CN103587548B - The city rail vehicle wheel out of round degree method of inspection that sensor is directly measured - Google Patents

Abstract

The invention discloses a device and method for detecting the out-of-roundness of urban rail vehicle wheels directly measured by a sensor. The device includes a central processing unit and a plurality of laser sensors, and the laser sensors are all connected to the central processing unit; the rails in the detection section deviate outward, and guard rails are arranged inside the rails in the detection section; the laser sensors are arranged on the rails Between the area vacated by the offset and the guard rail, the probes of the laser sensors are arranged along the direction of the rail and are located under the wheel, and all the laser sensors are coplanar with the circumference of the wheel for out-of-roundness measurement. This method uses multiple laser sensors, installs them under the wheel according to a certain geometric relationship, selects the detection point where the wheel passes through the measurement range of each sensor, obtains the diameter corresponding to each sensor through least square fitting, and then uses the largest The minimum value is subtracted from the value to get the wheel out of roundness. The online non-contact measurement of the present invention has the advantages of fast speed, high precision and large measuring diameter range.

Description

Detection method of urban rail vehicle wheel out-of-roundness directly measured by sensor

technical field

The invention relates to the field of railway wheel detection, in particular to a device and method for detecting out-of-roundness of urban rail vehicle wheels directly measured by a sensor.

Background technique

During the operation of urban rail vehicles, there will be different degrees of wear and tear, which will affect the safe operation of the wheels, and the polygonal wheel tread caused by uneven wear is particularly important, which poses a serious threat to the safety of the train, making the locomotive The dynamic effect of the vehicle on the line and itself is greatly increased, and at the same time it will bring additional vibration and impact, reduce the critical speed of the rolling stock, and make the stability and comfort of the train worse. Therefore, the measurement of the out-of-roundness of the wheel tread is of great significance to the safe operation of the train.

The detection methods of wheel roundness are mainly divided into static detection and dynamic monitoring. Static detection needs to be carried out when the train is stopped or the wheels are disassembled, which not only takes up the turnaround time of the train, but also is slow and labor-intensive; dynamic monitoring can not only realize the On-line monitoring of wheelsets, with a high degree of automation, does not take up vehicle turnover time, and is convenient for storing information. Currently, the dynamic monitoring methods for out-of-roundness include vibration acceleration detection method and contact measurement method:

The vibration acceleration detection method extracts the out-of-roundness information of the wheel by analyzing the vibration of the track when the whole train passes the detection point. However, this method is affected by the sensor installation fixture and the vibration attenuation of sleepers, and the measurement accuracy is not high. The typical contact measurement method is the parallelogram method, patent 1 (on-line dynamic detection device for lifting wheel tread damage and out-of-roundness, application number: 200720082608.9, application date: 2007-12-20) and patent 2 (a wheel tread Insertion and out-of-roundness online detection device, application number: 201210307496.8, application date: 2012-08-27) both disclose the online measurement method of parallelogram structure and its improvement. In this method, the displacement sensor and the parallelogram mechanism are fixed The support on one side of the rail is connected. The sensor can directly measure the change in the relative height between the wheel tread and the wheel rim. The displacement sensor records the diameter of the entire tread circumference. However, this method uses contact measurement, which is not suitable for trains passing at high speed, and has problems such as slow measurement response speed, low mechanical structure life, and difficulty in engineering implementation.

Contents of the invention

The purpose of the present invention is to provide a high-precision sensor direct measurement of urban rail vehicle wheel out-of-roundness detection device, using non-contact measurement, fast detection speed, large measurement range.

The technical solution for realizing the object of the present invention is: a kind of urban rail vehicle wheel out-of-roundness detection device directly measured by a sensor, including a central processing unit and a plurality of laser sensors, and the laser sensors are all connected with the central processing unit; the detection section The rails of the detection section are offset outwards, and guard rails are set on the inner side of the rails in the detection section, and the guard rails are tangent to the inner side of the wheel rim; the laser sensor is set between the area vacated by the rail offset and the guard rails, and the laser sensor The probes are arranged along the direction of the rail and distributed on the horizontal line, the detection beams of each laser sensor are vertical to the rail, and all the laser sensors are coplanar with the circumference of the wheel for out-of-roundness measurement.

A method for detecting the out-of-roundness of an urban rail vehicle wheel directly measured by a sensor, comprising the following steps:

Step 1, install each laser sensor in the area vacated by the rail offset, so that the probes of each laser sensor are arranged along the direction of the rail and are all located under the wheel. All laser sensors are in the same plane as the wheel circumference for diameter measurement. The laser sensor Denoted as P _i , along the rail direction i is sequentially 1, 2,...n, n is the number of laser sensors;

The second step is to establish a two-dimensional coordinate system on the plane where the wheel circumference for diameter measurement is located: the X axis along the rail direction, the Y axis passing through the first laser sensor P ₁ and perpendicular to the rail, then the coordinates of the laser sensor are (x _i , y _i ), the installation inclination angle of each laser sensor probe relative to the X axis is 90°;

Step 3, collect the output values of all laser sensors, and select a valid data set {S _i } output by a sensor, where S _i is the output value of the i-th sensor P _i , i=1,2,... n;

Step 4: Determine the speed of the train passing each sensor P:

v _i =d _i /t _i

where d _i =(x _i -x _i-1 )/2+(x _i+1 -x _i )/2, t _i =(t _pi -t _pi-1 )/2+(t _pi+1 -t _pi )/2, t _pi is the moment when the i-th sensor P _i outputs the minimum value, t _pi+1 is the moment when the i+1th sensor P _i+1 outputs the minimum value, and t _pi-1 is the moment when the i-1th sensor P i+1 outputs the minimum value The moment when a sensor P _i-1 outputs the minimum value, and it is assumed that it is a constant speed in the interval of the sensor P;

Step 5: Determine the coordinates of the measuring point corresponding to the sensor P _i on the wheel according to the output value S _i and the coordinate value ( _xi , y _i ) of the sensor P _i , and take t as the time axis to select the wheel passing the sensor P _i Measuring range Point coordinates in (X _it ,Y _it ):

(X _it ,Y _it )=(x _i ,y _i )+(tv _i /f,S _it )i=1,2...nt=1,2...

Among them: i represents the i-th sensor; t is the sampling moment; f is the sampling period; v _i is the speed of the wheel passing the sensor P _i ;

Step 6: Fit the circle according to the sequence points (X _it , Y _it ), and obtain the wheel diameter D _{i corresponding to the sensor P i ;}

Step 7, repeat steps 5-6, calculate and fit the coordinates of the measurement points for the effective data set S _i collected by each sensor to obtain n wheel diameters, and fit the n sensors to obtain the n diameters Subtract the minimum value from the maximum value to get the quantitative value E of the wheel out-of-roundness.

Compared with the prior art, the remarkable advantages of the present invention are: (1) Based on the laser detection system, the on-line non-contact measurement of the train wheels is realized through the least squares fitting algorithm, and the measurement accuracy is high; (2) The laser sensor Automatically obtain the coordinates of any multi-point of the wheel, and obtain the diameter of the currently measured wheel through the corresponding data processing algorithm, and subtract the minimum value from the maximum value of the diameter to obtain the quantitative index of out-of-roundness, which is simple, convenient and fast to operate; (3) Automatically obtain (4) It has the advantages of fast detection speed and large measurement range.

Description of drawings

Figure 1 is a schematic diagram of the wear of the wheel tread after running.

Fig. 2 is a structural diagram of the out-of-roundness detection device of the urban rail vehicle wheel directly measured by the sensor of the present invention.

Fig. 3 is a schematic diagram of a rail switching point in the wheel out-of-roundness detection device of an urban rail vehicle according to the present invention.

Fig. 4 is a cross-sectional schematic diagram of the distance Q of the rail offset and the size of the guard rail in the present invention.

Fig. 5 is a schematic diagram of the out-of-roundness detection of the wheel in which the laser sensor is installed in a straight line and vertically in the embodiment.

Fig. 6 is the relation of measured value S of 9 laser sensors with time t (ms) in the embodiment.

Fig. 7 shows the output (X _it , Y _it ) of a certain sensor measurement point and its fitted circle in the embodiment.

FIG. 8 is a schematic diagram of the results obtained by repeated measurement of the out-of-roundness 20 times in the embodiment.

detailed description

The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

Figure 1 shows the shape of the tread surface of a certain wheel after running and the shape of the tread surface when it is just put into operation. It can be seen that the wear concentration point is 70mm away from the side of the wheel rim, which is the location where the diameter is commonly used in engineering, and the wheel diameter It is often controlled between 770 and 840mm, so the detection point of the laser sensor is selected as the wheel circumference at this place.

The wheel out-of-roundness detection device for urban rail vehicles based on a laser sensor in the present invention includes a central processing unit and a plurality of laser sensors, and the laser sensors are all connected to the central processing unit; the rails in the detection section deviate outward, and the detection A guard rail is set on the inner side of the rail in the section, and the guard rail is tangent to the inner side of the wheel rim; the laser sensor is set between the area vacated by the rail offset and the guard rail, and the probes of the laser sensor are arranged along the direction of the rail and distributed on the horizontal line , the detection beams of each laser sensor are vertical to the rail, and all the laser sensors are coplanar with the circumference of the wheel for out-of-roundness measurement.

As shown in Figure 2, the steel rail 6 is deflected outward in the detection section, and a certain area is vacated, the laser sensor probe 3 is installed below the measurement point of the wheel 1, and the guard rail 5 is set on the inner side of the wheel rim to prevent the wheel pair from snaking or shaft To move to cause derailment, the laser sensor probe 3 is fixed by the sensor fixture 4, and the position and inclination angle of the laser sensor probe 3 can be adjusted, and the laser beam 2 emitted by each laser sensor probe 3 can simultaneously detect the corresponding detection point on the wheel.

As shown in Figure 3, the switching point where the rails are shifted outward is arc-shaped, which is beneficial for the train to enter and exit the detection area. Figure 4 illustrates the specific dimension Q of the outward offset of the rail. For the wheel tread and 60 rails, Q is controlled between 50 and 65mm, so that the centerline of the track does not exceed the outer edge of the wheel. The dimension P of the guard rail higher than the rim is controlled between 30 and 50mm. The distance from the circumference of the wheel for diameter measurement to the side of the wheel rim is 70mm.

Since the wheel to be measured is in long-term contact with the track, the surface is smooth and rough, so it involves the use of a laser scanning probe to measure the profile of a metal surface with strong specular reflection. This measured object is a difficult point in the field of shape measurement. Zhang Liang et al. analyzed the measurement capabilities of several existing laser probes on metal surfaces, and concluded that conoscopic polarization holographic probes and oblique beam triangular probes are more suitable for measuring metal surfaces (Zhang Liang, Fei Zhigen, Guo Junjie. Laser scanning Research on measuring probes on metal surfaces, Machine Tools and Hydraulics, Vol. 39 No. 9: May 2011). Therefore, the laser sensors involved in the present invention are preferably conoscopic polarization holographic probes and oblique triangular probes, the number of laser sensors is 3-20 and the probes of all laser sensors are fixed under the wheel through sensor fixtures.

The method for detecting the wheel out-of-roundness using the urban rail vehicle wheel out-of-roundness detection device directly measured by the above sensors comprises the following steps:

Step 1, install each laser sensor in the area vacated by the rail offset, so that the probes of each laser sensor are arranged along the direction of the rail and are all located under the wheel. All laser sensors are in the same plane as the wheel circumference for diameter measurement. The laser sensor Denoted as P _i , along the rail direction i is sequentially 1, 2,...n, n is the number of laser sensors;

The second step is to establish a two-dimensional coordinate system on the plane where the wheel circumference for diameter measurement is located: the X axis along the rail direction, the Y axis passing through the first laser sensor P ₁ and perpendicular to the rail, then the coordinates of the laser sensor are (x _i , y _i ), the installation inclination angle of each laser sensor probe relative to the X axis is 90°;

Step 3, collect the output values of all laser sensors, and select a valid data set {S _i } output by a sensor, where S _i is the output value of the i-th sensor P _i , i=1,2,... n;

Step 4: Determine the speed of the train passing each sensor P:

v _i =d _i /t _i

where d _i =(x _i -x _i-1 )/2+(x _i+1 -x _i )/2, t _i =(t _pi -t _pi-1 )/2+(t _pi+1 -t _pi )/2, t _pi is the moment when the i-th sensor P _i outputs the minimum value, t _pi+1 is the moment when the i+1th sensor P _i+1 outputs the minimum value, and t _pi-1 is the moment when the i-1th sensor P i+1 outputs the minimum value The moment when a sensor P _i-1 outputs the minimum value, and it is assumed that it is a constant speed in the interval of the sensor P;

Step 5: Determine the coordinates of the measuring point corresponding to the sensor P _i on the wheel according to the output value S _i and the coordinate value ( _xi , y _i ) of the sensor P _i , and take t as the time axis to select the wheel passing the sensor P _i Measuring range Point coordinates in (X _it ,Y _it ):

(X _it ,Y _it )=(x _i ,y _i )+(tv _i /f,S _it )i=1,2...nt=1,2...

Among them: i represents the i-th sensor; t is the sampling moment; f is the sampling period; v _i is the speed of the wheel passing the sensor P _i ;

Step 6: Fit the circle according to the sequence points (X _it , Y _it ) to obtain the wheel diameter D _{i corresponding to the sensor P i ;} use the least square method to fit the circle, the formula is as follows:

$\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; =</span>\sqrt{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; b</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 4</span>}\frac{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; a\&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; b\&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1,2</span>}<span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; no</span>}$

Wherein, a is -2 times of the abscissa x ₀ of the fitted circle center, i.e. a=-2x ₀ , b is -2 times of the fitted circle center ordinate y ₀ ie b=-2y ₀ , and

$\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; HD</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; EG</span>}}{\mathrm{<span\; class="notranslate">\; CG</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$ $\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; HC</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; ED</span>}}{{\mathrm{<span\; class="notranslate">\; D.</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; GC</span>}}$

Among them, C, D, E, G, and H are intermediate parameters, which are as follows:

$\left.\begin{array}{c}\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; n\&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ \mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; n\&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ \mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; n\&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; n\&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ \mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; n\&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ \mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; n\&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; n\&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\end{array}\right\}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1,2</span>}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; no</span>}$

Step 7, repeat steps 5-6, calculate and fit the coordinates of the measurement points for the effective data set S _i collected by each sensor to obtain n wheel diameters, and fit the n sensors to obtain the n diameters Subtract the minimum value from the maximum value to get the quantitative value E of the wheel out-of-roundness.

In the following, in conjunction with specific embodiments, the device and method for detecting the out-of-roundness of an urban rail vehicle wheel with sensors installed in a straight line vertical or a straight line inclination are respectively introduced, and the present invention will be further described in detail.

Example

This embodiment is an urban rail vehicle wheel out-of-roundness detection device and method in which sensors are installed vertically in a straight line.

As shown in Figure 5, the probes of n laser sensors are arranged along the direction of the rail and evenly distributed on the horizontal line, and the detection beams of each laser sensor are vertical to the rail.

The installation parameters of the laser sensor meet the following conditions: the number n of laser sensors is 9, the distance between adjacent laser sensors is 300mm, and the vertical distance between the installation point of the laser sensor and the rail is | _y1 |100mm. In this installation scheme, the distance measured by 6 or more sensors at the same time exceeds 2800mm, covering the case of the largest wheel diameter of 840×3.14=2637.6mm, which can detect the entire circumference of the wheel tread, and makes the sensor’s measurement light and the measured surface The inclination angle is controlled between ±20° to avoid sensor measurement errors caused by changing the inclination angle. The coordinates (x _i , y _i ) of each sensor are thus obtained (unit: mm):

x _i =300(i-1)i=1,2...9

where i represents the i-th sensor;

Assuming that the sampling period of the laser sensor is 1kHz, the measurement random error is 0.05mm, and assuming that the train running speed is 1m/s, the measurement data of the measured wheel with a diameter of 800 is generated by computer simulation as shown in Figure 6, and the measurement data is obtained according to the following steps Output out of roundness:

(1.1) Collect the output values of all laser sensors, and select an effective data group {S _i } with 9 sensor output values at the same time, S _i is the output value of the i-th sensor P _i , i=1,2,. ..n;

(1.2) Determine the coordinates (X _it , Y _it ) of the measuring point corresponding to the sensor P _i on the wheel according to the output value S _i and the coordinate value (x _i , y _i ) of the sensor P _i . Figure 7 draws the first A sensor outputs the sequence point (X _it ,Y _it ) determined by S _i and the fitted circle at this moment;

(1.3) According to the sequence point (X _it , Y _it ) to fit the circle, the wheel diameter corresponding to this sensor is obtained as 799.913mm. The diameter error sequence obtained after fitting the measured output S _i of nine sensors is:

ε=[-0.08700.1606-0.08200.09130.1481-0.1556-0.06810.0824-0.2800]

(1.4) For the diameter error sequence measured by the 9 sensors obtained in step (1.3), take the maximum value of the diameter and subtract the minimum value to obtain an out-of-roundness of 0.4406. After 20 simulation measurements, the measurement results shown in Figure 8 are obtained. It can be seen from the measurement results that this embodiment can realize high-precision measurement of wheel out-of-roundness, and the measurement error is <0.8mm without considering the installation error.

In summary, based on the laser detection system, the present invention realizes the online non-contact measurement of the out-of-roundness of the train wheel through the least squares fitting algorithm, with high measurement accuracy, simple operation, convenient and fast, and has the advantages of fast detection speed, The advantage of a large measuring range.

Claims (2)

1. an urban rail vehicle wheel out-of-roundness detection method directly measured by a sensor, is characterized in that, comprises a central processing unit and a plurality of laser sensors, and the laser sensors are all connected with the central processing unit; the steel rail of the detection section is outward The inner side of the rail in the detection section is set with a guard rail, which is tangent to the inner side of the wheel rim; the laser sensor is set between the area vacated by the rail offset and the guard rail, and the probe of the laser sensor is along the direction of the rail Arranged and distributed on the horizontal line, the detection beams of each laser sensor are vertical to the rail, and all the laser sensors are coplanar with the wheel circumference for out-of-roundness measurement, specifically including the following steps: Step 1, install each laser sensor in the area vacated by the rail offset, so that the probes of each laser sensor are arranged along the direction of the rail and are all located under the wheel. All laser sensors are in the same plane as the wheel circumference for diameter measurement. The laser sensor Denoted as P _i , the direction i along the rail is 1, 2, Kn in sequence, and n is the number of laser sensors; The second step is to establish a two-dimensional coordinate system on the plane where the wheel circumference for diameter measurement is located: the X axis along the rail direction, the Y axis passing through the first laser sensor P ₁ and perpendicular to the rail, then the coordinates of the laser sensor are (x _i , y _i ), the installation inclination angle of each laser sensor probe relative to the X axis is 90°; The third step is to collect the output values of all laser sensors, and select the effective data group {S _i } output by each sensor, S _i is the output value of the i-th sensor P _i , i=1, 2, Kn; Step 4: Determine the speed of the train passing each sensor P: v _i =d _i /t _i where d _i =(x _i -x _i-1 )/2+(x _i+1 -x _i )/2, t _i =(t _pi -t _pi-1 )/2+(t _pi+1 -t _pi )/2, t _pi is the moment when the i-th sensor P _i outputs the minimum value, t _pi+1 is the moment when the i+1th sensor P _i+1 outputs the minimum value, and t _pi-1 is the moment when the i-1th sensor P i+1 outputs the minimum value The moment when a sensor P _i-1 outputs the minimum value, and it is assumed that it is a constant speed in the interval of the sensor P; Step 5: Determine the coordinates of the measuring point corresponding to the sensor P _i on the wheel according to the output value S _i and the coordinate value ( _xi , y _i ) of the sensor P _i , and take t as the time axis to select the wheel passing the sensor P _i Measuring range Point coordinates in (X _it ,Y _it ): (X _it ,Y _it )=(x _i ,y _i )+(tv _i /f,S _it )i=1,2...nt=1,2... Among them: i represents the i-th sensor; t is the sampling moment; f is the sampling period; v _i is the speed of the wheel passing the sensor P _i ; Step 6: Fit the circle according to the sequence points (X _it , Y _it ), and obtain the wheel diameter D _{i corresponding to the sensor P i ;} Step 7, repeat steps 5-6, calculate and fit the coordinates of the measurement points for the effective data set S _i collected by each sensor to obtain n wheel diameters, and fit the n sensors to obtain n diameters The maximum value is subtracted from the minimum value to obtain the quantitative value E of the wheel out-of-roundness. 2. the urban rail vehicle wheel out-of-roundness detection method that sensor directly measures according to claim 1, is characterized in that, described in the 6th step carries out fitting according to n measuring point coordinates (X _i , Y _i ) on the wheel Circle, using the least square method, the formula is as follows: $\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; =</span>\sqrt{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; b</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 4</span>}\frac{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a\&Sigma;X</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; b\&Sigma;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ...</span>\mathrm{<span\; class="notranslate">\; no</span>}$ Wherein, a is -2 times of the abscissa x ₀ of the fitted circle center, i.e. a=-2x ₀ , b is -2 times of the fitted circle center ordinate y ₀ ie b=-2y ₀ , and $\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; E.</span>}\mathrm{<span\; class="notranslate">\; G</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$ $\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; E.</span>}\mathrm{<span\; class="notranslate">\; D.</span>}}{{\mathrm{<span\; class="notranslate">\; D.</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; G</span>}\mathrm{<span\; class="notranslate">\; C</span>}}$ Among them, C, D, E, G, and H are intermediate parameters, which are as follows: $\left.\begin{array}{c}\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; n\&Sigma;X</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;X</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;X</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ \mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; n\&Sigma;X</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;X</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ \mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; n\&Sigma;X</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; n\&Sigma;X</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;X</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ \mathrm{<span\; class="notranslate">\; G</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; n</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ \mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; n\&Sigma;X</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; n\&Sigma;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\end{array}\right\}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ...</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; .</span>$