CN113324476A - Crane guide rail detection system and detection method thereof - Google Patents
Crane guide rail detection system and detection method thereof Download PDFInfo
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Abstract
The invention provides a crane guide rail detection system and a detection method thereof.A light curtain sensor is arranged to obtain the appearance characteristics of a guide rail on a guide rail surface, so that whether the guide rail is broken can be judged, and the wear degree of the guide rail surface can be quantitatively evaluated; providing a correction basis for correcting errors of the test data caused by the flatness; the angle sensor is arranged and used for detecting angle values in X, Y and Z directions when the crane guide rail detection robot runs on the guide rail, the running state of the current crane guide rail detection robot is known based on the angle values, and the vertical height difference in a non-vertical state can be eliminated based on the running state by adopting a corresponding error elimination method; through setting up vibration sensor for detect crane guide rail inspection robot self natural vibration information and the vibration information that causes because of the rail surface unevenness in its operation process, provide the correction basis for revising test data because of vibrating and leading to the error.
Description
Technical Field
The invention relates to the field of crane rail detection, in particular to a crane guide rail detection system and a detection method thereof.
Background
The rail is used as a bearing device of the crane, bears the self weight of the crane and the gravity of a lifted cargo, the condition of the rail directly influences the use of the crane, but the rail gnawing phenomenon is most easily generated in the actual use, and the rail gnawing means that the wheel rim is in forced contact with the side surface of the rail to generate horizontal lateral thrust due to the influence of a plurality of complex factors in the running process of a crane cart or a trolley, so that the severe friction between the wheel rim and the rail is caused, and the severe abrasion is generated between the wheel rim and the side surface of the rail.
At present, measurement equipment such as a steel ruler, a steel tape, a steel wire, a level, a theodolite, a level and the like are still commonly used for measuring a crane track in a segmented manner. Wherein, the theodolite mainly comprises a sighting device, a horizontal dial and a vertical dial; the sighting device can move up and down in a vertical plane and can rotate in a horizontal plane. The basic principle of detecting the deviation of the track central line by using a theodolite is as follows: the sight line optical path of the sighting device moves up and down to form a vertical plane which is vertical to the upper surface of the track, and the vertical plane must pass through central points A and B of two end faces of the track. As shown in fig. 1, the specific operation is: the method comprises the steps of firstly, dropping a vertical point of a theodolite on a central line (or an extension line) of a track, then, adjusting a horizontal dial to enable the theodolite to be in a horizontal position, adjusting a vertical dial to enable the theodolite to be in a vertical position, locking the theodolite to be incapable of horizontally rotating after a sighting device is rotated to be aligned with a central point B of the end face of the track, then, measuring according to preset intervals, and marking the central line position of the track at a measuring point by using a steel needle or a stone pen before measuring. The scale mark with the scale value b of the steel ruler is coincided with the central line of the track, the scale value of the sighting device is read through the theodolite, and the deviation value is the absolute value of the difference value between the reading value of the theodolite and the value b.
The above measuring tools and methods are greatly influenced by human factors and environmental changes, and have certain dangers to detection personnel. When the error value scale causing the problem of the rail is small, the small-size error value cannot be found by means of visual inspection, theodolite inspection and the like, and further the wear degree of the guide rail cannot be quantitatively evaluated based on the appearance characteristics of the guide rail. Therefore, in order to solve the above problems, the present invention provides a crane rail detection system and a detection method thereof, which can identify and detect a small-size error value and determine a rail wear degree based on the small-size error value.
Disclosure of Invention
In view of this, the invention provides a crane guide rail detection system and a detection method thereof, which can identify and detect a small-size error value and judge the guide rail wear degree based on the small-size error value.
The technical scheme of the invention is realized as follows: the invention provides a crane guide rail detection system, which comprises a total station, a guide rail detection robot and an external PC (personal computer), wherein the guide rail detection robot comprises a data acquisition and measurement module, a prism, a light curtain sensor, an angle sensor and a vibration sensor;
the center of the prism is positioned at the geometric center of the frame of the guide rail detection robot; the light curtain sensor, the angle sensor and the vibration sensor are respectively integrated in the body of the guide rail detection robot; the light curtain sensor is installed right opposite to the rail surface of the guide rail and used for detecting the height of the rail surface of the guide rail from the crane guide rail detection robot; the light curtain sensor, the angle sensor and the vibration sensor are respectively and electrically connected with the input end of the data acquisition and measurement module, and the output end of the data acquisition and measurement module is connected with an external PC;
the guide rail detection robot runs at a constant speed along a detected track, the total station is located at any position to build a station, the total station detects a fixed point at the center of the prism and obtains coordinate data of the point, the coordinate data of the point are converted into a global coordinate system, and the coordinate data are transmitted to an external PC.
On the basis of the above technical solution, preferably, the guide rail detection robot further includes: a displacement sensor and a speed sensor;
and the displacement sensor and the speed sensor are respectively integrated in the body of the guide rail detection robot and are respectively electrically connected with the input end of the data acquisition and measurement module.
In another aspect, the invention provides a crane guide rail detection method, which comprises the following steps:
s1, the total station is located at any position on the ground, the guide rail detection robot carries a prism to advance at a constant speed along a track A, a measuring head of the total station is set as a coordinate origin, a global coordinate system is established, the target coordinates An of the prism at n detection points are detected in sequence, the motion parameters of the guide rail detection robot at the n detection points are detected synchronously, the target coordinates An of the abnormal prism are corrected based on the motion parameters, and the track surface coordinates A1n are obtained; the motion parameters comprise angle information, contour information, vibration information and displacement information of the guide rail detection robot at the detection point;
s2, after the track A detection is finished, moving the guide rail detection robot to an initial detection point on the track B, acquiring track surface coordinates B1n at n detection points by adopting the same principle, and integrating n track surface coordinates B1n on the track B into a global coordinate system;
s3, fitting the n track surface coordinates A1n and the n track surface coordinates B1n by adopting a least square method to obtain a track A datum line and a track B datum line; measuring the straightness of the track A and the track B by adopting a chord measuring method;
s4, using the track surface coordinate A1n of the track A as a reference, adopting an interpolation algorithm to find the track surface coordinate B' n corresponding to the track surface coordinate A1n one by one, and calculating the track space and the coplanarity of the track A and the track B based on the two one-to-one corresponding detection point positions.
Based on the above technical solution, preferably, the step of "correcting the coordinates An of the target center of the abnormal prism based on the motion parameters and acquiring the coordinates A1n of the track surface" in S1 specifically includes the steps of:
s101, acquiring vibration frequency and vibration amplitude at n detection points based on a vibration sensor, and correcting abnormal prism coordinate data in the Z-axis direction in n prism target coordinates An based on the vibration frequency and the vibration amplitude;
s102, acquiring angle information of n detection points based on An angle sensor, and correcting abnormal prism coordinate data in the Y-axis direction in n prism target coordinates An based on the angle information;
s103, acquiring the height difference between the rail surface at the n detection points and the crane guide rail detection robot based on the light curtain sensor, and correcting abnormal prism coordinate data in the X-axis direction in the prism target center coordinates An based on the height difference;
s104, acquiring n corrected prism target coordinates An, correcting the prism target coordinates An based on An angle correction formula, and acquiring track surface coordinates A1 n.
On the basis of the above technical solution, preferably, S101 specifically includes the following steps:
s201, acquiring n prism target center coordinates An through a total station, and performing db4 packet decomposition on the n prism target center coordinates An to obtain n X, Y frequency items in the Z direction;
s202, acquiring vibration information of n detection points based on a vibration sensor, performing Fourier transform on the n vibration information, and acquiring running natural frequencies of the crane guide rail detection robot in X, Y and Z directions;
s203, obtaining X, Y and Z direction similar frequency items in the decomposition of db4 small packets based on the inherent frequency of the crane guide rail detection robot running in X, Y and Z directions, and replacing the frequency values in the corresponding directions of m adjacent detection points with the frequency value mean values in the corresponding directions of the detection points to obtain reconstructed target coordinates An; wherein m is less than n;
s204, sequencing the amplitudes in the Z-axis direction in the reconstructed target coordinates An according to the magnitude, setting a threshold value, setting a detection point exceeding the amplitude threshold value as An abnormal vibration point, and replacing the coordinate value of the abnormal vibration point on the Z axis with the coordinate mean value of p adjacent points of the abnormal vibration point on the Z axis; wherein p is less than n.
On the basis of the above technical solution, preferably, S102 specifically includes the following steps:
s301, acquiring angle information of the crane guide rail detection robot at n detection points in X, Y and Z-axis directions based on an angle sensor;
s302, setting An angle threshold value in the Y-axis direction, setting a detection point of which the Y-axis numerical value exceeds the angle threshold value in n bull' S-eye coordinates An as An abnormal angle point, and replacing the coordinate value of the abnormal angle point on the Y-axis with the coordinate mean value of q adjacent points of the abnormal angle point on the Y-axis; wherein q is less than n.
On the basis of the above technical solution, preferably, 103 specifically includes the following steps:
s401, height differences between the rail surfaces of the n detection points and the crane guide rail detection robot are obtained based on the light curtain sensor;
s402, setting a height difference threshold, setting a detection point exceeding the height difference threshold as an abnormal contour point, and replacing a coordinate value of the abnormal contour point on an X axis with a coordinate mean value of h adjacent points of the abnormal contour point on the X axis; wherein h is less than n.
Based on the above technical solution, preferably, in S104, the "angle correction formula" is:
if the prism is in the horizontal state, the relationship between the track surface coordinate A1n and the prism bull's eye coordinate An is as follows:
wherein (x)i,yi,zi) The coordinate An of the target center of the prism at the ith detection point on the track A; (x)1i,y1i,z1i) The coordinate of the rail surface of the ith detection point on the rail surface is obtained; h is1Is the prism support height; h is2Is the height of the vehicle body;
if the prism is in a state of rotating around the X-axis direction, the relationship between the track surface coordinate A1n and the prism bull's-eye coordinate An is as follows:
wherein alpha is a front-back pitch angle measured by a total station;
if the prism rotates around the Y-axisIn the dynamic state, the relationship between the track surface coordinate A1n and the prism bulls-eye coordinate An is as follows:
wherein beta is a left roll angle and a right roll angle, and is measured by a total station.
On the basis of the above technical solution, preferably, in S4, with the track surface coordinate A1n of the track a as a reference, the track surface coordinate B' 1n corresponding to the track surface coordinate A1n position one by one is found by using a median interpolation algorithm, including the following steps:
s501, obtaining coordinates of initial detection points A11 and B11 of the track A and the track B, and matching the position relation of the initial points;
s502, matching and detecting a mapping relation F (x) between the displacement distance and the coordinate;
s503, with n track surface coordinates A1n points of the track A as base points, searching the track surface coordinates B' 1n of the track B corresponding to the position of the track A one by one in space based on the mapping relation F (x), and calculating the space coordinate values of the coordinate points.
Based on the above technical solution, preferably, the step of calculating the coplanarity of the track a and the track B based on the two one-to-one corresponding detection point positions in S4 includes the steps of:
s601, solving a reference plane based on the track A reference line and the track B reference line;
s602, with the n bull ' S-eye coordinates An of the track A as base points, respectively calculating the distances from the n bull ' S-eye coordinates An and the coordinate point B ' n to the reference plane based on a point-to-plane distance formula.
Compared with the prior art, the crane guide rail detection system and the detection method thereof have the following beneficial effects:
(1) the appearance characteristics of the guide rail on the rail surface of the guide rail are obtained by arranging the light curtain sensor, so that whether the guide rail is broken or not can be judged, and the abrasion degree of the rail surface of the guide rail is quantitatively evaluated; adjusting the direction of a plumb of the prism according to the smoothness data of the guide rail, and ensuring the consistency of the test data of the total station; providing a correction basis for correcting errors of the test data caused by the flatness;
(2) the angle sensor is arranged and used for detecting angle values in X, Y and Z directions when the crane guide rail detection robot runs on the guide rail, the running state of the current crane guide rail detection robot is known based on the angle values, and the vertical height difference in a non-vertical state can be eliminated based on the running state by adopting a corresponding error elimination method;
(3) the vibration sensor is arranged and used for detecting the inherent vibration information of the crane guide rail detection robot and the vibration information caused by uneven rail surface in the running process of the crane guide rail detection robot, so that a correction basis is provided for correcting errors of test data caused by vibration;
(4) correcting the coordinate An of the target center of the abnormal prism based on the vibration information at the detection point, and removing the abnormal value of the coordinate An of the target center of the prism on the coordinate axis caused by the factors such as the inherent vibration frequency of the crane guide rail detection robot, the rail surface fracture and the like;
(5) correcting the coordinates An of the target center of the abnormal prism based on the angle information at the detection point, and removing the measurement error caused by the deviation or loss of the prism on the Y axis;
(6) correcting the coordinates An of the target center of the abnormal prism based on the track profile information at the detection point, and avoiding measurement errors caused by corrosion or pot holes on the track surface of the crane guide rail detection robot;
(7) the method comprises the steps of taking a track surface coordinate A1n of a track A as a reference, obtaining a track surface coordinate B' 1n which is in the same position as the track surface coordinate A1n by adopting a Lagrange difference algorithm, making up for missing data, solving the problem that data detected on the track A and data detected on the track B are not in one-to-one correspondence in the prior art when track distance and coplanarity are calculated, and ensuring the accuracy of the coplanarity and the track distance;
(8) compared with the method for calculating the distance between the track A datum line and the track B datum line by adopting two linear distance formulas to replace the track gauge in the prior art, the method for acquiring the track gauge by adopting the space distance formula between the two points has smaller error and can acquire more accurate track gauge.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a theodolite detecting track centerline deviation in the prior art;
FIG. 2 is a schematic diagram of an embodiment of a crane rail detection system according to the present invention;
FIG. 3 is a schematic structural diagram of a crane rail detection system according to the present invention;
FIG. 4 is a schematic diagram of a total station detection in the crane rail detection system of the present invention;
FIG. 5 is a total station coordinate system in the crane rail detection system of the present invention;
FIG. 6 is a side view of a crane guide rail inspection system according to the present invention with the prism in a horizontal position;
FIG. 7 is a side view of a prism in a crane rail detection system according to the present invention, when the prism is rotating around the X-axis or Y-axis;
fig. 8 is a schematic diagram of fitting of detection points on a track a and a track B in the crane guide rail detection method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Example 1
As shown in fig. 2, when a total station is used for detecting a crane track, a crane guide rail detection robot carries An automatic tracking prism to move along the track, the total station tracks the position of the prism in real time, the spatial shape of a track top surface central line is reconstructed according to recorded data, the detection principle is as shown in fig. 4, firstly, two tracks of the total station are detected, a measuring head of the total station is set as An origin of coordinates, a global coordinate system is established, n points are detected in sequence, a required coordinate of a track top surface central point An can be obtained through calculation, after the track A is detected, a detection trolley is moved onto a track B, the coordinates of the n points are detected by the total station, and a Bn coordinate is also obtained. The relationship between the local coordinates and the global coordinates can be found out by utilizing the two groups of coordinates, then the coordinates of the track B detection points are integrated into the global coordinates, and the horizontal straightness of the top of the track, the straightness of the top of the center of the track, the span between the centers of the two tracks and the height difference between the corresponding two track detection points can be calculated.
At present, measurement equipment such as a straight steel ruler, a steel tape, a steel wire, a level, a theodolite or a level and the like are still commonly used for measuring a crane track in a segmented manner, and the measuring tool and the measuring method are greatly influenced by human factors and environmental changes and have certain danger for detection personnel. When the track has problems, part of the problems can be solved through common inspection and manual adjustment. However, in many cases, the error value scale causing the problem of the rail is small, visual inspection and common inspection cannot detect the shape feature of the guide rail with the small error value scale and realize flatness detection, and further the wear degree of the guide rail cannot be quantitatively evaluated based on the shape feature of the guide rail. Therefore, in order to solve the above problems, as shown in fig. 3, the present embodiment provides a crane rail inspection robot including a data acquisition and measurement module, a prism, a light curtain sensor, an angle sensor, and a vibration sensor.
The total station aims at a prism on the crane guide rail detection robot and tests the target coordinates of different positions of the track. In this embodiment, as shown in fig. 5, a total station is used as an origin to establish a coordinate system, the crane rail detection robot runs on the rail and aims at a reflection prism or a reflection target on the prism intelligent vehicle through the total station fixed at a certain position, and the spatial coordinates of the rail measurement point in the total station coordinate system can be obtained according to equation (1) to construct the spatial shape of the top surface center line of the rail by testing the slant distance from the center of the reflection prism or the reflection target to the total station and the rotation angles α and θ of the total station in the horizontal direction and the vertical direction. Wherein, formula (1) is as follows:
in the formula: (x)i,yi,zi) The coordinate An of the target center of the prism at the ith detection point on the track A; s is the slant distance from a detection point to the total station; theta is a vertical angle of the detection point; alpha is the horizontal angle of the detection point. The position of the total station is irrelevant to the measurement of parameters such as straightness accuracy and the like, and the site of the total station is not specifically required.
The relative position of the target of the prism and the central line of the top surface of the track is kept unchanged during the test, so that the motion track of the target can replace the contour of the central line of the top surface of the track. Preferably, in this embodiment, the center of the prism is located at the geometric center of the frame of the rail inspection robot.
And the light curtain sensor is integrated in the body of the crane guide rail detection robot, is arranged right opposite to the guide rail surface, and is used for detecting the height of the guide rail surface from the crane guide rail detection robot. In this embodiment, the appearance characteristics of the guide rail and the flatness of the guide rail are reflected based on the distance value between the rail surface of the guide rail and the crane guide rail detection robot. For example, the height from the guide rail worn part to the crane guide rail detection robot is obviously larger than a normal value, the corresponding height values of the worn parts with different degrees are different, even the small-size wear can be detected by the light curtain sensor, the technical problems that the shape characteristics of the guide rail with small-size error value cannot be detected and the flatness detection cannot be realized in the prior art are solved, the shape characteristics of the guide rail on the guide rail surface are obtained by the light curtain sensor, whether the guide rail is broken or not can be judged, and the wear degree of the guide rail surface can be quantitatively evaluated; adjusting the direction of a plumb of the prism according to the smoothness data of the guide rail, and ensuring the consistency of the test data of the total station; and a correction basis is provided for correcting errors of the test data caused by the flatness. In this embodiment, the light curtain sensor is connected with the input electric connection of data acquisition measurement module, and data acquisition measurement module's output is connected with outside PC.
And the angle sensor is used for detecting X, Y and the angle value in the Z direction when the crane guide rail detection robot runs on the guide rail. At present, the prism and the guide rail are required to be in a relatively horizontal state in the testing process, so that the prism data can be approximately used for replacing the guide rail data, in actual situations, due to rail abrasion and machining and installation errors of wheels, the crane guide rail detection robot can incline in the process of running on the rail, the prism deviates from the original position, the prism and the guide rail are not in a relatively horizontal state, and if the deviation distance of the prism is too large, the tested prism data cannot replace the guide rail data. Therefore, in order to solve the above problems, an angle sensor is provided in the present embodiment, and the angle sensor is electrically connected to the input end of the data acquisition and measurement module; the angle values in X, Y and Z directions when the crane guide rail detection robot runs on the guide rail are obtained, the running state of the current prism is known based on the angle values, and the rail surface coordinate is adjusted based on the running state, so that the prism data can approximately replace the guide rail data. In this embodiment, the operation state of the prism includes a horizontal state and an inclined state, wherein the inclination of the prism can be divided into three cases: rotation around the X-axis direction, rotation around the Y-axis direction, and rotation around the Z-axis direction. The geometrical relationship of the crane guide rail inspection robot in the yOz coordinate plane of the global coordinate system is shown in fig. 6 and 7. The height of the prism support is h1, the height of the crane guide rail detection robot is h2, 1 represents a prism position point, 2 represents a calculation point, 3 represents a track top surface central point, when the track surface is in a horizontal state, the prism is not inclined, the calculation point 2 is superposed with the track top surface central point 3, and at the moment, prism data can be approximately adopted to replace guide rail data; fig. 7 is a schematic diagram showing a prism tangential state, and the calculation point 2 does not coincide with the track top center point 3, and in this case, prism data approximation cannot be used instead of guide rail data. For example, when the angle values in the three directions of X, Y and Z are unchanged, the prism and the guide rail are in a horizontal state; if the error in the Y-axis direction and the error in the Z-axis direction are generated, judging that the prism rotates around the X-axis direction; if the error in the X-axis direction and the error in the Z-axis direction are generated, judging that the prism rotates around the Y-axis direction; because the position of the prism is at the geometric center of the crane guide rail detection robot, no error is generated when the prism rotates around the Z axis.
Because the prism and the guide rail have vertical height difference, if the crane guide rail detection robot keeps horizontal operation, the prism and the guide rail keep vertical state, the prism data can approximately replace the guide rail data, the operation state of the crane guide rail detection robot is shown in figure 6, at this time, the track surface coordinate corresponding to the detection point can be obtained by subtracting the height of the prism support from the Z-axis coordinate of the detection point, and further the vertical height difference is eliminated; if the prism rotates around the X-axis direction or rotates around the Y-axis direction in an inclined mode, the prism and the guide rail cannot be in a vertical state, the prism data cannot approximately replace the guide rail data, the running state of the crane guide rail detection robot is shown in figure 7, the vertical height difference cannot be eliminated by adopting an error eliminating method in horizontal running, and at the moment, the vertical height difference of the prism and the guide rail in a non-vertical state needs to be eliminated according to the guide rail flatness data. The specific method comprises the following steps:
if the prism tilts to rotate around the X-axis direction, the relationship between the orbit surface coordinate A1n and the prism bull's eye coordinate An is:
wherein alpha is a front-back pitch angle measured by a total station;
if the prism tilts to rotate around the Y-axis direction, the relationship between the track surface coordinate A1n and the prism bull's-eye coordinate An is:
wherein beta is a left roll angle and a right roll angle, and is measured by a total station.
And the vibration sensor is used for detecting the inherent vibration information of the crane guide rail detection robot and the vibration information caused by the unevenness of the rail surface in the running process of the crane guide rail detection robot. In this embodiment, the vibration sensor is electrically connected to the input end of the data acquisition and measurement module.
The working principle of the embodiment is as follows: the crane guide rail detection robot carries an automatic tracking prism to move along a rail, the total station tracks the position of the prism in real time, meanwhile, a light curtain sensor, an angle sensor and a vibration sensor which are integrated in the crane guide rail detection robot sample according to the same sampling frequency as the total station, the light curtain sensor detects the height of a guide rail surface from the crane guide rail detection robot, even small-sized abrasion can be detected by the light curtain sensor, the height value is transmitted to a data acquisition and measurement module after the detection of the light curtain sensor is finished, the data acquisition and measurement module performs signal conditioning and then transmits the signal to an external PC (personal computer), and the external PC acquires the abrasion degree and the flatness of the current rail surface according to the height value; the angle sensor detects X, Y and Z angle values of the crane guide rail detection robot when running on the guide rail, and transmits the detected angle values to the external PC, the external PC learns the running state of the current crane guide rail detection robot based on the angle values, and the vertical height difference under the non-vertical state is eliminated by adopting a corresponding error elimination method based on the running state; the vibration sensor detects the inherent vibration information of the crane guide rail detection robot and the vibration information caused by uneven rail surface in the running process of the crane guide rail detection robot, and transmits the vibration information to an external PC.
The beneficial effect of this embodiment does: the appearance characteristics of the guide rail on the rail surface of the guide rail are obtained by arranging the light curtain sensor, so that whether the guide rail is broken or not can be judged, and the abrasion degree of the rail surface of the guide rail is quantitatively evaluated; adjusting the direction of a plumb of the prism according to the smoothness data of the guide rail, and ensuring the consistency of the test data of the total station; providing a correction basis for correcting errors of the test data caused by the flatness;
the angle sensor is arranged and used for detecting angle values in X, Y and Z directions when the crane guide rail detection robot runs on the guide rail, the running state of the current crane guide rail detection robot is known based on the angle values, and the vertical height difference in a non-vertical state can be eliminated based on the running state by adopting a corresponding error elimination method;
through setting up vibration sensor for detect crane guide rail inspection robot self natural vibration information and the vibration information that causes because of the rail surface unevenness in its operation process, provide the correction basis for revising test data because of vibrating and leading to the error.
Example 2
In the embodiment 1, in the testing process, the relative position of the target center of the prism and the center line of the top surface of the track is required to be kept unchanged, so that the motion track of the target center can be used for replacing the outline of the center line of the top surface of the track, but the prism is inclined, so that the actually calculated track surface coordinate point is not coincident with the center line of the top surface of the track. However, in practical applications, due to the fact that the guide rail is not flat, the natural vibration frequency of the crane guide rail detection robot and the angular offset of the crane guide rail detection robot during operation all cause a large difference between the coordinates of the target center detected by the total station and actual coordinate values, if errors caused by flatness of the guide rail, the self vibration frequency and the angular offset of test data are not corrected, the acquired coordinates of the rail surface have a large error, and the rail wear assessment, the coplanarity detection, the levelness detection and the rail distance detection of the rail are all analyzed based on the coordinates of the rail surface coordinates, if the coordinates of the rail surface coordinates cannot be close to the actual coordinate values, the rail wear degree of the guide rail cannot be quantitatively assessed, the coordinates of the rail surface of the guide rail a and the rail surface of the guide rail B cannot be accurately acquired, and the coplanarity and the actual values obtained by reverse modeling calculation have large errors, it cannot be applied in engineering. Therefore, in order to solve the above problem, in this embodiment, a crane rail detection method includes performing error correction on total station test data from a track profile, vibration information of a crane rail detection robot, and an angle deviation during operation to obtain a relatively true rail plane coordinate, and calculating coplanarity and levelness based on reverse modeling of the relatively true rail plane coordinate. The method specifically comprises the following steps:
s1, the total station is located at any position on the ground to build a station, the guide rail detection robot in embodiment 1 is adopted to carry a prism to advance along a track A at a constant speed, a measuring head of the total station is set as a coordinate origin, a global coordinate system is built, the bull 'S-eye coordinates An of the prism at n detection points are detected in sequence, the motion parameters of the guide rail detection robot at the n detection points are detected synchronously, the bull' S-eye coordinates An of the abnormal prism are corrected based on the motion parameters, and track surface coordinates A1n are obtained; the motion parameters comprise angle information, track profile information, vibration information and displacement information of the guide rail detection robot at the detection point;
the coordinate origin of the global coordinate system is a measuring head of the total station, the Z axis of the global coordinate system is in the vertical direction, the xOy coordinate plane is parallel to the horizontal plane, the Y axis is perpendicular to the vertical plane, the vertical plane comprises a least square straight line of the track A, and the X axis can be determined according to the right-hand rule.
When the target coordinates An of the prisms at the n detection points are detected, the prisms are inclined due to reasons such as uneven guide rails, inherent vibration frequency of the crane guide rail detection robot, angular deviation of the crane guide rail detection robot during operation and the like, so that abnormal coordinate values exist in the target coordinates An detected by the total station, and if the abnormal coordinate values are not removed or corrected in advance, a large error exists in An analysis result due to the existence of the abnormal coordinate values during data analysis. In order to modify the abnormal coordinate values, in the present embodiment, the track profile at that time, the angle information and the vibration information of the rail inspection robot are detected at the detection point, and the abnormal prism target coordinates An are corrected based on the detection data. The specific method comprises the following steps:
s101, acquiring vibration frequency and vibration amplitude at n detection points based on a vibration sensor, and correcting abnormal prism coordinate data in the Z-axis direction in n prism target coordinates An based on the vibration frequency and the vibration amplitude;
in this step, because crane guide rail inspection robot has self natural frequency and the rail surface unevenness leads to crane guide rail inspection robot all to have vibration component on X axle, Y axle and Z axle, and this vibration component leads to there being great error between prism bull's eye coordinate An and the actual coordinate value, consequently, in order to eliminate crane guide rail inspection robot self natural frequency to the influence of prism bull's eye coordinate An, the concrete implementation process of this step is as follows:
s201, acquiring n prism target center coordinates An through a total station, and performing db4 packet decomposition on the n prism target center coordinates An to obtain n X, Y frequency items in the Z direction;
s202, acquiring vibration information of n detection points based on a vibration sensor, performing Fourier transform on the n vibration information, and acquiring running natural frequencies of the crane guide rail detection robot in X, Y and Z directions;
s203, obtaining X, Y and Z direction similar frequency items in the decomposition of db4 small packets based on the inherent frequency of the crane guide rail detection robot running in X, Y and Z directions, and replacing the frequency values in the corresponding directions of m adjacent detection points with the frequency value mean values in the corresponding directions of the detection points to obtain reconstructed target coordinates An; wherein m is less than n;
the purpose of steps S201-S203 is to remove the interference of the natural vibration frequency of the crane guide rail detection robot to the test. Preferably, the average value of the frequency values in the corresponding direction at 2 adjacent detection points can be used to replace the frequency value in the corresponding direction at the detection point;
s204, sequencing the amplitudes in the Z-axis direction in the reconstructed target coordinates An according to the magnitude, setting a threshold value, setting a detection point exceeding the amplitude threshold value as An abnormal vibration point, and replacing the coordinate value of the abnormal vibration point on the Z axis with the coordinate mean value of p adjacent points of the abnormal vibration point on the Z axis; wherein p is less than n.
Since the crane guide rail detection robot shakes most in the Z-axis direction when the rail is broken or has a large degree of wear, the purpose of step S204 is to eliminate an error in the Z-axis direction of the crane guide rail detection robot due to the rail being broken or having a large degree of wear. Preferably, the coordinate value of the abnormal vibration point on the Z axis may be replaced by the mean value of the coordinates of 2 adjacent points of the abnormal vibration point on the Z axis.
S102, acquiring angle information of n detection points based on An angle sensor, and correcting abnormal prism coordinate data in the Y-axis direction in n prism target coordinates An based on the angle information;
in the plane of the xOy coordinate, the error caused by the prism shift mainly appears on the Y axis, so the purpose of this step is mainly to eliminate the measurement error caused by the prism shift or loss on the Y axis, and the specific implementation process is as follows:
s301, acquiring angle information of the crane guide rail detection robot at n detection points in X, Y and Z-axis directions based on an angle sensor;
s302, setting An angle threshold value in the Y-axis direction, setting a detection point of which the Y-axis numerical value exceeds the angle threshold value in n bull' S-eye coordinates An as An abnormal angle point, and replacing the coordinate value of the abnormal angle point on the Y-axis with the coordinate mean value of q adjacent points of the abnormal angle point on the Y-axis; wherein q is less than n.
Preferably, the coordinate value of the abnormal angle point on the Y axis may be replaced by the mean value of the coordinates of 2 adjacent points of the abnormal angle point on the Y axis.
S103, acquiring the height difference between the rail surface at the n detection points and the crane guide rail detection robot based on the light curtain sensor, and correcting abnormal prism coordinate data in the X-axis direction in the prism target center coordinates An based on the height difference;
the method mainly aims to avoid measurement errors caused by the fact that the crane guide rail detection robot and the rail surface are not flat, and the errors are mainly caused by the fact that the rail surface is rusted or pits exist. The specific implementation process is as follows:
s401, height differences between the rail surfaces of the n detection points and the crane guide rail detection robot are obtained based on the light curtain sensor;
s402, setting a height difference threshold, setting a detection point exceeding the height difference threshold as an abnormal contour point, and replacing a coordinate value of the abnormal contour point on an X axis with a coordinate mean value of h adjacent points of the abnormal contour point on the X axis; wherein h is less than n.
Preferably, the coordinate value of the abnormal contour point on the X axis may be replaced by the mean value of the coordinates of 2 adjacent points of the abnormal contour point on the X axis.
S104, acquiring n corrected prism target coordinates An, correcting the prism target coordinates An based on An angle correction formula, and acquiring track surface coordinates A1 n.
The angle correction formula is a relational expression between the track surface coordinate A1n and the prism target coordinate An when the prism tilts around the X-axis direction or rotates around the Y-axis in embodiment 1, and will not be described herein again.
S2, after the track A detection is finished, moving the guide rail detection robot to an initial detection point on the track B, acquiring track surface coordinates B1n at n detection points by adopting the same principle, and integrating n track surface coordinates B1n on the track B into a global coordinate system;
s3, fitting the n track surface coordinates A1n and the n track surface coordinates B1n by adopting a least square method to obtain a track A datum line and a track B datum line; measuring the straightness of the track A and the track B by adopting a chord measuring method;
the track a reference line and the track B reference line are shown in fig. 8, and the linearity of the track is analyzed by comparing the distance between each measuring point position and the track reference line, and the error of the linearity can be calculated by a point-to-straight line distance formula when the track a reference line and the track B reference line are known.
S4, using the track surface coordinate A1n of the track A as a reference, adopting an interpolation algorithm to find the track surface coordinate B' n corresponding to the track surface coordinate A1n one by one, and calculating the track space and the coplanarity of the track A and the track B based on the two one-to-one corresponding detection point positions.
The beneficial effect of this embodiment does: correcting the coordinate An of the target center of the abnormal prism based on the vibration information at the detection point, and removing the abnormal value of the coordinate An of the target center of the prism on the coordinate axis caused by the factors such as the inherent vibration frequency of the crane guide rail detection robot, the rail surface fracture and the like;
correcting the coordinates An of the target center of the abnormal prism based on the angle information at the detection point, and removing the measurement error caused by the deviation or loss of the prism on the Y axis;
and correcting the coordinates An of the target center of the abnormal prism based on the track profile information at the detection point, so that the measurement error caused by the corrosion or pot holes on the rail surface of the crane guide rail detection robot is avoided.
Example 3
The existing detection method comprises the following steps: after the total station completes the station building, the two track detection trolleys are respectively positioned on the tracks, the total station can simultaneously detect the reflection targets carried by the two track detection trolleys, the scheme can ensure that the detection points on the two tracks are in one-to-one correspondence in position and time, and when the track gauge and the coplanarity are calculated, the problem that the two points currently participating in calculation are not in the same point position does not exist. Therefore, in order to solve the above problem, in this embodiment, the prism bull's-eye coordinate An and the orbit surface coordinate A1n after being corrected are obtained in embodiment 2, the orbit surface coordinate B' 1n located at the same position as the orbit surface coordinate A1n is obtained by using the lagrange difference algorithm with the orbit surface coordinate A1n of the orbit a as a reference, and the coplanarity and the track gauge are calculated based on the two detected point coordinates located at the same position.
The Lagrange difference algorithm comprises the following specific steps:
s501, obtaining coordinates of initial detection points A11 and B11 of the track A and the track B, and matching the position relation of the initial points;
s502, matching and detecting a mapping relation F (x) between the displacement distance and the coordinate;
s503, with n track surface coordinates A1n points of the track A as base points, searching the track surface coordinates B' 1n of the track B corresponding to the position of the track A one by one in space based on the mapping relation F (x), and calculating the space coordinate values of the coordinate points.
Through steps S501-S503, a track surface coordinate B' 1n of the same point as the track surface coordinate A1n can be obtained, and a distance between two same points can be calculated based on a spatial distance formula, where the distance is a distance between the track a and the track B. The conventional method for calculating the track gauge is a method for calculating the distance between a track A datum line and a track B datum line by adopting two linear distance formulas to replace the track gauge, and the track A datum line and the track B datum line are obtained by fitting n detection points, so that an error exists in the obtained datum line, and if the distance between the two datum lines is used for replacing the track gauge between the track A and the track B, the error is further increased, so that the calculated track gauge has a larger error with the actual track gauge, and the method cannot be applied to engineering. In order to solve the above problem, in this embodiment, the distance between two same-point locations is used to replace the track gauge, and compared with the conventional method, the calculation is performed based on two real coordinate points, and the real coordinate points are further corrected through the steps of embodiment 2, so that the error value is smaller when the distance between two same-point location real coordinate points is used to replace the track gauge.
Calculating the coplanarity of the track A and the track B based on the two one-to-one corresponding detection point positions, comprising the following steps:
s301, solving a reference plane based on the track A reference line and the track B reference line;
s302, with the n bull ' S-eye coordinates An of the track A as base points, respectively calculating distances from the n bull ' S-eye coordinates An and the coordinate point B ' n to the reference plane based on a point-to-plane distance formula.
The beneficial effect of this embodiment does: the method comprises the steps of taking a track surface coordinate A1n of a track A as a reference, obtaining a track surface coordinate B' 1n which is in the same position as the track surface coordinate A1n by adopting a Lagrange difference algorithm, making up for missing data, solving the problem that data detected on the track A and data detected on the track B are not in one-to-one correspondence in the prior art when track distance and coplanarity are calculated, and ensuring the accuracy of the coplanarity and the track distance;
compared with the method for calculating the distance between the track A datum line and the track B datum line by adopting two linear distance formulas to replace the track gauge in the prior art, the method for acquiring the track gauge by adopting the space distance formula between the two points has smaller error and can acquire more accurate track gauge.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. The utility model provides a hoist guide rail detecting system, its includes total powerstation, guide rail detection robot and outside PC, its characterized in that: the guide rail detection robot comprises a data acquisition and measurement module, a prism, a light curtain sensor, an angle sensor and a vibration sensor;
the center of the prism is positioned at the geometric center of the frame of the guide rail detection robot; the light curtain sensor, the angle sensor and the vibration sensor are respectively integrated in the body of the guide rail detection robot; the light curtain sensor is installed right opposite to the rail surface of the guide rail and used for detecting the height of the rail surface of the guide rail from the crane guide rail detection robot; the light curtain sensor, the angle sensor and the vibration sensor are respectively and electrically connected with the input end of the data acquisition and measurement module, and the output end of the data acquisition and measurement module is connected with an external PC;
the guide rail detection robot runs at a constant speed along a detected track, the total station is located at any position to build a station, and the total station detects a central fixed point of the prism and obtains coordinate data of the point, converts the coordinate data of the point into a global coordinate system, and transmits the coordinate data to an external PC.
2. A crane rail detection system as claimed in claim 1, wherein: the guide rail inspection robot further includes: a displacement sensor and a speed sensor;
and the displacement sensor and the speed sensor are respectively integrated in the body of the guide rail detection robot and are respectively electrically connected with the input end of the data acquisition and measurement module.
3. A crane guide rail detection method is characterized in that: the method comprises the following steps:
s1, the total station is located at any position on the ground, the guide rail detection robot of claim 1 carries a prism to advance along a track A at a constant speed, a measuring head of the total station is set as a coordinate origin, a global coordinate system is established, the bull 'S-eye coordinates An of the prism at n detection points are detected in sequence, the motion parameters of the guide rail detection robot at the n detection points are detected synchronously, the bull' S-eye coordinates An of the abnormal prism are corrected based on the motion parameters, and track surface coordinates A1n are obtained; the motion parameters comprise angle information, contour information, vibration information and displacement information of the guide rail detection robot at the detection point;
s2, after the track A detection is finished, moving the guide rail detection robot to an initial detection point on the track B, acquiring track surface coordinates B1n at n detection points by adopting the same principle, and integrating n track surface coordinates B1n on the track B into a global coordinate system;
s3, fitting the n track surface coordinates A1n and the n track surface coordinates B1n by adopting a least square method to obtain a track A datum line and a track B datum line; measuring the straightness of the track A and the track B by adopting a chord measuring method;
s4, using the track surface coordinate A1n of the track A as a reference, adopting an interpolation algorithm to find the track surface coordinate B' n corresponding to the track surface coordinate A1n one by one, and calculating the track space and the coplanarity of the track A and the track B based on the two one-to-one corresponding detection point positions.
4. A crane guide rail detection method as claimed in claim 3, wherein: in the step S1, the "correcting the coordinates An of the anomalous prism bulls-eye based on the motion parameters to obtain the track surface coordinates A1 n" specifically includes the following steps:
s101, acquiring vibration frequency and vibration amplitude at n detection points based on a vibration sensor, and correcting abnormal prism coordinate data in the Z-axis direction in n prism target coordinates An based on the vibration frequency and the vibration amplitude;
s102, acquiring angle information of n detection points based on An angle sensor, and correcting abnormal prism coordinate data in the Y-axis direction in n prism target coordinates An based on the angle information;
s103, acquiring the height difference between the rail surface at the n detection points and the crane guide rail detection robot based on the light curtain sensor, and correcting abnormal prism coordinate data in the X-axis direction in the prism target center coordinates An based on the height difference;
s104, acquiring n corrected prism target coordinates An, correcting the prism target coordinates An based on An angle correction formula, and acquiring track surface coordinates A1 n.
5. The crane guide rail detection method according to claim 4, wherein: the S101 specifically includes the following steps:
s201, acquiring n prism target center coordinates An through a total station, and performing db4 packet decomposition on the n prism target center coordinates An to obtain n X, Y frequency items in the Z direction;
s202, acquiring vibration information of n detection points based on a vibration sensor, performing Fourier transform on the n vibration information, and acquiring running natural frequencies of the crane guide rail detection robot in X, Y and Z directions;
s203, obtaining X, Y and Z direction similar frequency items in the decomposition of db4 small packets based on the inherent frequency of the crane guide rail detection robot running in X, Y and Z directions, and replacing the frequency values in the corresponding directions of m adjacent detection points with the frequency value mean values in the corresponding directions of the detection points to obtain reconstructed target coordinates An; wherein m is less than n;
s204, sequencing the amplitudes in the Z-axis direction in the reconstructed target coordinates An according to the magnitude, setting a threshold value, setting a detection point exceeding the amplitude threshold value as An abnormal vibration point, and replacing the coordinate value of the abnormal vibration point on the Z axis with the coordinate mean value of p adjacent points of the abnormal vibration point on the Z axis; wherein p is less than n.
6. The crane guide rail detection method according to claim 4, wherein: the S102 specifically includes the following steps:
s301, acquiring angle information of the crane guide rail detection robot at n detection points in X, Y and Z-axis directions based on an angle sensor;
s302, setting An angle threshold value in the Y-axis direction, setting a detection point of which the Y-axis numerical value exceeds the angle threshold value in n bull' S-eye coordinates An as An abnormal angle point, and replacing the coordinate value of the abnormal angle point on the Y-axis with the coordinate mean value of q adjacent points of the abnormal angle point on the Y-axis; wherein q is less than n.
7. The crane guide rail detection method according to claim 4, wherein: the step 103 specifically comprises the following steps:
s401, height differences between the rail surfaces of the n detection points and the crane guide rail detection robot are obtained based on the light curtain sensor;
s402, setting a height difference threshold, setting a detection point exceeding the height difference threshold as an abnormal contour point, and replacing a coordinate value of the abnormal contour point on an X axis with a coordinate mean value of h adjacent points of the abnormal contour point on the X axis; wherein h is less than n.
8. The crane guide rail detection method according to claim 4, wherein: the "angle correction formula" in S104 is:
if the prism is in the horizontal state, the relationship between the track surface coordinate A1n and the prism bull's eye coordinate An is as follows:
wherein (x)i,yi,zi) The coordinate An of the target center of the prism at the ith detection point on the track A; (x)1i,y1i,z1i) The coordinate of the rail surface of the ith detection point on the rail surface is obtained; h is1Is the prism support height; h is2Is the height of the vehicle body;
if the prism is in a state of rotating around the X-axis direction, the relationship between the track surface coordinate A1n and the prism bull's-eye coordinate An is as follows:
wherein alpha is a front-back pitch angle measured by a total station;
if the prism is in a rotating state around the Y-axis direction, the relationship between the track surface coordinate A1n and the prism bull's-eye coordinate An is as follows:
wherein beta is a left roll angle and a right roll angle, and is measured by a total station.
9. A crane guide rail detection method as claimed in claim 3, wherein: in the step S4, with the track surface coordinate A1n of the track a as a reference, a median interpolation algorithm is used to find track surface coordinates B' 1n corresponding to the track surface coordinates A1n one by one, and the method includes the following steps:
s501, obtaining coordinates of initial detection points A11 and B11 of the track A and the track B, and matching the position relation of the initial points;
s502, matching and detecting a mapping relation F (x) between the displacement distance and the coordinate;
s503, with n track surface coordinates A1n points of the track A as base points, searching the track surface coordinates B' 1n of the track B corresponding to the position of the track A one by one in space based on the mapping relation F (x), and calculating the space coordinate values of the coordinate points.
10. A crane rail detection method according to claim 3 or 9, characterized in that: in S4, calculating the coplanarity of the track a and the track B based on the two one-to-one corresponding detection point positions, includes the following steps:
s601, solving a reference plane based on the track A reference line and the track B reference line;
s602, with the n bull ' S-eye coordinates An of the track A as base points, respectively calculating the distances from the n bull ' S-eye coordinates An and the coordinate point B ' n to the reference plane based on a point-to-plane distance formula.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115824138A (en) * | 2023-02-24 | 2023-03-21 | 江苏宣胜金属科技有限公司 | Cold rolling seamless steel pipe straightness accuracy detection device |
| CN116446227A (en) * | 2023-06-19 | 2023-07-18 | 中国铁建高新装备股份有限公司 | String measurement equipment, track line restoration method, device, equipment and system |
| CN117781946A (en) * | 2023-12-15 | 2024-03-29 | 武汉万曦智能科技有限公司 | Guide rail straightness detection robot and detection method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105783836A (en) * | 2016-04-06 | 2016-07-20 | 天奇自动化工程股份有限公司 | Track wear automatic detection vehicle |
| CN106018557A (en) * | 2016-07-28 | 2016-10-12 | 南京理工大学 | Track surface indentation detection method based on wavelet analysis |
| CN215114395U (en) * | 2021-05-24 | 2021-12-10 | 武汉万曦智能科技有限公司 | Crane guide rail detection system |
-
2021
- 2021-05-24 CN CN202110565159.8A patent/CN113324476A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105783836A (en) * | 2016-04-06 | 2016-07-20 | 天奇自动化工程股份有限公司 | Track wear automatic detection vehicle |
| CN106018557A (en) * | 2016-07-28 | 2016-10-12 | 南京理工大学 | Track surface indentation detection method based on wavelet analysis |
| CN215114395U (en) * | 2021-05-24 | 2021-12-10 | 武汉万曦智能科技有限公司 | Crane guide rail detection system |
Non-Patent Citations (2)
| Title |
|---|
| 刘伟: "起重机轨道自动检测系统研发", 中国优秀硕士学位论文全文数据库（电子期刊）工程科技Ⅱ辑, no. 12, 15 December 2015 (2015-12-15), pages 029 - 84 * |
| 孔庆彬;程维明;: "一种新的起重机轨道检测方法和装置", 自动化仪表, no. 04, 20 April 2015 (2015-04-20) * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115824138A (en) * | 2023-02-24 | 2023-03-21 | 江苏宣胜金属科技有限公司 | Cold rolling seamless steel pipe straightness accuracy detection device |
| CN116446227A (en) * | 2023-06-19 | 2023-07-18 | 中国铁建高新装备股份有限公司 | String measurement equipment, track line restoration method, device, equipment and system |
| CN116446227B (en) * | 2023-06-19 | 2023-08-25 | 中国铁建高新装备股份有限公司 | String measurement equipment, track line restoration method, device, equipment and system |
| CN117781946A (en) * | 2023-12-15 | 2024-03-29 | 武汉万曦智能科技有限公司 | Guide rail straightness detection robot and detection method |
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