and right of the highest point of the rail top to obtain a highest point mean value, and constructing a first linear equation according to the highest point mean value and the translation value;
taking points from the data points on the top surface of the rail, selecting a sampling point with the smallest output value of the ordinate and the first linear equation, and performing linear fitting by adopting a least square method to construct a second linear equation;
the coordinate of the left rail gauge point is M (x) calculated according to the first linear equation and the second linear equationa,ya) And the coordinates of the right rail gauge point are as follows: n (x)b,yb)。
As an optimal technical scheme, the method for detecting the irregularity of the track by adopting a four-point chord measuring method comprises the following specific steps:
selecting four track gauge points from the track gauge point coordinates obtained by calculation in the upper computer, wherein the four track gauge points are respectively expressed as: a (x1, y1), B (x2, y2), C (x3, y3) and D (x4, y4), the distances between two adjacent track gauge points are respectively expressed as m, n and k,
selecting A, B, C points, wherein the value of B point deviating from chord line AC is represented as BO', selecting A, B, D points, the value of B point deviating from chord line AD is BO, calculating chord measuring value f1 and chord measuring value f2, wherein the chord measuring value f1 and the chord measuring value f2 respectively represent the true values of the irregularity and the irregularity in the rail direction, and the specific calculation formula is as follows:
where x5 represents the abscissa of the groove track feature point and y5 represents the distance of the groove track from the two-dimensional laser sensor.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the invention adopts the technical scheme of combining the laser triangulation method and the four-point chord measurement method, solves the problem of waveform distortion caused by the fact that the short wave cannot be recovered by the traditional three-point chord measurement method, and achieves the technical effect of non-contact real-time dynamic accurate measurement of the geometric parameters of the grooved rail.
(2) The invention adopts the coding wheel as the mileage sensor and improves the coding wheel, solves the technical problem of large mileage error, controls the mileage error to be about 6mm, and achieves the technical effect of further improving the precision of geometric parameters.
(3) According to the technical scheme, the two-dimensional laser sensors are adopted to carry out real-time grooved rail surface data acquisition on the left rail and the right rail and realize data processing through a comprehensive filtering algorithm, so that the technical problem of detection errors caused by snake-shaped movement and vibration of the trolley in the detection process is solved, and the accuracy and the detection precision of the acquired data are improved.
(4) The invention adopts the technical scheme that the two-dimensional laser sensor is adopted to measure and analyze the whole track section to obtain the track measuring point, achieves the technical effect of increasing the number of other track parameters to be measured on the basis of realizing the calculation of two parameters of the track direction and the height without increasing the sensor, and improves the expansibility of a track inspection system.
Drawings
Fig. 1 is a schematic diagram of an overall implementation scheme of a track irregularity detection system based on a four-point chord measuring method in the embodiment;
FIG. 2 is a schematic structural diagram of a track irregularity detecting system based on a four-point chord measuring method according to the present embodiment;
FIG. 3 is a schematic diagram of track gauge characteristic points of the track irregularity detecting method based on the four-point chord measuring method according to the present embodiment;
FIG. 4 is a schematic diagram of the principle of measuring height irregularity by the four-point chord measuring method according to the present embodiment;
fig. 5 is a schematic diagram illustrating the principle of measuring the track irregularity by the four-point chord measuring method according to the embodiment.
The system comprises a 1-two-dimensional laser sensor, a 2-coding wheel, a 3-rail inspection vehicle, a 4-microcontroller, a 5-power supply module, a 6-upper computer, a 7-groove rail, a 8-left rail, a 9-right rail and a 10-gauge point.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Examples
As shown in fig. 1 and fig. 2, the present embodiment provides a track irregularity detecting system based on a four-point chord measuring method, including: the system comprises two-dimensional laser sensors 1, a coding wheel 2, a rail inspection vehicle 3, a microcontroller 4, a power supply module 5 and an upper computer 6, wherein the two-dimensional laser sensors 1 are installed below a cross beam of the rail inspection vehicle 3 and are fixedly suspended through nuts and knobs, the coding wheel 2 is installed on a wheel of the rail inspection vehicle 3, the microcontroller 4 and the power supply module 5 are both placed in a space in the cross beam, and the upper computer 6 is placed on a lapping table above the rail inspection vehicle 3;
in the embodiment, two-dimensional laser sensors are connected with an Ethernet switch, the two-dimensional laser sensors are horizontally and rigidly connected to two sides of a rail inspection vehicle, an encoding wheel is used as a rail running wheel, data of the encoding wheel is transmitted to an upper computer through an STM32 and synchronously triggered with the two-dimensional laser sensors, groove-shaped rail surface data and mileage data are transmitted to the upper computer in real time, and each parameter is calculated in the upper computer;
the microcontroller of the present embodiment employs an STM32 chip;
in the embodiment, the two-dimensional laser sensor is connected with the Ethernet switch by adopting the Ethernet, and the working environment of the rail inspection vehicle is at a rail laying point, compared with a wireless network, the Ethernet realizes network connection by pulling a wire to a central connection switch or a router, so that the connection reliability is high, the construction is convenient, the cost is low, the network speed is higher than that of the wireless network, and the data transmission speed is higher;
the power supply module of the embodiment provides a working voltage range from 2.0V to 3.6V, so that the STM32 can be compatible with mainstream battery technology, for example, a lithium battery and a nickel-metal hydride battery are adopted, and a special pin Vbat for a battery working mode is also arranged in a package; 5V voltage input is used, while the two-dimensional laser sensor needs 24V voltage input, so the lithium battery is boosted to 24V voltage through the booster and then supplied to the two-dimensional laser sensor.
The embodiment also provides a track irregularity detection method based on a four-point chord measuring method, which comprises the following steps:
s1: the two-dimensional laser sensor scans and images the groove-shaped rail by adopting a laser triangulation method principle, and calculates and extracts characteristic points of the rail surface of the groove-shaped rail, such as groove bottom points and rail gauge points;
however, due to environmental factors, dust, leaves and other impurities are often accumulated at the bottom of the groove, and interference is caused to detection, so that the track gauge point is selected as the track irregularity measuring point to effectively avoid interference caused by snake-shaped movement and vibration of the vehicle body during point taking;
the specific steps for acquiring the gauge points in this embodiment are as follows:
s11: the method comprises the following steps of collecting rail surface coordinate point data by adopting a two-dimensional laser sensor, processing the data by using a comprehensive filtering method and uploading the data to an industrial personal computer, wherein the comprehensive filtering method comprises amplitude limiting filtering and sectional mean filtering, and comprises the following specific steps:
(1) and (3) amplitude limiting and filtering: firstly, basic data (namely preliminary rail surface coordinate point data obtained by a two-dimensional laser sensor) is subjected to basic amplitude limiting filtering processing, namely, the range of the horizontal and vertical coordinates of the acquired data point is limited and is used as a standard value of amplitude limiting filtering (because the rail is relatively stable, the fluctuation is not too large even if the rail is changed), the measured data value is compared with the specified standard value every time, the data outside the standard value range is filtered, and primary interference data is preliminarily eliminated;
(2) and (3) segmented mean filtering: dividing the track data into five sections, namely a wide track top, a track waist left, a groove bottom, a track waist right and a narrow track top, according to the track profile of the groove-shaped track, wherein the two-dimensional laser sensor adopts a direct laser triangulation method, and light rays are vertically irradiated from a central point, so that the number of data points of each section is inconsistent, the number of sampling points of each section is inconsistent during mean value filtering, for example, the number of data points of the track top is more, and the central value is replaced by the average value of 20 points in the field; taking the average value of the horizontal and vertical coordinates of the same number of points before and after each calculated point of the track profile as the value of the point according to the number of sampling points of the two-dimensional laser sensor on the divided track section and the coordinates of the sampling points, wherein the number of the points before and after the same track section is the same, the points in different track sections are determined according to the proportion of the sampling points, and the ratio of the number of the selected points of the average filtering of each section to the total sampling points of the section can be determined to be 10% according to experience;
s12: and (3) coordinate conversion: the two-dimensional laser sensor is based on the laser triangulation principle, light emitted by a semiconductor laser generator forms an X-plane light curtain through a lens and is transmitted on a target object to form a contour line, the lens collects the light emitted by the target object and projects the light to a two-dimensional CMOS array, a formed object contour image is analyzed by an FPGA (field programmable gate array) and a signal processor of the two-dimensional laser sensor, so that a distance value (Y-axis direction) corresponding to each point of the laser contour line (X-axis direction) on the target object is calculated, the distance value is integrated on the same coordinate system through the process coordinate conversion, then the measured right rail abscissa and the distance between the two sensors are used as the right rail abscissa after the coordinate conversion, and the left rail and the right rail are integrated to the same coordinate system;
s13: as shown in fig. 3, the track vertices are extracted in the left track 8 and the right track 9: the tramcar adopts the groove rail, marks gauge point 10, gauge L1 on the groove rail, marks the range that the gauge position sets up at the railhead center, railhead circular arc department, under the railhead L2 become 14mm, and concrete step is:
firstly, find the left rail of the groove railHighest point of the roof y1maxThen at the highest point y1maxTaking 5 points from the left and right sides respectively to average data to obtain the highest point mean value
Will be straight line
By a downward translation of 14mm, i.e.
And (3) starting to get points to the right from the rail top surface data point, and finding out a plurality of points with the minimum difference between the ordinate and the y:
Δy1k，Δy1(k+1)，…，Δy1(k+m)
a straight line can be obtained by fitting these data with a least squares method:
ya＝kax+ba
and (3) converting a straight line:
and a straight line: y isa＝kax+baThe coordinates of the left rail gauge point M can be obtained through simultaneous solution: m (x)a,ya) Similarly, the coordinates of the right rail gauge point N obtained in this embodiment are: n (x)b,yb)；
S2: in the advancing process of the trolley, the two-dimensional laser sensor and the coding wheel are synchronously triggered, and the mileage acquired by the coding wheel and the track gauge point coordinate calculated in the upper computer are stored in real time;
s3: calculating track irregularity by adopting a four-point chord measuring method, accurately measuring mileage by using a coding wheel to further realize accurate positioning, selecting four track gauge points A (x1, y1), B (x2, y2), C (x3, y3) and D (x4, y4) from stored left track gauge points according to the mileage, wherein the distances between every two adjacent track gauge points are m, n and k respectively, and the longitudinal coordinates y1, y2, y3 and y4 are the distances from a laser sensor to the corresponding track gauge points, and substituting coordinate point data into the four-point chord measuring method;
s31: detecting the unevenness of the track:
as shown in fig. 4, when the rail inspection vehicle travels forward, the sensor passes through four gauge points A, B, C, D in sequence, A, B, C points are first taken, the value of B deviation from the chord line AC is BO ', A, B, D points are then taken, the value of B deviation from the chord line AD is BO, the difference between BO ' and BO is taken as a chord measurement value f1, that is, f1 is BO ' -BO, and the following geometric relationship is derived:
where y5 is the distance from the sensor of a known standard slotted track;
since 4 measurement points experience the same section of track irregularity and only have a constant phase difference, the transfer function of the four-point chord measuring method can be obtained by the above formula according to the linear property and the displacement property of Fourier transform:
selecting a construction formula:
wherein: n is the number of sinusoids; a, λ and
the amplitude, the wavelength and the phase angle of each sine wave are sequentially calculated;
s32: detecting rail irregularity of a rail:
as shown in fig. 5, A, B, C points are first taken during the detection process, and the distance between point B and chord AC is BO; and taking A, B, D points, taking the distance between the point B and the chord AD as BO, and taking the difference value between BO 'and BO as a detection value f2 of a four-point chord measuring method, namely f2 is BO' -BO, and deriving the following by geometric relationship as shown in the figure:
wherein x5 is the abscissa of the orbit feature point of the known standard orbit;
in the present embodiment, the actual values of the level unevenness and the track unevenness are directly expressed by chord measurement values, and since the recovery error is the smallest when m is 150mm, n is 750mm, and k is 50mm, the chord measurement form of m is 150mm, n is 750mm, and k is 50mm is selected in the present embodiment.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (8)
1. A track irregularity detection system based on a four-point chord measuring method is characterized by comprising: the system comprises a two-dimensional laser sensor, a coding wheel, a rail inspection vehicle, a microcontroller, an upper computer and a power supply module;
the two-dimensional laser sensor, the microcontroller, the upper computer and the power supply module are all arranged on the rail inspection vehicle, and the coding wheel is arranged on a wheel of the rail inspection vehicle;
the two-dimensional laser sensor is used for scanning and imaging the groove-shaped rail and collecting the rail surface data of the groove-shaped rail;
the coding wheel is used for collecting the driving mileage of the rail inspection vehicle;
the microcontroller is used for controlling the coding wheel and the two-dimensional laser sensor to synchronously trigger, and transmitting the mileage data acquired by the coding wheel and the grooved rail surface data acquired by the two-dimensional laser sensor to the upper computer in real time;
the upper computer is used for receiving data uploaded by the microcontroller, calculating to obtain track gauge point coordinates, and detecting the irregularity of the track by adopting a four-point chord measuring method;
the power supply module is used for providing power for the two-dimensional laser sensor, the coding wheel, the rail inspection vehicle, the microcontroller and the upper computer.
2. The track irregularity detection system based on the four-point chord measuring method is characterized in that the two-dimensional laser sensor is connected with an Ethernet switch or a router, and the two-dimensional laser sensor transmits data through the Ethernet.
3. A track irregularity detection method based on a four-point chord measuring method is characterized by comprising the following steps:
scanning and imaging the groove rail by a two-dimensional laser sensor, processing data by adopting a comprehensive filtering method, and extracting characteristic points of the rail surface of the groove rail;
the microcontroller controls the two-dimensional laser sensor to synchronously trigger the coding wheel, and the mileage acquired by the coding wheel and the track gauge point coordinate calculated in the upper computer are stored in real time;
and detecting the unevenness of the track by adopting a four-point chord measuring method, wherein the unevenness of the track comprises the height unevenness and the rail direction unevenness of the track.
4. The method for detecting track irregularity based on the four-point chord measuring method according to claim 3, wherein the data processing is performed by using a comprehensive filtering method, the comprehensive filtering method comprises an amplitude limiting filtering method and a segment mean filtering method, and the amplitude limiting filtering method comprises the following specific steps: setting a standard value range, comparing the measured data value with a specified standard value, and filtering the data value exceeding the standard value range;
the segmented mean filtering method comprises the following specific steps: the method comprises the steps of segmenting track data values according to a track profile of the groove-type track, and taking an average value of horizontal and vertical coordinates of points with the same number before and after each calculated point of the track profile as a value of the point according to the number and coordinates of sampling points of a two-dimensional laser sensor on the segmented track, wherein the number of the points before and after the same point in the same track segment is the same, and the points in different track segments are determined according to sampling point proportions.
5. The method for detecting the track irregularity based on the four-point chord measuring method according to claim 3, wherein the two-dimensional laser sensor is used for measuring by a laser triangulation method, a distance value corresponding to each point of a laser contour line on the grooved track is obtained through calculation, and coordinates of the left track and the right track are integrated into the same coordinate system.
6. The method for detecting the track irregularity based on the four-point chord measuring method as claimed in claim 3, wherein the feature points of the track surface of the groove-shaped track are extracted and the feature points of the track surface of the groove-shaped track are the feature points of the track gauge points.
7. The track irregularity detection method based on the four-point chord measuring method according to claim 6, wherein the specific calculation steps for extracting the characteristic points of the track gauge points are as follows:
acquiring the highest point of the rail top of the groove rail, selecting a plurality of sampling point values to perform data average calculation on the left and right of the highest point of the rail top to obtain a highest point mean value, and constructing a first linear equation according to the highest point mean value and the translation value;
taking points from the data points on the top surface of the rail, selecting a sampling point with the smallest output value of the ordinate and the first linear equation, and performing linear fitting by adopting a least square method to construct a second linear equation;
the coordinate of the left rail gauge point is M (x) calculated according to the first linear equation and the second linear equationa,ya) And the coordinates of the right rail gauge point are as follows: n (x)b,yb)。
8. The track irregularity detection method based on the four-point chord measuring method as claimed in claim 3, wherein the track irregularity detection method based on the four-point chord measuring method comprises the following specific steps:
selecting four track gauge points from the track gauge point coordinates obtained by calculation in the upper computer, wherein the four track gauge points are respectively expressed as: a (x1, y1), B (x2, y2), C (x3, y3) and D (x4, y4), the distances between two adjacent track gauge points are respectively expressed as m, n and k,
selecting A, B, C points, wherein the value of B point deviating from chord line AC is represented as BO', selecting A, B, D points, the value of B point deviating from chord line AD is BO, calculating chord measuring value f1 and chord measuring value f2, wherein the chord measuring value f1 and the chord measuring value f2 respectively represent the true values of the irregularity and the irregularity in the rail direction, and the specific calculation formula is as follows:
where x5 represents the abscissa of the groove track feature point and y5 represents the distance of the groove track from the two-dimensional laser sensor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010106317.9A CN111307078A (en) | 2020-02-21 | 2020-02-21 | Track irregularity detection method and system based on four-point chord measurement method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010106317.9A CN111307078A (en) | 2020-02-21 | 2020-02-21 | Track irregularity detection method and system based on four-point chord measurement method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111307078A true CN111307078A (en) | 2020-06-19 |
Family
ID=71151232
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010106317.9A Pending CN111307078A (en) | 2020-02-21 | 2020-02-21 | Track irregularity detection method and system based on four-point chord measurement method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111307078A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112033316A (en) * | 2020-07-20 | 2020-12-04 | 深圳市埃伯瑞科技有限公司 | Track waveform determining method and device, electronic equipment and storage medium |
| CN112902871A (en) * | 2021-02-04 | 2021-06-04 | 暨南大学 | Tramcar track irregularity detection method |
| CN112964195A (en) * | 2021-02-04 | 2021-06-15 | 暨南大学 | Power supply rail geometric parameter comprehensive detection method and system based on laser triangulation |
| CN113312715A (en) * | 2021-05-10 | 2021-08-27 | 暨南大学 | Tramcar groove rail distortion irregularity prediction method |
| CN113548068A (en) * | 2021-07-23 | 2021-10-26 | 中车长春轨道客车股份有限公司 | Rail surface irregularity detection device and detection method |
| CN113959354A (en) * | 2021-11-26 | 2022-01-21 | 中国铁道科学研究院集团有限公司 | Device and method for measuring structural deformation based on optical fiber sensing |
| CN114001695A (en) * | 2021-11-10 | 2022-02-01 | 济南维优科技开发有限公司 | Method for online detecting shape and contour of brake rail of anti-slip stopper of railway station yard |
| CN114754962A (en) * | 2022-03-10 | 2022-07-15 | 西南交通大学 | Method and device for determining falling shaft height, electronic equipment and storage medium |
| CN115388815A (en) * | 2022-07-08 | 2022-11-25 | 西南交通大学 | Static measurement method and device for irregularity of track functional part of magnetic suspension system |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101041359A (en) * | 2007-03-08 | 2007-09-26 | 重庆大学 | Method for measurement of railcar stroke encoding wheel |
| CN201161592Y (en) * | 2008-03-06 | 2008-12-10 | 北京光电技术研究所 | Rail smooth degree detecting vehicle |
| CN101487211A (en) * | 2009-01-19 | 2009-07-22 | 江西日月明铁道设备开发有限公司 | Existing line three-dimensional restriction measuring method |
| CN103115581A (en) * | 2013-01-23 | 2013-05-22 | 爱佩仪中测（成都）精密仪器有限公司 | Multifunctional rail measuring system and method thereof |
| CN104947555A (en) * | 2015-07-01 | 2015-09-30 | 中国神华能源股份有限公司 | Track irregularity detection method and system based on four-point chord measuring method |
| JP2015183428A (en) * | 2014-03-24 | 2015-10-22 | 三軌建設株式会社 | (laser type) mobile track three-point measuring device |
| CN105083320A (en) * | 2015-05-15 | 2015-11-25 | 西南交通大学 | Detection method and device of track regularity state |
| CN106595561A (en) * | 2016-12-08 | 2017-04-26 | 中南大学 | Track detector track direction irregularity measuring method based improved chord measuring method |
| CN107380204A (en) * | 2017-08-11 | 2017-11-24 | 暨南大学 | Orbit geometry parameter detects car and orbit geometry parameter detection method |
| CN107554553A (en) * | 2017-08-31 | 2018-01-09 | 常州路航轨道交通科技有限公司 | Track geometry irregularities detection method based on two-dimensional laser displacement transducer |
| CN109455197A (en) * | 2018-12-19 | 2019-03-12 | 暨南大学 | Orbit geometry parameter high-precision detecting method and orbit geometry parameter high-precision detection vehicle |
-
2020
- 2020-02-21 CN CN202010106317.9A patent/CN111307078A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101041359A (en) * | 2007-03-08 | 2007-09-26 | 重庆大学 | Method for measurement of railcar stroke encoding wheel |
| CN201161592Y (en) * | 2008-03-06 | 2008-12-10 | 北京光电技术研究所 | Rail smooth degree detecting vehicle |
| CN101487211A (en) * | 2009-01-19 | 2009-07-22 | 江西日月明铁道设备开发有限公司 | Existing line three-dimensional restriction measuring method |
| CN103115581A (en) * | 2013-01-23 | 2013-05-22 | 爱佩仪中测（成都）精密仪器有限公司 | Multifunctional rail measuring system and method thereof |
| JP2015183428A (en) * | 2014-03-24 | 2015-10-22 | 三軌建設株式会社 | (laser type) mobile track three-point measuring device |
| CN105083320A (en) * | 2015-05-15 | 2015-11-25 | 西南交通大学 | Detection method and device of track regularity state |
| CN104947555A (en) * | 2015-07-01 | 2015-09-30 | 中国神华能源股份有限公司 | Track irregularity detection method and system based on four-point chord measuring method |
| CN106595561A (en) * | 2016-12-08 | 2017-04-26 | 中南大学 | Track detector track direction irregularity measuring method based improved chord measuring method |
| CN107380204A (en) * | 2017-08-11 | 2017-11-24 | 暨南大学 | Orbit geometry parameter detects car and orbit geometry parameter detection method |
| CN107554553A (en) * | 2017-08-31 | 2018-01-09 | 常州路航轨道交通科技有限公司 | Track geometry irregularities detection method based on two-dimensional laser displacement transducer |
| CN109455197A (en) * | 2018-12-19 | 2019-03-12 | 暨南大学 | Orbit geometry parameter high-precision detecting method and orbit geometry parameter high-precision detection vehicle |
Non-Patent Citations (3)
| Title |
|---|
| 何思婷: "基于激光扫描传感器的车体姿态测量方法", 《自动化与仪表》 * |
| 李菲: "轨道几何变形检测中的轨距点定位算法研究", 《中国优秀硕士学位论文全文数据库（电子期刊）》 * |
| 毛晓君等: "基于四点弦测法的轨面不平顺检测及复原方法", 《华东交通大学学报》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112033316A (en) * | 2020-07-20 | 2020-12-04 | 深圳市埃伯瑞科技有限公司 | Track waveform determining method and device, electronic equipment and storage medium |
| CN112902871A (en) * | 2021-02-04 | 2021-06-04 | 暨南大学 | Tramcar track irregularity detection method |
| CN112964195A (en) * | 2021-02-04 | 2021-06-15 | 暨南大学 | Power supply rail geometric parameter comprehensive detection method and system based on laser triangulation |
| CN113312715A (en) * | 2021-05-10 | 2021-08-27 | 暨南大学 | Tramcar groove rail distortion irregularity prediction method |
| CN113548068A (en) * | 2021-07-23 | 2021-10-26 | 中车长春轨道客车股份有限公司 | Rail surface irregularity detection device and detection method |
| CN113548068B (en) * | 2021-07-23 | 2023-09-12 | 中车长春轨道客车股份有限公司 | Rail surface irregularity detection device and detection method |
| CN114001695A (en) * | 2021-11-10 | 2022-02-01 | 济南维优科技开发有限公司 | Method for online detecting shape and contour of brake rail of anti-slip stopper of railway station yard |
| CN114001695B (en) * | 2021-11-10 | 2023-10-03 | 济南维优科技开发有限公司 | Method for online detection of form profile of brake rail of anti-slip parking device of railway station |
| CN113959354A (en) * | 2021-11-26 | 2022-01-21 | 中国铁道科学研究院集团有限公司 | Device and method for measuring structural deformation based on optical fiber sensing |
| CN114754962A (en) * | 2022-03-10 | 2022-07-15 | 西南交通大学 | Method and device for determining falling shaft height, electronic equipment and storage medium |
| CN114754962B (en) * | 2022-03-10 | 2023-03-17 | 西南交通大学 | Method and device for determining falling shaft height, electronic equipment and storage medium |
| CN115388815A (en) * | 2022-07-08 | 2022-11-25 | 西南交通大学 | Static measurement method and device for irregularity of track functional part of magnetic suspension system |
| CN115388815B (en) * | 2022-07-08 | 2024-04-02 | 西南交通大学 | Method and device for measuring irregularity of track functional part of magnetic suspension system in static mode |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111307078A (en) | Track irregularity detection method and system based on four-point chord measurement method | |
| CN107607044B (en) | A kind of hollow on worn tread detection method based on laser displacement sensor | |
| CN107380204B (en) | Track geometric parameter detection vehicle and track geometric parameter detection method | |
| CN106871805B (en) | vehicle-mounted rail gauge measuring system and measuring method | |
| CN102297658B (en) | Three-dimensional information detection method based on dual laser | |
| CN104748685A (en) | Dynamic measurement method of geometric parameters of overhead contact system | |
| CN106091951B (en) | A kind of municipal rail train wheel rim parameter on-line detecting system and method | |
| CN104260751B (en) | The high-speed railway rail center line detecting system of a kind of Multi-sensor Fusion and detection method | |
| CN105674896A (en) | Catenary geometrical parameter dynamic detection method based on triangulation | |
| CN104792790A (en) | Tunnel state detection device and detection method | |
| CN102897192A (en) | Detection system for urban railway traffic contact rail and detection method thereof | |
| CN112172862A (en) | Multifunctional track detection system | |
| CN206781779U (en) | Laser scanning rail gauge measuring apparatus under a kind of track checking car | |
| CN110843826A (en) | Accurate measuring equipment for geometrical parameters of track | |
| CN109017867A (en) | Rail corrugation dynamic measurement method | |
| CN204495733U (en) | Tunnel state pick-up unit | |
| TW201936431A (en) | Rail three-dimensional detection system | |
| CN204944427U (en) | A kind of contact net geometric parameter detection of dynamic dolly | |
| CN211504015U (en) | Movable contact rail detection device | |
| CN110926417B (en) | Vehicle-mounted railway tunnel detection system based on machine vision | |
| CN112964195B (en) | Power supply rail geometric parameter comprehensive detection method and system based on laser triangulation method | |
| CN106184284B (en) | Railway wheelset diameter automatic measurement method and system based on the scanning of line laser multi-section | |
| CN209783519U (en) | Hybrid contact net contact rail geometric parameters detection device | |
| CN112781493A (en) | Online detection equipment for comprehensive geometric parameters of pantograph of train | |
| CN114935308B (en) | Train pantograph abrasion detection method based on curve registration algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |