CN108662983B - Method for detecting and calculating correlation coefficient of steel rail abrasion - Google Patents
Method for detecting and calculating correlation coefficient of steel rail abrasion Download PDFInfo
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- 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
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Abstract
The divisional application discloses a method for detecting and calculating the correlation coefficient of steel rail abrasion, which belongs to the detection field and is used for optimizing the detection and calculation process of the steel rail abrasion, and the technical key points are as follows: correlation coefficient rTijReflects two groups of characteristics TiAnd TjDegree of correlation of (2), rTijWhen the value of (A) is negative, the negative correlation of the two characteristics is shown; rTijWhen the value of (a) is positive, the positive correlation of the two characteristics is shown; when rTijWhen 0, there is no correlation between the two wear characteristics, when rTijThe closer to 1 the absolute value of (a), the higher the correlation degree of the two laser image features, the greater the redundancy generated, and the effect is: the calculation of the correlation coefficient of the rail abrasion detection calculation is realized.
Description
The application is a divisional application with the name of 'automatic detection method for rail abrasion' on application number 201610765942.8, application date 2016-08-30.
Technical Field
The invention belongs to the field of detection, relates to a steel rail abrasion automatic detection method, and particularly relates to a steel rail abrasion automatic detection method which is based on a word line laser image processing and a microprocessor and can effectively detect the surface abrasion depth and width of a steel rail head.
Background
The railway is a main artery for transportation, and compared with other transportation modes, the heavy haul railway transportation is rapidly developed all over the world due to the characteristics of large transportation volume and low cost. In rail equipment, rails are the most important components to directly bear the load of a train and guide the running of wheels. Whether the technical state of the steel rail is intact directly influences whether the train can run safely, stably and uninterruptedly at the specified speed. Railway locomotives transmit driving and braking forces through friction between wheel and rail, which can cause rail wear. Along with the high-speed, heavy-load and high-density operation of a locomotive, the abrasion of a steel rail is increased rapidly, and particularly the abrasion of the inner side surface of a small-radius curve outer rail is serious.
The detection technology of the rail abrasion goes through the processes of simple and visual detection of ruler tools, detection of a digitizer and the like. At present, in China, main methods such as contact clamp measurement, eddy current detection and optical triangulation are available in the aspect of rail abrasion detection, detection results often depend on attitudes of detection workers and experience of instrument use, and the methods have the problems of low detection efficiency, low detection precision and the like, and can not meet the development requirements of high speed at present. Although a detection device for detecting the abrasion of the surface of the rail by using laser is available at present, accurate step-by-step detection cannot be realized, and other detection methods only stay at the theoretical research level.
Disclosure of Invention
In order to optimize the detection and calculation process of the steel rail abrasion, the invention provides an automatic detection method of the steel rail abrasion, which has the technical key points that: the method comprises the steps of collecting a laser image of a steel rail, comparing the laser image with a complete steel rail laser light band image to judge whether the steel rail is abraded or not, judging whether the steel rail is abraded or not, selecting and extracting laser image characteristic quantities related to the abrasion quantity of the steel rail, and calculating to obtain the abrasion depth and/or width of the steel rail.
Has the advantages that: the laser image of the steel rail collected by the invention is compared with the complete image to judge whether the steel rail in the collected image is worn or not, when the steel rail is judged to be worn, the characteristic quantity is further selected to calculate to obtain the wear, the wear is judged in a fixed manner, then the thought of quantitatively calculating the depth and the width of the wear is carried out, and in the calculation process, the characteristic quantity is selected to optimize the calculation process of the depth and the width of the wear.
Drawings
Fig. 1 is a block diagram showing the structure of an automatic rail wear detection device according to embodiment 2;
FIG. 2 is a schematic view of a non-abrasive laser image;
FIG. 3 is a schematic view of a laser image with wear;
FIG. 4 is a graph showing the luminance curve and the diameter of the disk;
FIG. 5 is a labeled diagram of data points and feature quantities.
Detailed Description
Example 1: a rail abrasion automatic detection method comprises the steps of collecting a laser image of a rail, comparing the laser image with a complete rail laser light band image to judge whether the detected rail is abraded or not, judging whether the rail is abraded or not, carrying out laser image processing on the collected laser image, wherein the laser image processing comprises image preprocessing and image edge extraction, and after the laser image processing, selecting and extracting laser image characteristic quantities related to rail abrasion quantity to calculate rail abrasion depth and width. Wherein: the extracted laser image characteristic quantity related to the rail abrasion quantity is more than one of the following characteristic quantities:
1) length l of two linear portions of laser imageAAnd lB；
2) The width difference e of the two linear laser images;
3) longitudinal position difference z of the two linear laser images;
4) length l of transition section between two linear laser imagesC；
5) The inclination angle theta of a transition section between two sections of linear laser images;
the wear width and the wear depth are collectively called as the characteristic quantity of the rail wear, and one or more laser image characteristic quantities are selected and used for calculating the depth and the width of the rail wear;
when the depth and the width of the rail abrasion are calculated, not all the laser image characteristic quantities are selected for calculation, in order to optimize the calculation process, a combination of the laser image characteristic quantities is selected as basic calculation data for calculating the depth and the width of the rail abrasion, the combination is selected by firstly determining the laser image characteristic quantities related to the abrasion characteristics, selecting a preferred characteristic quantity from the laser image characteristic quantities, calculating correlation coefficients of the other laser image characteristic quantities and the preferred characteristic quantity, and calculating an average value of the correlation coefficients, wherein the average value is a threshold β for characteristic quantity selection, if an absolute value | rTij | β of the correlation coefficients between two laser image characteristics is equal to or more than a set of characteristic samples collected at fixed points on a rail, the set comprises the laser image characteristic quantities of the reaction of N fixed points, only one of the correlation coefficients is selected as the laser image characteristic quantity for rail abrasion judgment, namely, when the combination of the laser image characteristic quantities for judgment is selected, M is a characteristic sample set collected at the fixed points on the rail with the abrasion characteristics, the abrasion characteristics are selected as a measurement, and only one of the laser image characteristic quantity is selected as an effective parameter for the depth judgment of the rail abrasion characteristics of the laser image combination of the abrasion characteristics.
As an embodiment, the specific method for representing the correlation between the features by using the correlation coefficient as a measurement parameter is as follows: two different sets of laser image characteristics are used: t isi＝{tikK is 1,2, …, n and Tj＝{tjkAnd k is 1,2, …, n, where k denotes the kth test point and n test points are total, the correlation coefficient of the two groups of laser image features is defined as follows:
in the formula (I), the compound is shown in the specification,
and
are respectively two groups of characteristics TiAnd TjAverage value of (d):
and
correlation coefficient rTijReflects two groups of characteristics TiAnd TjDegree of correlation of (2), rTijWhen the value of (A) is negative, the negative correlation of the two characteristics is shown; rTijWhen the value of (a) is positive, the positive correlation of the two characteristics is shown; when rTijWhen 0, there is no correlation between the two wear characteristics, when rTijThe closer to 1 the absolute value of (A) is, the higher the correlation between the two laser image characteristics is, the greater the redundancy is, and the more the laser beam is reflected in the wear of the railIn the light image feature set, a threshold value β is set by using a correlation coefficient between the laser image feature quantities, and if the absolute value | rT of the correlation coefficient between two laser image features is within the threshold value, the absolute value | rT of the correlation coefficient between two laser image features is within the threshold valueijAnd | not less than β, wherein the two laser image characteristics are related redundant characteristics, and only one of the two laser image characteristics is selected as the laser image characteristic quantity for judging the rail wear.
In another embodiment, the method for determining the threshold β is to select a single laser image feature quantity as a preferred feature, and determine the possibility that the remaining laser image feature quantities are redundant features by, after determining the preferred feature, obtaining a correlation coefficient between the preferred feature quantity and the laser image feature set in the rail wear width and depth correlation, setting the average value of the correlation coefficient data as the threshold β, and determining the threshold β by:
wherein: where c is the number of feature quantities, l is the number of preferred feature quantities, and j is the number of candidate feature quantities.
Thus, in the above embodiment, the correlation coefficient between the features is obtained to obtain the possibility of redundancy between the features, the average value of the set of correlation coefficient data is set as the threshold β, and the threshold is used as the basis for judging whether the features are redundant, so that when judging that two features are redundant, only one of the redundant features is selected as the laser image feature for calculating the depth and width of wear, so as to optimize the calculation process, thereby obtaining the minimum feature combination.
As an example, a method of calculating the wear depth and width is specifically disclosed: length l of two linear portions of laser imageAAs a preferred feature for abrasion width detection, the longitudinal position difference z of the two linear laser images is used as a preferred feature for abrasion depth detection; calculating the length l of two linear parts of the laser image according to the correlation coefficient of the two groups of laser image characteristicsAAnd lBLongitudinal position difference of two linear laser images as characteristic quantity for abrasion width detectionz is taken as a characteristic quantity of abrasion depth detection, and an abrasion width calculation formula is as follows:
wherein l is the width of the unworn steel rail;
the abrasion depth calculation formula is as follows:
V＝z·tan 60°
as an embodiment, the image preprocessing comprises the steps of:
firstly, graying an image, drawing a histogram of a grayscale image, and finding out a grayscale concentration range;
then, the gray level of the gray level image is enhanced by using the following formula, so that the image is clearer;
wherein: a. b are respectively the left and right boundary points of the gray value centralized distribution in the gray image histogram, and x and y respectively represent the gray values before and after the gray enhancement.
As an embodiment, the method for image edge extraction includes the following steps:
taking a median filtering brightness curve in which pixel points are distributed along the horizontal direction, respectively taking continuous points with the maximum brightness gradient change at two sides of the maximum peak value of the curve, and taking midpoints p and q of the two groups of continuous points, and taking the distance between p and q as the diameter of a detection template;
setting the brightness of the image as f (i, j), and taking a circle s (c, r) in the image field as a detection template, wherein c is the center of the circle and the coordinate is (i, j)c,jc) R is a radius;
defining a set of pixel points in s (c, r), and recording the brightness sum of the pixel points in the circle s as:
make the center of the detection template atMoving in a small range in the horizontal direction, calculating the brightness sum of each pixel in each position detection template, wherein the brightness sum in the range and the maximum template circle center position are one pixel level ridge edge point of the bright strip, fitting a straight line by using a least square method, wherein the straight line is a word line laser image central line, and the small range is an image interval with the circle center as a central point and 2 times of radius at the left and right sides so as to obtain the length l of two linear parts of the laser imageAAnd lBLength l of transition section between two linear laser imagesC。
Example 2: as a supplement to the solution of embodiment 1, or as a separate embodiment: the abrasion mainly occurs on the head of the steel rail, the abrasion comprises top surface abrasion and side surface abrasion, and the two numerical values must be detected simultaneously during detection to comprehensively judge the abrasion degree of the steel rail. In the embodiment, a high-intensity narrow-beam linear laser beam is utilized, an angle of 60 degrees is formed between the plane where the laser and the linear laser beam are located and the surface of the measured steel rail, and the high-resolution area array CCD image sensor is located right above the laser image to shoot the laser image. The light beam image shows bending on the surface of the worn steel rail, and the width and the depth of the rail wear are determined by the position and the bending degree of the bending point.
The rail wearing and tearing automatic checkout device includes: the device comprises a linear laser, a CCD image sensor, a microprocessor, an execution unit, a display and acousto-optic alarm unit and an interface unit. The CCD image sensor collects laser images, the obtained image information is transmitted to the microprocessor for analysis and processing, the edge and the center of the image are extracted, a straight line is fitted, a complete steel rail laser light band image outline is formed, the image information is converted into steel rail outline parameters, the characteristic quantity of the steel rail outline is stored and compared with the complete steel rail parameters, and whether the steel rail has abrasion or not is judged. Continuing to detect the next point when no abrasion is generated; and (4) determining abrasion loss, including the depth and width of abrasion loss. The execution unit receives a control signal of the microprocessor, controls the advancing direction and the speed of the detection device, and adjusts the orientation of the CCD image sensor, the output end of the microprocessor is respectively connected with the LCD display and the acousto-optic alarm system, the LCD display is used for displaying the current position and the abrasion degree of the steel rail, and the acousto-optic alarm system is used for prompting that the current position of the steel rail is abraded and needs to be repaired. The interface unit is used for exchanging information with an upper computer, and the upper computer can further finely process the image of the abrasion position and determine the accurate abrasion amount.
The image preprocessing is a preprocessing stage of laser image edge extraction, firstly graying an image, drawing a histogram of the grayscale image, finding out a grayscale concentration range, and performing grayscale enhancement on the grayscale image by using a formula (1) (wherein a and b are respectively left and right boundary points of the grayscale value concentration distribution in the histogram of the grayscale image, and x and y respectively represent grayscale values before and after grayscale enhancement) so as to make the image clearer.
The edge detection of the laser image of the first line adopts a roof ridge edge detection method. The edge detection method based on the brightness of a single pixel point has poor noise resistance, and in order to reduce the interference of image noise, the brightness sum of each pixel point in a certain area is used as a ridge-shaped edge judgment basis. Because the circle has isotropy and is not influenced by the ridge edge direction, the invention adopts a ridge edge detection method by a disc method. And moving a disc detection template with a proper size in a certain range at two sides of the laser image of the same line, wherein when the brightness and gradient change of each pixel point in the template meet certain requirements, the central point of the template is a ridge-shaped edge point.
Any one median filtering brightness curve with pixel points distributed along the horizontal direction is taken, continuous points with the maximum brightness gradient change are taken out from two sides of the maximum peak value of the curve respectively, the middle points p and q of the two groups of continuous points are taken, and the distance between the points p and q is taken as the diameter of the detection template, as shown in fig. 3.
Setting the brightness of the image as f (i, j), and taking a circle s (c, r) in the image field as a detection template, wherein c is the center of the circle and the coordinate is (i, j)c,jc) And r is a radius. Defining a set of pixel points within s (c, r):
and recording the brightness sum of the pixel points in the circle s as:
and moving the circle center of the detection template in a small range in the horizontal direction, and calculating the brightness sum of each pixel in each position detection template, wherein the position of the circle center of the template with the maximum brightness sum in the range is a pixel-level ridge edge point of the bright strip. And fitting a straight line by using a least square method, wherein the straight line is the central line of the laser image of the word line. The detected edge points and the fitted straight line are shown in fig. 4.
And further extracting characteristic quantities of the laser image related to the rail abrasion loss, including a selection method of the characteristic quantities and determination of a threshold value.
The width and the depth of the rail abrasion, which are mainly related by the invention, can be determined through the bending degree of the laser image, and the following characteristic quantities can be used for selection:
1) length l of two linear portions of laser imageAAnd lB；
2) The width difference e of the two linear laser images;
3) longitudinal position difference z of the two linear laser images;
4) length l of transition section between two linear laser imagesC；
5) And the inclination angle theta of the transition section between the two linear laser images.
One or more characteristic quantities can be selected for judging the depth and the width of the wear of the steel rail, and when the combination of the characteristic quantities for judging is selected, different types of characteristics are required to have obvious difference, so that redundant characteristic interference judgment is avoided. Assume M is a set of feature samples collected at fixed points on a rail with wear, the set containing wear features for n fixed points. And selecting the correlation coefficient as a measurement parameter, wherein the parameter can reflect the similarity between the characteristics and is used for searching the combination of the minimum characteristic quantity which can effectively judge the wear width and the depth of the steel rail. Two different sets of wear characteristics were set as: t isi＝{tikK 1,2, n, and Tj＝{tjkAnd k is 1,2,., n }, where k denotes the kth test point and n test points are total, the correlation coefficients of the two groups of features are defined as follows:
in the formula (I), the compound is shown in the specification,
and
are respectively two groups of characteristics TiAnd TjAverage value of (d):
and
correlation coefficient rTijReflects two groups of characteristics TiAnd TjDegree of correlation of (2), rTijWhen the value of (A) is negative, the negative correlation of the two characteristics is shown; and if the value is positive, the positive correlation of the two characteristics is shown. When rTijWhen 0, there is no correlation between the two features. When rT is thenijThe closer the absolute value of (a) is to 1, the higher the degree of correlation between the two features is, and the greater the redundancy that can be generated in this case.
In the feature set of rail wear, a threshold value β is set using the correlation coefficient between the respective feature quantities, and if the absolute value | rT of the correlation coefficient between two of the feature quantities is greater than the threshold value | rTijAnd | is equal to or more than β, the two characteristics are related redundant characteristics, and only one of the characteristics can be selected as the characteristic quantity for judging the rail wear.
After the preferred features are determined, a correlation coefficient between the preferred feature quantity and the rail wear width in a related feature set is obtained through calculation, and the mean value of the group of data is set as a threshold β, wherein the average value is shown in formula (4):
and after the characteristic quantity is determined, calculating the steel rail abrasion quantity of the detection position, storing and displaying, and starting an audible and visual alarm device when the abrasion exceeds the limit.
Under the off-line condition, the interface unit is used for exchanging information with the upper computer, and the upper computer can further finely process the image of the abrasion position to determine the accurate abrasion amount. By adopting the technical scheme, the automatic steel rail wear detection device provided by the embodiment has the beneficial effects that the image processing method is adopted, the control of the microprocessor is separated from the control of a PC, and the device can automatically run under the setting of an operator. The device has certain perfection and effectiveness, is convenient for detection personnel to use, and has the advantages of simple operation, accurate detection result and low production and manufacturing cost.
The above description is only for the purpose of creating a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the technical scope of the present invention.
Claims (1)
1. A method for detecting and calculating a correlation coefficient of steel rail abrasion is characterized in that the method for calculating the correlation coefficient comprises the following steps:
two different sets of laser image characteristics are respectively: t isi＝{tikK is 1,2, …, n and Tj＝{tjkAnd k is 1,2, …, n, where k denotes the kth test point and n test points are total, and the correlation coefficient of the two groups of laser image features is determinedIt is defined as follows:
in the formula (I), the compound is shown in the specification,
and
are respectively two groups of characteristics TiAnd TjAverage value of (d):
and
；
correlation coefficient rTijReflects two groups of characteristics TiAnd TjDegree of correlation of (2), rTijWhen the value of (A) is negative, the negative correlation of the two characteristics is shown; rTijWhen the value of (a) is positive, the positive correlation of the two characteristics is shown; when rTijWhen 0, there is no correlation between the two wear characteristics, when rTijWhen the absolute value of (1) is closer to 1, the correlation degree of the two laser image characteristics is higher, and the generated redundancy is higher;
two groups of different laser image characteristics, wherein one group is a preferred laser image characteristic quantity selected from the laser image characteristic quantities, and the other group is the other laser image characteristic quantities in the laser image characteristic quantities;
the characteristic quantity of the laser image includes
1) Length l of two linear portions of laser imageAAnd lB；
2) The width difference e of the two linear laser images;
3) longitudinal position difference z of the two linear laser images;
4) length l of transition section between two linear laser imagesC；
5) And the inclination angle theta of the transition section between the two linear laser images.
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| CN107194921B (en) * | 2017-05-19 | 2020-04-07 | 华夏高铁技术有限公司 | Automatic identification method and device for rail surface light band of steel rail |
| CN107264570B (en) * | 2017-07-25 | 2019-07-05 | 西南交通大学 | Rail light strip distribution detection device and method |
| JP2019060711A (en) * | 2017-09-26 | 2019-04-18 | 横浜ゴム株式会社 | Life prediction method for conveyer belt |
| CN109816645B (en) * | 2019-01-18 | 2020-11-17 | 创新奇智(广州)科技有限公司 | Automatic detection method for steel coil loosening |
| CN111457851B (en) * | 2020-04-14 | 2021-11-23 | 中国铁建重工集团股份有限公司 | Shield tail clearance measurement system and method for shield machine |
| CN112215264B (en) * | 2020-09-23 | 2022-04-12 | 西南交通大学 | Steel rail abrasion detection method based on steel rail light band image |
| CN113882199B (en) * | 2021-10-09 | 2023-09-19 | 重庆交通大学 | Visual detection device and method for waveform abrasion of steel rail |
| CN118898559A (en) * | 2024-08-15 | 2024-11-05 | 南京弘云茂建筑工程有限公司 | Intelligent identification system for working status of thermal power generator |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1776364A (en) * | 2005-11-22 | 2006-05-24 | 北京航空航天大学 | Rail wear laser vision dynamic measurement device and measurement method |
| CN101144714A (en) * | 2007-10-26 | 2008-03-19 | 北京航空航天大学 | A vehicle-mounted dynamic measurement device and method for comprehensive parameters of rail wear |
| CN101532827A (en) * | 2009-04-15 | 2009-09-16 | 北京航空航天大学 | Deviation correction method for measurement of rail wear based on laser vision |
| CN101576375A (en) * | 2009-05-21 | 2009-11-11 | 北京航空航天大学 | Fast processing method of laser vision image of steel rail wear |
| CN202320395U (en) * | 2011-08-04 | 2012-07-11 | 大连民族学院 | Steel rail abrasion detection device |
| CN203274695U (en) * | 2013-05-24 | 2013-11-06 | 武汉铁路局武汉大型养路机械运用检修段 | Steel-rail wear measurement device based on machine vision |
Family Cites Families (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU4879579A (en) * | 1978-07-11 | 1980-03-06 | Commonwealth Scientific And Industrial Research Organisation | Profile measurement |
| JPH0611331A (en) * | 1991-12-26 | 1994-01-21 | Tokimec Inc | Instrument and method for measuring undulating wear of rail |
| JP3388051B2 (en) * | 1995-03-09 | 2003-03-17 | 株式会社トキメック | Turnout inspection system and turnout inspection method |
| US5636026A (en) * | 1995-03-16 | 1997-06-03 | International Electronic Machines Corporation | Method and system for contactless measurement of railroad wheel characteristics |
| AT5911U3 (en) * | 2002-10-29 | 2003-11-25 | Plasser Bahnbaumasch Franz | METHOD FOR CONTACT-FREE MEASUREMENT OF A CROSS-PROFILE OR DISTANCE FROM RAILS OF A TRACK |
| BRPI0512871A (en) * | 2004-06-30 | 2008-04-08 | Georgetown Rail Equipment Comp | system and method for inspecting railroad |
| AU2005285009A1 (en) * | 2004-09-11 | 2006-03-23 | General Electric Company | Rail sensing apparatus and method |
| JP2006131168A (en) * | 2004-11-09 | 2006-05-25 | Mitsubishi Heavy Ind Ltd | Wear volume measuring device |
| JP2006258531A (en) * | 2005-03-16 | 2006-09-28 | Act Denshi Kk | Method of measuring rail section and device for measuring rail section used therefor |
| CN101178812A (en) * | 2007-12-10 | 2008-05-14 | 北京航空航天大学 | A hybrid image processing method for extracting the centerline of structured light stripes |
| CN101727659B (en) * | 2008-10-31 | 2012-06-20 | 比亚迪股份有限公司 | Method and system for enhancing image edge |
| US9105106B2 (en) * | 2010-05-11 | 2015-08-11 | Zoran (France) S.A. | Two-dimensional super resolution scaling |
| CN102030016A (en) * | 2010-11-03 | 2011-04-27 | 西南交通大学 | Structured light vision-based track irregularity state detection method |
| CN202011407U (en) * | 2011-03-09 | 2011-10-19 | 大连民族学院 | Rail profile gauge |
| CN102426649B (en) * | 2011-10-13 | 2013-08-21 | 石家庄开发区冀科双实科技有限公司 | Simple steel seal digital automatic identification method with high accuracy rate |
| CN202400107U (en) * | 2011-10-19 | 2012-08-29 | 北京鼎汉检测技术有限公司 | Detection device for dynamically detecting abrasion of lateral sides of steel railway rails |
| JP5946272B2 (en) * | 2011-12-28 | 2016-07-06 | 川崎重工業株式会社 | Railway rail displacement detector |
| CN102749336B (en) * | 2012-07-09 | 2015-01-07 | 南京航空航天大学 | Structured light-based surface defect high-speed detection system and detection method thereof |
| CN102785166B (en) * | 2012-07-18 | 2014-08-27 | 华中科技大学 | Kinematic transformation based grinding machining method for numerically controlled grinding wheel |
| CN102749061B (en) * | 2012-07-26 | 2014-12-24 | 上海工程技术大学 | Steel rail abrasion measuring method based on dynamic template |
| CN203224212U (en) * | 2013-03-05 | 2013-10-02 | 大连民族学院 | Rail wearing detector based on photoelectric reflection principle |
| CO7060224A1 (en) * | 2013-03-18 | 2014-09-19 | Univ Eafit | Inspection system and method for the inspection of the geometric parameters of railway vehicle wheels |
| CN104315984A (en) * | 2014-10-31 | 2015-01-28 | 中国神华能源股份有限公司 | Method and system for measuring abrasion of railway contact line |
| CN204944426U (en) * | 2015-01-27 | 2016-01-06 | 中国铁道科学研究院铁道建筑研究所 | A kind of device of accurate detection railway track abrasion |
| CN104794502A (en) * | 2015-05-15 | 2015-07-22 | 哈尔滨工业大学 | Image processing and mode recognition technology-based rice blast spore microscopic image recognition method |
| CN104908775B (en) * | 2015-06-12 | 2017-08-04 | 华东交通大学 | Non-contact rail wear detection device |
| CN105004280A (en) * | 2015-07-13 | 2015-10-28 | 成都多极子科技有限公司 | Image restoring method in train guiderail contour measurement based on machine vision |
| CN105480256B (en) * | 2015-11-20 | 2018-06-22 | 武汉滨湖电子有限责任公司 | A kind of high-speed railway rail measuring device and measuring method |
| CN105571502B (en) * | 2015-12-29 | 2019-08-09 | 上海交通大学 | Measurement Method of Weld Gap in Friction Stir Welding |
| CN106080661A (en) * | 2016-05-27 | 2016-11-09 | 电子科技大学 | Train guide rail profile undulatory wear measuring method |
| CN108128323B (en) * | 2016-08-30 | 2019-09-17 | 大连民族大学 | The selection method of laser image characteristic quantity relevant to rail wear amount |
| CN108731599B (en) * | 2016-08-30 | 2021-02-05 | 大连民族大学 | Calculation method of rail wear depth |
-
2016
- 2016-08-30 CN CN201810556239.5A patent/CN108731599B/en not_active Expired - Fee Related
- 2016-08-30 CN CN201810556247.XA patent/CN109059775B/en not_active Expired - Fee Related
- 2016-08-30 CN CN201810555967.4A patent/CN108662983B/en not_active Expired - Fee Related
- 2016-08-30 CN CN201610765942.8A patent/CN106441107B/en not_active Expired - Fee Related
- 2016-08-30 CN CN201810556266.2A patent/CN108830841B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1776364A (en) * | 2005-11-22 | 2006-05-24 | 北京航空航天大学 | Rail wear laser vision dynamic measurement device and measurement method |
| CN101144714A (en) * | 2007-10-26 | 2008-03-19 | 北京航空航天大学 | A vehicle-mounted dynamic measurement device and method for comprehensive parameters of rail wear |
| CN101532827A (en) * | 2009-04-15 | 2009-09-16 | 北京航空航天大学 | Deviation correction method for measurement of rail wear based on laser vision |
| CN101576375A (en) * | 2009-05-21 | 2009-11-11 | 北京航空航天大学 | Fast processing method of laser vision image of steel rail wear |
| CN202320395U (en) * | 2011-08-04 | 2012-07-11 | 大连民族学院 | Steel rail abrasion detection device |
| CN203274695U (en) * | 2013-05-24 | 2013-11-06 | 武汉铁路局武汉大型养路机械运用检修段 | Steel-rail wear measurement device based on machine vision |
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| CN108731599A (en) | 2018-11-02 |
| CN109059775B (en) | 2020-11-24 |
| CN108830841B (en) | 2021-11-26 |
| CN108731599B (en) | 2021-02-05 |
| CN106441107A (en) | 2017-02-22 |
| CN108662983A (en) | 2018-10-16 |
| CN109059775A (en) | 2018-12-21 |
| CN108830841A (en) | 2018-11-16 |
| CN106441107B (en) | 2019-02-22 |
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