CN114620091A - Train wheel out-of-roundness detection method based on three-dimensional information - Google Patents
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- B61K9/00—Railway vehicle profile gauges; Detecting or indicating overheating of components; Apparatus on locomotives or cars to indicate bad track sections; General design of track recording vehicles
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Abstract
The invention discloses a train wheel out-of-roundness detection method based on three-dimensional information, which comprises image acquisition and preprocessing, laser center line extraction, laser coordinate transformation and wheel out-of-roundness detection. According to the method, only a small number of linear array cameras and 3D laser scanner modules are combined to generate a plurality of two-dimensional image data and laser scanning data with depth information, a tread area of laser scanning is extracted, and a wheel tread profile with the depth information is reconstructed based on an elliptical model. Whether the wheel is partially out of roundness or integrally out of roundness, the method can accurately calculate the wheel tread profile data and dynamically generate a detection report in time for a customer to review and overhaul.
Description
Technical Field
The invention belongs to the field of digital image processing, and particularly relates to a train wheel out-of-roundness detection method based on three-dimensional information, which is applied to a train wheel set online detection product.
Background
With the rapid development of rail transit in China, the conventional manual wheel out-of-roundness detection cannot meet the daily operation requirement, and the appearance of high-precision intelligent non-contact detection equipment guarantees the running safety of increasingly more trains. In the running process of a train, wheels are key parts for driving safety, and the wheels are abraded due to the friction between the running wheels and wheel rails; in the turning process, the whole out-of-round wheel is easily caused by the conditions of insufficient lubrication and the like; when the braking is unreasonable, the wheel has local radius changes such as scratch, peeling, block falling and the like, so that the local out-of-round of the wheel is caused. The out-of-roundness of the wheels of the train can cause train accidents under severe conditions, so that the out-of-roundness of the wheels of the train needs to be dynamically detected.
At present, the out-of-roundness detection of train wheels mainly comprises static detection and dynamic detection. Static detection is mainly performed manually under the condition that the train stops, and the method cannot meet the increasing demand of the train. The dynamic detection is a non-contact on-line detection method which is carried out by intelligent equipment under the condition of not influencing the running of the train.
Chinese patent ZL201310556634 discloses a device and a method for detecting the out-of-roundness of an urban rail vehicle wheel based on laser sensors. Although the method can measure the out-of-roundness of the wheel, the method requires that all laser sensors are coplanar with the circumference of the wheel to be measured and are arranged below the wheel according to a certain geometrical relationship, the mounting mode has higher requirement on early-stage work and more laser sensors, and the precision is relatively low by fitting a circle through wheel tread data.
Disclosure of Invention
In view of the above-mentioned drawbacks and deficiencies of the prior art, the present invention is directed to a method for detecting out-of-roundness of a train wheel based on three-dimensional information.
The invention adopts the following technical scheme for solving the technical problems:
a train wheel out-of-roundness detection method based on three-dimensional information comprises the following steps:
acquiring and preprocessing an image to obtain a laser line image;
extracting a laser central line: extracting a central line of the obtained laser image, wherein the two-dimensional coordinate is (x, y);
laser coordinate transformation: converting the set of image points generated by the extracted wheel surface laser lines, namely (X, y), into coordinates (X) in a real world coordinate systemw，Yw，Zw)；
And (3) detecting out-of-roundness of the wheel: extracting three-dimensional data of a tread area from the converted three-dimensional laser data of the wheel; sequentially extracting the positions 70 +/-5 mm away from the side surface of the wheel rim at O-XwZWTread coordinates in a plane; general equation A.X according to ellipsesw 2+B·Xw·ZW+C·Zw 2+E·Xw+F·ZWAnd when the +1 is equal to 0, modeling the wheel tread according to an ellipse model, acquiring the fitted ellipse center and the lengths of the long axis and the short axis, determining the size of the wheel according to the lengths of the long axis and the short axis, determining the position of the wheel shaft according to the ellipse center, and facilitating the detection of the out-of-roundness of the subsequent wheel through fitted tread contour data.
Further, out-of-roundness information for the wheel may be calculated from the tread profile data: marking an initial coordinate according to the wheel tread profile, and counting all diameters on the wheel tread profile according to the center of an ellipse; and calculating the maximum value and the minimum value of all the diameters, and finally subtracting the minimum value from the maximum value of the diameters to obtain a difference value which is recorded as the out-of-roundness of the wheel tread.
Further, the laser centerline extraction: adjusting and amplifying the preprocessed laser line image by multiple times to achieve a sub-pixel precision image; detecting the edge points of the amplified laser line image by adopting a self-adaptive edge detection algorithm, and performing morphological expansion processing on the edge detection image; calculating a self-adaptive threshold value through a histogram of the laser line image after statistical amplification; dividing the amplified laser line image by using a self-adaptive threshold, and thinning the divided image; and combining the expanded points through region growing processing, and finally extracting the central line of the obtained laser image, wherein the coordinate of the central line is (x, y).
Further, the image acquisition: a plurality of collection equipment distribute and form the collection equipment array beside the track, and collection equipment includes linear array camera and laser scanner.
Further, the laser coordinate transformation: the (X, y) and coordinates (X) in the world coordinate systemw，Yw，Zw) The relationship of (a) to (b) is as follows:
wherein f is the focal length of the line-scan camera (X)c，Yc，Zc) Coordinates of the linear array camera in a coordinate system, and H is a parameter obtained by calibrating the linear array camera; the corresponding relation between the two-dimensional image point coordinates and the corresponding target three-dimensional coordinates can be known through the formula.
Further, O-X in world coordinate systemwYwZwAnd correcting the three-dimensional wheel information to facilitate splicing and reconstruction of wheel data: and (3) segmenting the point cloud data of the non-wheel part by using a point cloud clustering algorithm DNSCN, extracting the point cloud data only containing wheel information, correcting the cambered wheels into regular rectangular wheels by using a coordinate mapping method, and obtaining laser line data after coordinate transformation and correction.
Further, the pre-processing: and finally, deleting interference data irrelevant to the laser line in a contour extraction and judgment mode. According to the train wheel out-of-roundness detection method based on three-dimensional information, only a small number of linear array cameras and 3D laser scanner modules need to be combined to generate a plurality of two-dimensional image data and laser scanning data with depth information, a tread area of laser scanning is extracted, and a wheel tread contour with the depth information is reconstructed based on an elliptical model. Whether the wheel is partially out of roundness or integrally out of roundness, the method can accurately calculate the wheel tread profile data and timely and dynamically generate a detection report for a customer to review and overhaul.
Drawings
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a schematic view of the acquisition apparatus of the present invention: (a) is a data acquisition schematic diagram of acquisition equipment; (b) the position layout of a plurality of acquisition devices is schematic;
FIG. 3 is a laser line drawing of a raw wheel;
FIG. 4 is a laser line processing diagram;
FIG. 5 is a three-dimensional coordinate transformation diagram;
FIG. 6 is a wheel tread profile.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples.
As can be seen from fig. 1, the train wheel out-of-roundness detection method based on three-dimensional information according to the present invention has 4 main implementation steps, namely, data acquisition, laser data processing, laser coordinate transformation, and wheel out-of-roundness detection, and the specific conditions of each step are as follows:
first, data acquisition
The collecting devices used by the method are arranged at two sides of the track of the throat section of the warehouse-in and warehouse-out of the train, and the number of the collecting devices is selected according to the size of the wheels. The acquisition equipment comprises a laser scanner, a linear array camera, a light source, a transmitter, a receiver, a temperature control component and the like. In this embodiment, 10 acquisition devices are installed in the detection shed of the motor train unit, and only 5 acquisition devices need to be installed on one side of the track to complete data acquisition of a single wheel, and the layout of the devices is shown in fig. 2(b), which shows the layout of the devices on the left side of the track, and the layout of the right side of the track is the same as that on the left side of the track. In addition, the collecting equipment is arranged at the edge of the rail and keeps a safe distance with the rail, the height of the collecting equipment cannot be higher than the height of the rail surface, the angle between the collecting equipment and the rail is about 5 degrees, and the distance between the collecting equipment and the rail is about 600 millimeters. Since the diameter of the wheel of the motor car is 910 mm, the circumference of the wheel can be calculated to be about 2857 mm, the length acquired by a single acquisition device is 600 mm, the total length of continuous acquisition of 5 acquisition devices is 3000 mm, and the total length of data acquired by the acquisition devices is greater than the circumference of the wheel, which indicates that the installation layout of the equipment meets the requirement of wheel acquisition.
Due to the characteristic that the wheel image acquisition equipment is fixed for imaging, when a wheel passes through, each wheel has thousands of scans to capture the detailed state of each wheel, a schematic diagram of the data of the wheel acquired by the equipment is shown in fig. 2(a), the acquired wheel images are numbered in sequence according to the layout and shooting sequence of the acquisition equipment and are transmitted to the server for storage through TCP, and subsequent data processing and analysis are facilitated.
Second, laser data processing
As shown in fig. 3, a part of original laser images of wheels collected by a certain collection device affects subsequent data processing and measurement accuracy due to interference of light sources, noise and the like in the obtained data, in this embodiment, gaussian filtering is used to perform noise reduction processing on the original data to obtain a smoother laser line image, the definition of the laser line image is improved by contrast stretching, and finally, interference data irrelevant to the laser line is deleted by means of contour extraction and judgment, and the preprocessed image is recorded as Img.
Further extracting the central coordinate of the laser line, which comprises the following specific steps:
adjusting and amplifying the Img by 2 times to obtain a sub-pixel precision image, and marking as ImgResize;
detecting the edge point of the amplified laser line image ImgResize by adopting a self-adaptive edge detection algorithm, and performing morphological expansion processing on the edge detection image, wherein the image after the expansion processing is recorded as ImgDilate;
counting a histogram of the ImgResize, and calculating an adaptive threshold according to histogram information;
dividing the image ImgResize by using the adaptive threshold obtained in the step (3), and thinning the divided image, and recording as ImgThin;
the ImgThin is subjected to region growing, and the imgdialte points are processed and merged to finally extract the center line of the laser image, and as shown in fig. 4, the center line coordinates are (x, y).
Three, laser coordinate transformation
Because linear array camera and laser scanner mounted position are relatively fixed, this embodiment is markd linear array camera earlier, marks laser scanner again, obtains the position of laser scanner relative linear array camera, and the data of laser scanner is reflected through the camera formation of image, and two-dimensional laser image data just can convert to in the three-dimensional world coordinate system like this.
Specifically, the image point set (X, y) generated by the wheel surface laser line extracted in the step two is converted into coordinates (X) in the world coordinate systemw，Yw，Zw) According to the principle of photogrammetry, the relationship between them is as follows:
wherein f is the focal length of the line-scan camera,
h is a variation parameter obtained by calibration, (X)c，Yc，Zc) Are the coordinates in the camera coordinate system. Because the installation positions of the laser scanner and the camera in the acquisition equipment are relatively fixed, the camera is calibrated in advance through the black and white checkerboard, and the calibrated camera variation can be obtainedAnd changing the parameter H.
Because the coordinates of the laser line image and the position of the calibration object are known, the laser scanner is calibrated, and the laser plane equation p is obtained1·Xw+p2·YW+p3·Zw+ T-0 is written as P · X-T, where P is1 2+p2 2+p3 2＝1，P＝[p1，p2，p3]，X＝[Xw Yw Zw]TAnd calculating the T-T by an SVD (singular Value decomposition) decomposition method to obtain the calibrated parameter P of the laser scanner.
Since any laser point on the wheel tread image is the result of the intersection of a laser plane and an object, the position of the wheel tread three-dimensional information in the world coordinate system can be obtained through the coordinate transformation. Namely, once the laser scanner and the line camera are calibrated, any laser point on the wheel image can be converted through the coordinate to obtain the position of the wheel tread and the wheel rim three-dimensional information in the world coordinate system.
In the data acquisition process, the distance between the wheel and the laser scanner is changed, namely the laser line data acquired at a short distance is wider, the laser line data acquired at a far distance is narrower, so the acquired laser line is in an arc shape in a world coordinate system, and 0-X in the world coordinate system is neededwYwZwAnd the three-dimensional wheel tread information is corrected, so that the fitting reconstruction of subsequent wheel tread data is facilitated. In this embodiment, the point cloud data of the non-wheel portion is segmented by a point cloud Clustering algorithm DNSCN (Density-Based Spatial Clustering of Applications with Noise), point cloud data only with wheel information is extracted, and finally, the cambered wheel is corrected into a regular wheel by a coordinate mapping method, as shown in fig. 5, laser line data after coordinate transformation correction is obtained.
Fourth, wheel out-of-roundness detection
Obtaining the three-dimensional coordinate of the wheel tread laser line under the world coordinate system through the third step, in order to obtain the real information of the tread contour,the following steps are employed. Firstly, extracting three-dimensional data of a tread area from the transformed three-dimensional laser data of the wheel in a world coordinate system; then sequentially extracting the position of the flange 70 +/-5 mm away from the side surface of the wheel rim at 0-XwZWTread coordinates in a plane; finally, modeling the wheel tread according to an ellipse model according to a general equation of an ellipse, wherein the general equation of the ellipse is
A·Xw 2+B·Xw·ZW+C·Zw 2+D·Xw+E·ZW+1＝0
Write the above formula into
Order to
Then there is
Xw 2+a·Xw·ZW+b·Zw 2+c·Xw+d·ZW+e＝0
Written in matrix form as:
the values of parameters a, b, c, d, e, and further A, B, C, D, E, were obtained by calculation using the SVD (singular Value decomposition) decomposition method.
By the fitting calculation, the ellipse center (x) can be obtainedc，zc) Major axis length R1And minor axis length R2The length of the major axis and the minor axis determines the size of the wheel, the center of the ellipse determines the position of the wheel shaft, and as shown in fig. 6, the fitted tread profile data is used for facilitating the detection of out-of-roundness of the subsequent wheel for the comparison of real data and fitted data.
And calculating out-of-roundness information of the wheel according to the obtained tread contour data. Specifically, the method comprises the following steps:
(1) marking the starting coordinate (x) according to the wheel tread profile1，z1) According to the ellipse center (x)c，zc) Obtaining a straight line segment L;
(2) calculating the intersection point of the straight line segment L and the other end of the tread contour and recording as (x)11，z11) Through (x)1，z1) And (x)11，z11) Obtaining the diameter of the tread profile;
(3) counting all diameters on the wheel tread profile according to the steps (1) and (2), and calculating the maximum value and the minimum value of all the diameters;
(4) and subtracting the minimum value from the maximum value of the diameter to obtain a difference value, and recording the difference value as the out-of-roundness of the wheel tread.
The embodiment discloses a train wheel out-of-roundness detection method based on three-dimensional depth information, which adopts a combination mode of a plurality of linear cameras and a 3D laser scanner to generate a plurality of two-dimensional image data and laser scanning data with depth information, extracts a tread area of the laser scanning, and reconstructs a wheel tread contour with the depth information based on an elliptical model. Whether the wheel is partially out of roundness or wholly out of roundness, the wheel tread profile data can be accurately calculated, and a detection report can be dynamically generated in time for a customer to review and overhaul.
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (7)
1. A train wheel out-of-roundness detection method based on three-dimensional information is characterized by comprising the following steps:
acquiring and preprocessing an image to obtain a laser line image;
extracting a laser central line: extracting a central line of the obtained laser image, wherein the two-dimensional coordinate is (x, y);
laser coordinate transformation: converting the set of image points generated by the extracted wheel surface laser lines, namely (X, y), into coordinates (X) in a real world coordinate systemw，Yw，Zw)；
And (3) detecting out-of-roundness of the wheel: extracting three-dimensional data of a tread area from the converted three-dimensional laser data of the wheel; sequentially extracting the positions 70 +/-5 mm away from the side surface of the wheel rim at O-XwZWTread coordinates in a plane; according to the general equation A.X for ellipsesw 2+B.Xw·Zw+C·Zw 2+E.Xw+F.ZwAnd when the +1 is equal to 0, modeling the wheel tread according to an ellipse model, acquiring the fitted ellipse center and the lengths of the long axis and the short axis, determining the size of the wheel according to the lengths of the long axis and the short axis, determining the position of the wheel shaft according to the ellipse center, and facilitating the detection of the out-of-roundness of the subsequent wheel through fitted tread contour data.
2. The method for detecting out-of-roundness of a train wheel based on three-dimensional information according to claim 1, wherein the out-of-roundness information of the wheel is calculated from the tread profile data by: marking an initial coordinate according to the wheel tread profile, and counting all diameters on the wheel tread profile according to the center of an ellipse; and calculating the maximum value and the minimum value of all the diameters, and finally subtracting the minimum value from the maximum value of the diameters to obtain a difference value which is recorded as the out-of-roundness of the wheel tread.
3. The train wheel out-of-roundness detection method based on three-dimensional information according to claim 1 or 2, wherein the laser centerline extraction: adjusting and amplifying the preprocessed laser line image by multiple times to achieve a sub-pixel precision image; detecting the edge points of the amplified laser line image by adopting a self-adaptive edge detection algorithm, and performing morphological expansion processing on the edge detection image; calculating a self-adaptive threshold value through a histogram of the laser line image after statistical amplification; dividing the amplified laser line image by using a self-adaptive threshold value, and thinning the divided image; and combining the expanded points through region growing processing, and finally extracting the central line of the obtained laser image, wherein the coordinate of the central line is (x, y).
4. The train wheel out-of-roundness detection method based on three-dimensional information according to claim 1 or 2, wherein the image acquisition: a plurality of collection equipment distribute and form the collection equipment array beside the track, and collection equipment includes linear array camera and laser scanner.
5. The train wheel defect detection method based on three-dimensional information as claimed in claim 1 or 2, wherein the laser coordinate transformation: the (X, y) and coordinates (X) in world coordinate systemw，Yw，Zw) The relationship of (a) to (b) is as follows:
wherein f is the focal length of the line-scan camera (X)c，Yc，Zc) Coordinates of the linear array camera in a coordinate system, and H is a parameter obtained by calibrating the linear array camera; the corresponding relation between the two-dimensional image point coordinates and the corresponding target three-dimensional coordinates can be known through the formula.
6. The method of claim 5, wherein the method comprises O-X in a world coordinate systemwYwZwAnd correcting the three-dimensional wheel information to facilitate splicing and reconstruction of wheel data: and (3) segmenting the point cloud data of the non-wheel part by using a point cloud clustering algorithm DNSCN, extracting the point cloud data only containing wheel information, correcting the cambered wheels into regular rectangular wheels by using a coordinate mapping method, and obtaining laser line data after coordinate transformation and correction.
7. The train wheel out-of-roundness detection method based on three-dimensional information according to claim 1 or 2, wherein the preprocessing: and finally, deleting interference data irrelevant to the laser line in a contour extraction and judgment mode.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2266226C2 (en) * | 2003-04-09 | 2005-12-20 | Конструкторско-технологический институт научного приборостроения (статус государственного учреждения) | Method of monitoring parameters of wheelset and its position relative to rail track |
| CN103587549A (en) * | 2013-11-11 | 2014-02-19 | 南京理工大学 | Laser-sensor-based device and method for detecting out-of-roundness of wheels of urban rail vehicles |
| CN104792274A (en) * | 2015-04-02 | 2015-07-22 | 同济大学 | Method for measuring circular tunnel convergence deformation |
| KR20150128300A (en) * | 2014-05-09 | 2015-11-18 | 한국건설기술연구원 | method of making three dimension model and defect analysis using camera and laser scanning |
| CN105571502A (en) * | 2015-12-29 | 2016-05-11 | 上海交通大学 | Measuring method of weld gap in friction-stir welding |
| WO2017041419A1 (en) * | 2015-09-10 | 2017-03-16 | 天远三维（天津）科技有限公司 | Multi-line array laser three-dimensional scanning system, and multi-line array laser three-dimensional scanning method |
| CN107167073A (en) * | 2017-05-18 | 2017-09-15 | 浙江四点灵机器人股份有限公司 | A kind of three-dimensional rapid measurement device of linear array structure light and its measuring method |
| CN107607044A (en) * | 2017-08-21 | 2018-01-19 | 南京理工大学 | A kind of hollow on worn tread detection method based on laser displacement sensor |
| CN111442736A (en) * | 2020-04-29 | 2020-07-24 | 安徽国钜工程机械科技有限公司 | Railway tunnel deformation detection method and device based on laser scanner |
| CN112799088A (en) * | 2021-01-21 | 2021-05-14 | 中南大学 | Full-automatic tunnel wet spraying state detection method based on three-dimensional laser radar |
| CN114264491A (en) * | 2021-12-27 | 2022-04-01 | 中车青岛四方车辆研究所有限公司 | Rail vehicle wheel pair parameter detection system |
-
2022
- 2022-04-11 CN CN202210372005.1A patent/CN114620091A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2266226C2 (en) * | 2003-04-09 | 2005-12-20 | Конструкторско-технологический институт научного приборостроения (статус государственного учреждения) | Method of monitoring parameters of wheelset and its position relative to rail track |
| CN103587549A (en) * | 2013-11-11 | 2014-02-19 | 南京理工大学 | Laser-sensor-based device and method for detecting out-of-roundness of wheels of urban rail vehicles |
| KR20150128300A (en) * | 2014-05-09 | 2015-11-18 | 한국건설기술연구원 | method of making three dimension model and defect analysis using camera and laser scanning |
| CN104792274A (en) * | 2015-04-02 | 2015-07-22 | 同济大学 | Method for measuring circular tunnel convergence deformation |
| WO2017041419A1 (en) * | 2015-09-10 | 2017-03-16 | 天远三维（天津）科技有限公司 | Multi-line array laser three-dimensional scanning system, and multi-line array laser three-dimensional scanning method |
| CN105571502A (en) * | 2015-12-29 | 2016-05-11 | 上海交通大学 | Measuring method of weld gap in friction-stir welding |
| CN107167073A (en) * | 2017-05-18 | 2017-09-15 | 浙江四点灵机器人股份有限公司 | A kind of three-dimensional rapid measurement device of linear array structure light and its measuring method |
| CN107607044A (en) * | 2017-08-21 | 2018-01-19 | 南京理工大学 | A kind of hollow on worn tread detection method based on laser displacement sensor |
| CN111442736A (en) * | 2020-04-29 | 2020-07-24 | 安徽国钜工程机械科技有限公司 | Railway tunnel deformation detection method and device based on laser scanner |
| CN112799088A (en) * | 2021-01-21 | 2021-05-14 | 中南大学 | Full-automatic tunnel wet spraying state detection method based on three-dimensional laser radar |
| CN114264491A (en) * | 2021-12-27 | 2022-04-01 | 中车青岛四方车辆研究所有限公司 | Rail vehicle wheel pair parameter detection system |
Non-Patent Citations (1)
| Title |
|---|
| 孙博文: "基于激光结构光的视觉传感器的图像处理技术研究应用及展望", 《焊接 试验研究》, 25 August 2018 (2018-08-25), pages 10 - 14 * |
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