Non-contact vehicle profile detection device and method

Abstract

The invention provides a non-contact vehicle profile detection device and method, and belongs to the field of vehicle profile detection. The device comprises a rack unit which is used for driving a point laser measurement unit to perform continuous measurement after a reference position is fixed; the point laser measuring unit is used for continuously measuring wheel section point data; the positioning and fixing unit is used for fixing a reference position by taking the edge arc as the reference position; and the data interaction unit is used for storing the wheel section point data and inputting the continuously measured wheel section point data to the PC terminal. The problem that a traditional detection device cannot perform continuous sampling is effectively solved, the working efficiency is improved, meanwhile, the data processing method can effectively remove abnormal points of wheel data to achieve smooth processing of the wheel data, targeted segmentation processing is performed on a profile curve, the calculation efficiency is improved, and the detection accuracy is improved. Finally, a complete vehicle contour curve is obtained, and data support is provided for wheel health management.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wheel profile detection, and particularly relates to a non-contact wheel profile detection device and method. BACKGROUND

[0002] Wheelset is the part of locomotive vehicle which contacts with steel rail, and has the functions of bearing the whole load and impact of the vehicle, guiding the vehicle to run, and generating traction force or braking force with the adhesion of steel rail. The complex wheel-rail contact environment can cause the wheel to produce wear and peeling problems. Although the actual contact area of the profile is limited, the force it bears is extremely complex, and various diseases are prone to occur, so it is particularly important to establish the wheel health management data.

[0003] In the traditional wheel health management system, the profile detection method is manual operation of a measuring ruler. The maintenance personnel judge the wheel profile state through the measuring ruler data to determine whether the wheel needs to be refinished. This detection method has low work efficiency, and may be misjudged due to subjective factors, and cannot provide reliable data support for the wheel health management system.

[0004] To solve the above problems, people have been seeking an effective method. SUMMARY

[0005] In view of the above shortcomings in the prior art, the present application provides a non-contact wheel profile detection device and method. The present application effectively solves the problem that the traditional detection device cannot continuously sample, improves the work efficiency, and the data processing method proposed by the present application can effectively remove the abnormal points of the wheel data to realize the smoothing processing of the wheel data, and the profile curve is processed in segments, the calculation efficiency is improved, and finally the complete wheel profile curve is obtained, which provides data support for the wheel health management.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: a non-contact wheel profile detection device, comprising a rack unit, a point laser measurement unit, a positioning and fixing unit, and a data interaction unit. The rack unit is used to drive the point laser measurement unit to continuously measure after fixing the reference position. The point laser measurement unit is used to continuously measure the wheel cross-section point data. The positioning and fixing unit is used to fix the reference position by taking the edge arc as the reference position. The data interaction unit is used to store the wheel cross-section point data, and input the continuously measured wheel cross-section point data to the PC end.

[0007] The beneficial effects of the present application are: the present application uses the laser displacement sensor to identify the wheel cross-section point data after the positioning and fixing unit aligns the rear wheel, realizes the continuous identification function of the wheel cross-section point data by combining the sensor moving part, transmits the wheel cross-section point data to the PC end, processes the data by using the algorithm, obtains the wheel contour cross-section data, then compares the wheel cross-section data with the design model data, judges the wheel state through the data difference, and provides data support for the wheel health management.

[0008] Further, the rack unit comprises a rack, an upper shell located above the rack and connected with the rack, a front end head of the shell, a touch display screen and a rear end head of the shell, a lower shell located below the rack and connected with the rack, a bottom shell connected with the lower shell, and a support frame connected with the bottom shell and the lower shell; the rack is connected with the point laser measurement unit and the data interaction unit respectively, and the support frame is connected with the positioning and fixing unit.

[0009] The beneficial effects of the above further scheme are: the rack body in the present application provides support for the detection unit and the positioning unit.

[0010] Further, the point laser measurement unit comprises a linear guide rail and a motor support seat installed on the rack respectively, a driving motor installed on the motor support seat, a synchronous pulley connected with the driving motor, a synchronous belt idler connected with the driving motor in meshing connection, an idler mounting seat connected with the synchronous belt idler, a mounting seat connected with the linear guide rail, a synchronous belt pressing plate connected with the mounting seat, and a laser displacement sensor installed on the mounting seat; the synchronous belt pressing plate is connected with the synchronous belt and the mounting seat; the idler mounting seat is installed on the rack, and the synchronous pulley is connected with the driving motor.

[0011] The beneficial effects of the above further scheme are: the present application realizes the continuous scanning function of the cross-section by driving the point laser displacement sensor with the driving motor, so that the wheel cross-section detection data is more comprehensive, and the data reliability is high.

[0012] Further, the positioning and fixing unit comprises a positioning rod and a magnet assembly connected with the support frame respectively; the positioning rod and the magnet assembly are locked and installed on the support frame by screws.

[0013] The beneficial effects of the above further scheme are: when artificial detection is simulated, the detection of the traditional tool is aligned, and the magnet assembly in the present application can fix the device to realize hands-off detection, thereby reducing the working intensity during artificial detection.

[0014] The present application also provides a non-contact wheel contour detection method, comprising the following steps: S1, using the edge arc as the reference position, aligning the wheel rim outer arc part with the positioning rod, and attaching the magnet assembly to the inner side plane of the wheel and adsorbing; S2, driving the synchronous pulley by the driving motor, driving the synchronous belt engaged with the synchronous pulley to move, so that the mounting base of the laser displacement sensor is also displaced, and the laser displacement sensor is used to continuously measure the wheel cross-section point data; S3, using the data interaction unit to input the continuously measured wheel cross-section point data to the PC end; S4, in the PC end, using the moving average method to detect and remove the jump points, and according to the wheel profile characteristics, the removed wheel cross-section point data is segmented to obtain the first segment wheel profile data, the second segment wheel profile data and the third segment wheel profile data; S5, the first segment wheel profile data and the second segment wheel profile data are processed by the moving average method, and the third segment wheel profile data is processed by the quartile range method; S6, using the locally weighted regression to process the first segment wheel profile data and the second segment wheel profile data processed by the moving average method; S7, using the weighted linear least square method to fit the first segment wheel profile data, the second segment wheel profile data and the third segment wheel profile data processed to obtain a complete fitting profile curve; S8, comparing the complete fitting profile curve obtained with the profile curve output by the standard design model to obtain the wheel surface wear data, and completing the detection of the non-contact wheel profile.

[0015] The beneficial effects of the present application are: the present application is based on the moving average method (Moving Average, MA), the quartile range method (Interquartile Rang, IQR), the locally weighted regression (Locally Weighted Regression, LWR) and the weighted linear least square fitting data processing method, which can effectively remove the abnormal points of the wheel data and realize the smoothing processing of the wheel data, and the profile curve is processed segmentally, the calculation efficiency is improved, and finally the complete wheel profile curve is obtained, the present application effectively solves the problem that the traditional detection device cannot continuously sample, improves the work efficiency, and provides data support for the wheel health management.

[0016] Further, the S4 comprises the following steps: In the PC end, using the moving average method to detect the jump points, and using the large window to iteratively process the wheel cross-section point data, wherein the large window is used to preliminarily process the jump points; The wheel cross-section point data is divided into three segments by the highest point and the position 30mm before the nominal rolling circle, wherein the first segment wheel profile data is recorded before the highest point, the second segment wheel profile data is recorded from the highest point to the position 30mm before the nominal rolling circle, and the third segment wheel profile data is recorded after the position 30mm before the nominal rolling circle.

[0017] Further, the third section of the vehicle wheel profile data is smoothed by using the interquartile range method, comprising the following steps: The third section of the vehicle wheel profile data is sorted from small to large, divided into three partition points of four equal parts, and the quartiles Q1 and Q3 are calculated, wherein Q1 represents the lower quartile, and Q3 represents the upper quartile; The difference between the quartiles Q1 and Q3 is used to calculate the interquartile range IQR; Based on the interquartile range IQR, the normal value range of the third section of the vehicle wheel profile data is defined, wherein the lower limit is , and the upper limit is , and the range outside the upper and lower limits is defined as an abnormal value; The abnormal data is cleaned up, and the smoothing of the third section of the vehicle wheel profile data is completed.

[0018] The beneficial effects of the above further scheme are: the abnormal data is cleaned up, the smoothing of the third section of the vehicle wheel profile data is completed, the abnormal data is effectively removed, and the final obtained data has high reliability and is closer to the true value.

[0019] Further, the S6 comprises the following steps: The Gaussian kernel function is used to assign a corresponding weight to each wheel data sample point in the window of the prediction point , , , n , i , i , , , , , , , , , , , , , , ,

[0020] , , , ; ; ; ; ; wherein, represents a predicted value, represents a constant coefficient matrix, represents d a second-order term coefficient matrix, represents a second power of a prediction point , d represents a wheel data sample polynomial base function matrix, X represents a transpose of , represents a weight diagonal matrix, represents a column vector of , , , , represents a first power of , d represents a second power of , n represents an i-th wheel data sample point, represents a corresponding Gaussian kernel function when i n , represents a Gaussian kernel function, represents a bandwidth parameter.

[0021] The above further scheme has the beneficial effect that the above method can efficiently process the smoothing problem of nonlinear and interference-containing wheel profile data, especially for the case that the local wheel profile data changes greatly and there is great interference in the local position. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 is a method flowchart of the present application.

[0023] Figure 2 is a working schematic diagram of the detector in the present application.

[0024] Figure 3 is a schematic diagram of the three-dimensional structure of the detector in the present application.

[0025] Figure 4 is a schematic diagram of the internal structure of the detector in the present application.

[0026] Figure 5 is a schematic diagram of the rack unit structure of the present application.

[0027] ​Figure 6 Structure diagram of point laser measurement unit of the application.

[0028] Figure 7 Structure diagram of positioning and fixing unit of the application.

[0029] Wherein, 1 is a rack unit, 101 is a rack, 102 is an upper shell, 103 is a touch display screen, 104 is a front end of the shell, 105 is a rear end of the shell, 106 is a support frame, 107 is a lower shell, 108 is a bottom shell, 2 is a point laser measurement unit, 201 is a driving motor, 202 is a motor support seat, 203 is a synchronous pulley, 204 is a linear guide rail, 205 is a mounting seat, 206 is a laser displacement sensor, 207 is a synchronous belt pressing plate, 208 is a synchronous belt idler, 209 is an idler mounting seat, 210 is a synchronous belt, 3 is a positioning and fixing unit, 301 is a positioning rod, 302 is a magnet assembly, and 4 is a data interaction unit. DETAILED DESCRIPTION

[0030] The specific embodiments of the application are described below to facilitate the understanding of the application by those skilled in the art, but it should be clear that the application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the application defined and determined by the appended claims, and all applications utilizing the concept of the application are within the scope of protection.

[0031] Example 1 As shown in Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 , the application provides a non-contact wheel profile detection device, which comprises a rack unit 1, a point laser measurement unit 2, a positioning and fixing unit 3, and a data interaction unit 4. The rack unit 1 is used to drive the point laser measurement unit 2 to continuously measure after fixing the reference position; The point laser measurement unit 2 is used to continuously measure the wheel cross-section point data; The positioning and fixing unit 3 is used to fix the reference position with the edge arc as the reference position; The data interaction unit 4 is used to store the wheel cross-section point data and input the continuously measured wheel cross-section point data to the PC end.

[0032] In this embodiment, as shown in Figures 3 to 5 , Figure 4As shown in the schematic diagram of the internal structure of the detection device, the rack unit 1 comprises a rack 101, an upper housing 102 located above the rack 101 and connected with the rack 101, a housing front end 104, a touch display screen 103 and a housing rear end 105, a lower housing 107 located below the rack 101 and connected with the rack 101, a bottom housing 108 connected with the lower housing 107, and a support frame 106 connected with the bottom housing 108 and the lower housing 107; the rack 101 is connected with a point laser measurement unit 2 and a data interaction unit 4 respectively, and the support frame 106 is connected with a positioning and fixing unit 3.

[0033] As shown in the schematic diagram of the internal structure of the detection device, Figure 3 and Figure 6 As shown in the schematic diagram of the internal structure of the detection device, Figure 3 the point laser measurement unit 2 comprises a linear guide rail 204 and a motor support seat 202 mounted on the rack 101 respectively, a driving motor 201 mounted on the motor support seat 202, a synchronous belt pulley 203 connected with the driving motor 201, a synchronous belt idler pulley 208 meshed and connected with the driving motor 201, an idler pulley mounting seat 209 connected with the synchronous belt idler pulley 208, a mounting seat 205 connected with the linear guide rail 204, a synchronous belt pressing plate 207 connected with the mounting seat 205, and a laser displacement sensor 206 mounted on the mounting seat 205; the synchronous belt pressing plate 207 is connected with a synchronous belt 210 and the mounting seat 205; the idler pulley mounting seat 209 is mounted on the rack 101, and the synchronous belt pulley 203 is connected with the driving motor 201.

[0034] As shown in the schematic diagram of the internal structure of the detection device, Figure 7 the positioning and fixing unit 3 comprises a positioning rod 301 and a magnet assembly 302 connected with the support frame 106 respectively, and the positioning rod 301 and the magnet assembly 302 are locked and mounted on the support frame 106 by screws.

[0035] In this embodiment, the device comprises a detection position fixing function, a wheel cross section continuous detection function, and a data transmission function, etc.

[0036] In this embodiment, the rack unit 1 comprises: the rack 101 is fixedly connected with the upper housing 102, the housing front end 104, the housing rear end 105, and the support frame 106; the lower housing 107 and the bottom housing 108 are fixedly connected with the support frame 106. The data interaction unit 4 in the rack is fixedly connected with the rack 101.

[0037] In the embodiment, the point laser measurement unit 2 comprises: a driving motor 201 fixedly installed on a motor support seat 202, a front section of the driving motor 201 fixedly connected with a synchronous belt pulley 203, the synchronous belt pulley 203 and a synchronous belt idler 208 meshed and connected with a synchronous belt 210, the synchronous belt idler 208 fixedly connected with an idler mounting seat 209, a laser displacement sensor 206 fixedly installed on a sensor mounting seat 205, the mounting seat 205 fixedly connected with the synchronous belt 210 and a synchronous belt pressing plate 207, the mounting seat 205 further connected with a linear guide rail 204, and the linear guide rail 204, the motor support seat 202 and the idler mounting seat 209 fixedly installed on a rack 101.

[0038] In the embodiment, the positioning and fixing unit 3 comprises a positioning rod 301 and a magnet assembly 302 fixedly connected with the support frame 106.

[0039] In the embodiment, after the positioning and fixing unit 3 is aligned with the rear wheel, the driving motor 201 drives the driving wheel to rotate, drives the synchronous belt 210 to move horizontally, and drives the sensor mounting seat 205 fixedly connected with the synchronous belt 210 to move horizontally, so as to drive the laser displacement sensor 206 to move horizontally, and the laser displacement sensor 206 identifies point data. The laser displacement sensor 206 realizes the function of continuous identification of wheel section point data, transmits the wheel section point data to a PC, processes the data by using an algorithm, obtains wheel profile section data (complete fitting profile curve), compares the wheel profile data with standard design model wheel profile data, judges the wheel state through the wheel data difference, provides data support for wheel health management, that is, obtains wheel surface wear data through the data difference, and further judges the wheel service state.

[0040] In the embodiment, the wheel profile model is a wheel profile model obtained by processing an algorithm, and the standard wheel profile design model is a standard existing model, such as a standard design model in TB / T 2817-2018.

[0041] The working principle of the present application is as follows: In the traditional manual detection, a detection ruler (such as a wheel tread profile detector in Figure 2 The wheel tread profile detector is set between a wheel set and takes the outer circular arc of the flange as a reference edge, and the inner side of the wheel is a reference surface for detection. Figure 2As shown, the device is used as a reference in this way, by positioning the positioning rod 301 assembly in the fixed unit 3 to align the wheel rim outer arc, the magnet assembly 302 is magnetically attracted to the side plane of the wheel to fix the device in the measurement position, start the laser displacement sensor 206, continuously measure the wheel cross-section point data on the moving unit path, the internal data interaction unit 4 of the device transmits the measurement data to the PC end, and uses the algorithm to jump the point processing and curve smoothing when the sensor measures the abnormal value and data fluctuation, then the profile curve is segmented and processed, the integrity of the data is retained, and finally the wheel profile curve is obtained. Then compare the wheel profile data with the design data to judge the wheel state through the data difference, and provide data support for the wheel health management system.

[0042] Embodiment 2 As Figure 1 shown, the present application provides a non-contact wheel profile detection method, which is implemented as follows: S1, using the edge arc as the reference position, using the positioning rod 301 to align the wheel rim outer arc part, and using the magnet assembly 302 to stick to the inner side plane of the wheel and be attracted; S2, using the driving motor 201 to drive the synchronous pulley 203, the synchronous pulley 203 drives the meshing synchronous belt 210 to move, so that the mounting seat 205 of the laser displacement sensor 206 also displaces, and the laser displacement sensor 206 is used to continuously measure the wheel cross-section point data; S3, using the data interaction unit 4 to input the continuously measured wheel cross-section point data to the PC end; S4, in the PC end, using the moving average method to detect and remove the jump point, and according to the wheel profile characteristics, the removed wheel cross-section point data is segmented and processed to obtain the first segment wheel profile data, the second segment wheel profile data and the third segment wheel profile data, and the implementation method is as follows: In the PC end, using the moving average method to detect the jump point, and using the large window to iteratively process the wheel cross-section point data, wherein the large window is used to preliminarily process the higher jump point; The wheel cross-section point data is divided into three segments at the highest point and the position 30mm before the nominal rolling circle, wherein the first segment wheel profile data is recorded before the highest point, the second segment wheel profile data is recorded from the highest point to the position 30mm before the nominal rolling circle, and the third segment wheel profile data is recorded after the position 30mm before the nominal rolling circle.

[0043] S5, the first segment wheel profile data and the second segment wheel profile data are processed by the moving average method, and the third segment wheel profile data is processed by the quartile range method; wherein the quartile range method is used to smooth the third segment wheel profile data, and the implementation method is as follows: The third section of the wheel profile data is sorted from small to large, divided into three split points of four equal parts, and the quartiles Q1 and Q3 are calculated, wherein Q1 represents the lower quartile, and Q3 represents the upper quartile; The interquartile range IQR is calculated by the difference between the quartiles Q1 and Q3; Based on the interquartile range IQR, the normal value range of the third section of the wheel profile data is defined, wherein the lower limit is , and the upper limit is , and the range other than the lower and upper limits is defined as an abnormal value; The abnormal data is cleaned, and the smoothing processing of the third section of the wheel profile data is completed; S6, using local weighted regression, the first section of the wheel profile data and the second section of the wheel profile data processed by the moving average method are processed; the implementation method is as follows: A Gaussian kernel function is used to predict the point The window Each wheel data sample point in the neighborhood is given a corresponding weight, wherein , n represents the total number of wheel data samples in the window, i represents the i wheel data sample; the smoothing process is a process of processing each data point, and the prediction point is the point that needs to be smoothed at present; the wheel data sample point is all points in the neighborhood of the point that needs to be smoothed; For each prediction point , a weight diagonal matrix is constructed; In the field of the prediction point , a polynomial function is used to fit the wheel data; Based on the wheel data fitting result and the constructed weight diagonal matrix, the coefficient vector matrix is solved by weighted linear least squares method; Based on the solved coefficient vector matrix and the prediction point , the predicted value is obtained; Based on the predicted value obtained for each prediction point , the processing of the first section of the wheel profile data and the second section of the wheel profile data is completed; S7, using weighted linear least squares method, the first section of the wheel profile data, the second section of the wheel profile data and the third section of the wheel profile data processed are fitted to obtain a complete fitted profile curve; S8, the obtained complete fitting profile curve is compared with the profile curve output by the standard design model to obtain wheel surface wear data, and the non-contact wheel profile detection is completed.

[0044] In the embodiment, the data processing methods of moving average (MA), interquartile range (IQR), locally weighted regression (LWR), and weighted linear least square fitting are used to effectively solve the abnormal data measured by the sensor, smooth the measurement data, and keep the integrity of the profile curve data by segment processing. The above methods have the characteristics of good final data effectiveness, fast data processing speed, and strong noise resistance.

[0045] In the embodiment, the two positioning rods 301 are tangent to the outer circular arc position of the wheel rim, and the magnet assembly 302 is attached to the inner side of the wheel. This structure is quick to install, easy to operate, and accurate in positioning. It effectively reduces the work intensity of manual operation and avoids data anomalies caused by manual positioning errors.

[0046] In the embodiment, the detection device uses the edge circular arc as the reference position, and the two positioning rods 301 are aligned with the outer circular arc part of the wheel rim. Then the magnet assembly 302 is attached to the inner side of the wheel and is attracted, thereby realizing the fixation of the device in the working position. The driving motor 201 drives the synchronous pulley 203 to displace the synchronous belt 210. The laser displacement sensor 206 mounting seat 205 installed at the synchronous belt 210 also moves, continuously measuring the wheel section point data. The measurement of the wheel section point data is transmitted to the data interaction unit 4 through the connection line, and then the data interaction unit 4 transmits the measurement of the wheel section point data to the PC end. Through the comprehensive moving average (MA) interquartile range (IQR), locally weighted regression (LWR), and weighted linear least square fitting methods, the sensor data is processed. The specific steps of this method are as follows: Step one, first, use the moving average method to detect the jump point, and process the wheel section point data by size window iteration. The large window is set for preliminary processing to handle higher jump points, and the window is set to 50 and the threshold is set to 10 mm. Since there are obvious jump points before and after the highest point, in order to improve the calculation rate, combined with the actual measurement of the laser displacement sensor 206, the wheel section point data is divided into three parts: the highest point before the first segment of the wheel profile data, the highest point to the nominal rolling circle 30 mm position, and the third wheel data after the nominal rolling circle 30 mm position.

[0047] Step two, further refinement of the first and second section of the wheel profile data is performed by setting a window of 5 and a threshold of 0.5 to handle small range changes in jitter.

[0048] Since the third section of the wheel profile data is relatively stable, the interquartile range method is used to smooth the third section of the wheel profile data, and the specific steps are as follows: 1) Sort the third section of the wheel profile data from small to large, divide it into four equal parts, and calculate the quartiles: Q1 (lower quartile): the value at the 25% position, Q3 (upper quartile): the value at the 75% position; 2) Calculate the interquartile range (IQR) IQR = Q3 - Q1; 3) Define the normal data range, where the lower bound is: and the upper bound is: The remaining range is defined as an outlier; 4) Data cleaning of abnormal data; Step three, local weighted regression is used to further remove and smooth the wheel profile data anomalies. To improve computational efficiency, only the first and second sections of the wheel profile data are processed using local weighted regression. As a non-parametric learning method, the core idea of local weighted regression is to assign different weights to the local data points near each prediction point, and fit a local model using weighted linear least squares to capture the local characteristics of the data. For each prediction point Construct a dedicated local approximation model. The specific implementation effect in the profile curve is as follows: 1) Use the Gaussian kernel function: for the prediction point window neighborhood each sample point is assigned a corresponding weight, where denotes the bandwidth parameter, which controls the speed of weight decay (bandwidth parameter), denotes the Gaussian kernel function, n denotes the th wheel data sample point, n , i denotes the total number of wheel data samples in the window, i denotes the th wheel data sample; 2) Construct the weight matrix for each prediction point ; 3) Local polynomial fitting at the prediction point In the neighborhood of each data sample point, a polynomial function is used for data fitting, i.e. for each data sample point , there is ; wherein denotes the polynomial degree (here, in order to stabilize the data fitting, the value is taken) and denotes the sample polynomial basis function matrix ; denotes the coefficient vector matrix ; 4) Solving the coefficients by weighted linear least squares method: minimizing the weighted error sum of squares in the local neighborhood: ; Taking the derivative of and setting it to zero, we get ; 5) Data prediction: substituting each prediction point into the fitting polynomial to get the predicted value ; wherein denotes the predicted value, denotes the constant coefficient matrix, denotes the d th order coefficient matrix, denotes the th power of the prediction point d , X denotes the wheel data sample polynomial basis function matrix, denotes the transpose of , denotes the weight diagonal matrix, denotes the column vector of consisting of the sample points , , denotes the th power of d , denotes the n th wheel data sample point, denotes the corresponding Gaussian kernel function when i = n , denotes the Gaussian kernel function, denotes the bandwidth parameter.

[0049] Repeating the above steps, the corresponding predicted value of each prediction point is obtained, and the fitting profile curve of the first and second wheel profile data is obtained.

[0050] Step four, for the three processed wheel profile data, weighted linear least squares method is used for global data fitting processing to ensure the continuity and smoothness of the overall data.

[0051] After data processing, the obtained wheel profile model is compared with the design model, and the wheel state is judged by the model difference, which provides data support for the wheel health management system.

Claims

1. A non-contact wheel profile detection device, characterized by, The rack unit (1), the point laser measurement unit (2), the positioning and fixing unit (3) and the data interaction unit (4) are included. The rack unit (1) is used for driving the point laser measurement unit (2) to continuously measure after fixing the reference position. The point laser measurement unit (2) is used for continuously measuring the wheel cross-section point data. The positioning and fixing unit (3) is used for fixing the reference position by taking the edge circular arc as the reference position. The data interaction unit (4) is used for storing the wheel cross-section point data and inputting the continuously measured wheel cross-section point data to the PC end.

2. The non-contact wheel profile detection device according to claim 1, characterized by The rack unit (1) includes a rack (101), an upper shell (102) located above the rack (101) and connected with the rack (101), a shell front end (104), a touch display screen (103) and a shell rear end (105), a lower shell (107) located below the rack (101) and connected with the rack (101), a bottom shell (108) connected with the lower shell (107), and a support frame (106) connected with the bottom shell (108) and the lower shell (107); the rack (101) is connected with the point laser measurement unit (2) and the data interaction unit (4), and the support frame (106) is connected with the positioning and fixing unit (3).

3. The non-contact wheel profile detection device according to claim 2, characterized by The point laser measurement unit (2) includes straight line guide rails (204) and a motor support seat (202) mounted on the rack (101), a driving motor (201) mounted on the motor support seat (202), a synchronous belt pulley (203) connected with the driving motor (201), a synchronous belt idler pulley (208) meshed with the driving motor (201), an idler pulley mounting seat (209) connected with the synchronous belt idler pulley (208), a mounting seat (205) connected with the straight line guide rails (204), a synchronous belt pressing plate (207) connected with the mounting seat (205), and a laser displacement sensor (206) mounted on the mounting seat (205); the synchronous belt pressing plate (207) is connected with the synchronous belt (210) and the mounting seat (205); the idler pulley mounting seat (209) is mounted on the rack (101), and the synchronous belt pulley (203) is connected with the driving motor (201).

4. The non-contact wheel profile detection device according to claim 2, characterized by The positioning and fixing unit (3) includes a positioning rod (301) and a magnet assembly (302) connected with the support frame (106); the positioning rod (301) and the magnet assembly (302) are locked and mounted on the support frame (106) by screws.

5. A non-contact wheel profile detection method characterized by, The following steps are included: S1, taking the edge circular arc as the reference position, using the positioning rod (301) to align the wheel rim outer circular arc part, and using the magnet assembly (302) to stick to the wheel inner side plane and be adsorbed; S2, using the driving motor (201) to drive the synchronous belt pulley (203), the synchronous belt pulley (203) drives the meshed synchronous belt (210) to move, so that the mounting seat (205) of the laser displacement sensor (206) also displaces, and the laser displacement sensor (206) is used to continuously measure the wheel cross-section point data; S3, inputting the continuously measured wheel section point data to the PC end by using the data interaction unit (4); S4, detecting and removing the jump points by using the moving average method on the PC end, and segmenting the removed wheel section point data according to the wheel profile characteristics to obtain the first segment wheel profile data, the second segment wheel profile data and the third segment wheel profile data respectively; S5, processing the first segment wheel profile data and the second segment wheel profile data by using the moving average method, and processing the third segment wheel profile data by using the interquartile range method; S6, processing the first segment wheel profile data and the second segment wheel profile data processed by the moving average method by using the local weighted regression; S7, fitting the first segment wheel profile data, the second segment wheel profile data and the third segment wheel profile data processed by using the weighted linear least square method to obtain a complete fitting profile curve; S8, comparing the obtained complete fitting profile curve with the profile curve output by the standard design model to obtain the wheel surface wear data, and completing the detection of the non-contact wheel profile.

6. The non-contact wheel profile detection method according to claim 5, characterized by, The S4 includes the following steps: detecting the jump points by using the moving average method on the PC end, and processing the wheel section point data by using the large window iteration, wherein the large window is used for preliminarily processing the jump points; dividing the wheel section point data into three segments by using the highest point and the position 30mm before the nominal rolling circle, wherein the first segment wheel profile data is recorded before the highest point, the second segment wheel profile data is recorded from the highest point to the position 30mm before the nominal rolling circle, and the third segment wheel profile data is recorded after the position 30mm before the nominal rolling circle.

7. The non-contact wheel profile detection method according to claim 5, characterized by, The processing of the third segment wheel profile data by using the interquartile range method includes the following steps: sorting the third segment wheel profile data from small to large, dividing three segmentation points in four equal parts, and calculating the quartiles Q1 and Q3, wherein Q1 represents the lower quartile and Q3 represents the upper quartile; calculating the interquartile range IQR by using the difference between the quartiles Q1 and Q3; Based on the interquartile range IQR, the normal value range of the third section wheel profile data is defined, wherein the lower limit is the upper limit is and the range other than the upper and lower limits is defined as an abnormal value; cleaning the abnormal data to complete the smoothing processing of the third segment wheel profile data.

8. The non-contact wheel profile detection method according to claim 5, characterized by, The S6 includes the following steps: Using a Gaussian kernel function, the predicted points are... window Each wheel data sample point in the neighborhood Assign corresponding weights, where , n This indicates the total number of wheel data samples within the window. i Indicates the first i One wheel data sample; For each prediction point , construct a diagonal matrix of weights; In the field of predicting points polynomial functions are used for wheel data fitting; Based on the wheel data fitting results and the constructed weight diagonal matrix, the coefficient vector matrix is solved by weighted linear least square method ; based on the coefficient vector matrix and the prediction point to obtain a prediction value ; based on each prediction point the resulting prediction value the processing of the first and second segments of wheel profile data is completed.

9. The non-contact wheel profile detection method according to claim 8, characterized by, the predicted value The expression is as follows: ； ； ； ； ； wherein denotes the predicted value, denotes the constant coefficient matrix, denotes d the polynomial coefficient matrix, denotes the prediction point to the d power, X denotes the polynomial basis function matrix of the wheel data samples, denotes the transpose of denotes the weight diagonal matrix, denotes the column vector of the sample points comprising the polynomial basis function matrix, , denotes the d power, denotes the n wheel data sample point, denotes i = n the corresponding Gaussian kernel function, denotes the Gaussian kernel function, denotes the bandwidth parameter.

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