CN109084697B - Method and structure for detecting outline of induction plate - Google Patents
Method and structure for detecting outline of induction plate Download PDFInfo
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- CN109084697B CN109084697B CN201810721823.1A CN201810721823A CN109084697B CN 109084697 B CN109084697 B CN 109084697B CN 201810721823 A CN201810721823 A CN 201810721823A CN 109084697 B CN109084697 B CN 109084697B
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- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
- G01B11/167—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge by projecting a pattern on the object
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Abstract
The invention discloses a method for detecting the profile of an induction plate, which comprises a profile matching method, a profile curve segmentation method and a profile deformation evaluation method. The sensing plate profile detection structure comprises a sensing plate, wherein the sensing plate comprises a left sensing plate and a right sensing plate which are respectively arranged on a left rail and a right rail; two 2D laser measurement assemblies are arranged above the left induction plate and the right induction plate in parallel; the 2D laser sensor respectively projects laser light bars on the left induction plate and the right induction plate; the section profile of the induction plate is detected through the 2D laser sensor, the detected profile is compared with the standard design profile of the induction plate, the profile change condition is obtained, and the deformation of the induction plate is evaluated accordingly. The invention matches the detection profile with the standard profile, and evaluates the profile deformation according to the result after matching to obtain the profile deformation index of the induction plate.
Description
Technical Field
The invention relates to a method and a structure for detecting the profile of an induction plate.
Background
The induction plate is used as one of the cores of the traction system of the medium-low speed maglev train, and the deformation of the structure, the interference of foreign matters on the surface and the like of the induction plate all affect the safe operation of the medium-low speed maglev train. At present, adopt the manual work to patrol induction plate surface condition, inefficiency, work load are big, simultaneously, because the magnetic levitation circuit mostly erects the circuit in the high altitude, daytime for satisfying the operation needs, detects and can only go on at night, and artifical the measuring will have very big potential safety hazard, is difficult to satisfy daily contact track and detects the requirement of maintaining.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides the detection method and the structure of the profile of the induction plate, which are convenient to construct and high in precision.
The purpose of the invention is realized by the following technical scheme: the method for detecting the profile of the induction plate comprises a profile curve segmentation method, a profile matching method and a profile deformation evaluation method;
wherein the profile curve is divided:
in order to realize high-precision measurement of the profile of the induction plate, the F-shaped profile part and the fastening screw part in the detection profile are automatically divided, and the dividing points between the F-shaped profile part and the fastening screw part are accurately positioned; in the process of vehicle body movement, the generated random vibration does not influence the geometric distribution relation among the sensing plate profile, the F-shaped track profile and the fastening screw profile in the detection profile, so that the profile curve is divided by utilizing the stable geometric position distribution relation;
STEP1 traverses the whole contour line segment, a broken part is found, the short part is used as an F-shaped contour part, and the remaining point cloud AH is the induction plate contour part;
STEP2 obtains segment segments divided according to the proportion of AB/AH, AC/AH, AD/AH, AE/AF, AF/AH and AG/AH for AH point cloud segment;
STEP3, for the divided result, BC, DE, FG are used as the part for fastening the screw, and the rest is the sensing plate profile point cloud data P;
the profile matching method comprises the following steps:
the magnetic suspension track detection vehicle body is a multi-degree-of-freedom vibration system with a spring suspension device, and a 2D laser sensor arranged below the vehicle body can generate multi-degree-of-freedom random vibration along with the vehicle body, so that the profile of a detected induction plate cannot be directly naturally superposed with a standard profile; in order to realize the high-precision deformation condition evaluation of the profile of the induction plate, the detection profile needs to be matched with the designed standard profile of the induction plate with high precision; for the profile point cloud, in order to ensure that the angle and position relation between the point and the point at the matching time does not change, a rotational translation matrix of a rigid body needs to be solved;
aiming at the detected profile point cloud data P, a corresponding point set Q is directly found on the standard profile data, so that rigid rotation R and translation T transformation of the profile point cloud are converted into a problem of solving the least square optimal solution of the point cloud data P to Q;
the problem is converted to the following computational description:
wherein n is the number of profile point clouds, WiFor weighting point cloud data, here take Wi＝1；
STEP11 calculates the center point of the point cloud data P, Q, as formula (2);
STEP12 performs center reduction processing on the original data to obtain new point cloud data as formula (3);
STEP13 calculates d × d dimensional covariance matrix S as formula (4);
S＝XWYT(4)
x, Y are d × n dimensional matrices, where d is 1 and W is diag (W)1,w2,w3,...,wn,)；
STEP14 is calculated by STEP 13S to obtain SVD decomposition, i.e., SVD (S) can obtain S ═ U Σ VTNamely, obtaining U and V by solution; decomposing the obtained value by svd to obtain a rotation matrix R, and calculating according to the method (5);
STEP15 can be calculated according to STEP14 to obtain T as formula (6);
STEP15 rotates and translates the profile according to the formula (7) according to the R and T matrixes obtained in the STEP, and a final matching result is obtained;
the profile deformation evaluation method comprises the following steps:
calculating the profile deformation condition by using the result of the steel rail profile matching data;
STEP111 sets profile matching threshold WmaxTraversing the outline point cloud data and traversing each point cloud data piPoint cloud data q corresponding theretoiThe distance is calculated according to the formula (8), and the judgment is completed to obtain N corresponding to each pointiThe value:
STEP112 calculates a profile deformation index D:
the sensing plate profile detection structure comprises a left sensing plate and a right sensing plate which are respectively arranged on a left rail and a right rail; two 2D laser measurement assemblies are arranged above the left induction plate and the right induction plate in parallel;
the 2D laser sensor respectively projects laser light bars on the left induction plate and the right induction plate;
the section profile of the induction plate is detected through the 2D laser sensor, the detected profile is compared with the standard design profile of the induction plate, the profile change condition is obtained, and the deformation of the induction plate is evaluated accordingly.
Preferably, the track is an F-track.
Preferably, the track is a magnetic levitation track.
Preferably, in order to compare the detected sensing plate profile with the standard profile to obtain the sensing plate profile deformation index, the detected profile is matched with the standard profile, and the profile deformation is evaluated based on the result of matching.
The invention has the beneficial effects that: the invention matches the detection profile with the standard profile, and evaluates the profile deformation according to the result after matching to obtain the profile deformation index of the induction plate.
Drawings
FIG. 1 is a profile of a sensing plate profile sensor;
FIG. 2 is a schematic diagram of sensing plate profile detection;
FIG. 3 is a view of an original profile;
FIG. 4 is a graph of matching effect;
FIG. 5 is an original profile disturbed by a fastening screw;
FIG. 6 is a diagram of the effect of matching the induction plate;
FIG. 7 is a profile segmentation method;
fig. 8 shows the result of the profile segmentation.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, the method for detecting the profile of the induction plate comprises several steps of a profile curve segmentation method, a profile matching method and a profile deformation evaluation method;
profile curve segmentation:
in order to realize high-precision measurement of the profile of the induction plate, the F-shaped profile part and the fastening screw part in the detection profile are automatically divided, and the dividing points between the F-shaped profile part and the fastening screw part are accurately positioned; in the process of vehicle body movement, the generated random vibration does not influence the geometric distribution relation among the sensing plate profile, the F-shaped track profile and the fastening screw profile in the detection profile, so that the profile curve is divided by utilizing the stable geometric position distribution relation; the outline point cloud segmentation principle is shown in fig. 7 and 8, and the specific segmentation steps are as follows:
STEP1 traverses the whole contour line segment, a broken part is found, the short part is used as an F-shaped contour part, and the remaining point cloud AH is the induction plate contour part;
STEP2 obtains segment segments divided according to the proportion of AB/AH, AC/AH, AD/AH, AE/AF, AF/AH and AG/AH for AH point cloud segment;
STEP3, for the divided result, BC, DE, FG are used as the part for fastening the screw, and the rest is the sensing plate profile point cloud data P;
the profile matching method comprises the following steps:
the magnetic suspension track detection vehicle body is a multi-degree-of-freedom vibration system with a spring suspension device, and a 2D laser sensor arranged below the vehicle body can generate multi-degree-of-freedom random vibration along with the vehicle body, so that the profile of a detected induction plate cannot be directly naturally superposed with a standard profile; in order to realize the high-precision deformation condition evaluation of the profile of the induction plate, the detection profile needs to be matched with the designed standard profile of the induction plate with high precision; for the profile point cloud, in order to ensure that the angle and position relation between the point and the point at the matching time does not change, a rotational translation matrix of a rigid body needs to be solved;
aiming at the detected profile point cloud data P, a corresponding point set Q is directly found on the standard profile data, so that rigid rotation R and translation T transformation of the profile point cloud are converted into a problem of solving the least square optimal solution of the point cloud data P to Q;
the induction plate profile matching principle is shown in fig. 3, 4, 5 and 6; the sensing plate profile is provided with a fastening screw as shown in fig. 2, and meanwhile, the detection profile also comprises a partial F-track profile which cannot be used as a profile matching reference;
the problem is converted to the following computational description:
wherein n is the number of profile point clouds, WiFor weighting point cloud data, here take Wi＝1；
STEP11 calculates the center point of the point cloud data P, Q, as formula (2);
STEP12 performs center reduction processing on the original data to obtain new point cloud data as formula (3);
STEP13 calculates d × d dimensional covariance matrix S as formula (4);
S＝XWYT(4)
x, Y are d × n dimensional matrices, where d is 1 and W is diag (W)1,w2,w3,...,wn,)；
STEP14 is calculated by STEP 13S to obtain SVD decomposition, i.e., SVD (S) can obtain S ═ U Σ VTNamely, obtaining U and V by solution; using svd the decomposed values to obtain a rotation matrix R, the calculation method is as follows(5)；
STEP15 can be calculated according to STEP14 to obtain T as formula (6);
STEP15 rotates and translates the profile according to the formula (7) according to the R and T matrixes obtained in the STEP, and a final matching result is obtained;
the profile deformation evaluation method comprises the following steps:
calculating the profile deformation condition by using the result of the steel rail profile matching data;
STEP111 sets profile matching threshold WmaxTraversing the outline point cloud data and traversing each point cloud data piPoint cloud data q corresponding theretoiThe distance is calculated according to the formula (8), and the judgment is completed to obtain N corresponding to each pointiThe value:
STEP112 calculates a profile deformation index D:
as shown in fig. 1, the sensing plate profile detection structure includes left and right sensing plates respectively mounted on left and right rails; two 2D laser measurement assemblies are arranged above the left induction plate and the right induction plate in parallel;
the 2D laser sensor respectively projects laser light bars on the left induction plate and the right induction plate;
the section profile of the induction plate is detected through the 2D laser sensor, the detected profile is compared with the standard design profile of the induction plate, the profile change condition is obtained, and the deformation of the induction plate is evaluated accordingly.
In a preferred embodiment, the track is an F-track.
In a preferred embodiment, the track is a magnetic levitation track.
In a preferred embodiment, in order to compare the detected sensing plate profile with the standard profile to obtain the sensing plate profile deformation index, the detected profile is matched with the standard profile, and the profile deformation is evaluated according to the matching result.
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, it should be noted that any modifications, equivalents and improvements made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. The method for detecting the profile of the induction plate is characterized by comprising the following steps: the method comprises three steps of a profile curve segmentation method, a profile matching method and a profile deformation evaluation method;
the method for segmenting the profile curve comprises the following steps:
in order to realize high-precision measurement of the profile of the induction plate, the F-shaped profile part and the fastening screw part in the detection profile are automatically divided, and the dividing points between the F-shaped profile part and the fastening screw part are accurately positioned; in the process of vehicle body movement, the generated random vibration does not influence the geometric distribution relation among the sensing plate profile, the F-shaped track profile and the fastening screw profile in the detection profile, so that the profile curve is divided by utilizing the stable geometric position distribution relation;
STEP1 traverses the whole contour line segment, a broken part is found, the short part is used as an F-shaped contour part, and the remaining point cloud AH is the induction plate contour part;
STEP2 obtains segment segments divided according to the proportion of AB/AH, AC/AH, AD/AH, AE/AF, AF/AH and AG/AH for AH point cloud segment;
STEP3, for the divided result, BC, DE, FG are used as the part for fastening the screw, and the rest is the sensing plate profile point cloud data P;
the profile matching method comprises the following steps:
the magnetic suspension track detection vehicle body is a multi-degree-of-freedom vibration system with a spring suspension device, and a 2D laser sensor arranged below the vehicle body can generate multi-degree-of-freedom random vibration along with the vehicle body, so that the profile of a detected induction plate cannot be directly naturally superposed with a standard profile; in order to realize the high-precision deformation condition evaluation of the profile of the induction plate, the detection profile needs to be matched with the designed standard profile of the induction plate with high precision; for the profile point cloud, in order to ensure that the angle and position relation between the point and the point at the matching time does not change, a rotational translation matrix of a rigid body needs to be solved;
aiming at the detected profile point cloud data P, a corresponding point set Q is directly found on the standard profile data, so that rigid rotation R and translation T transformation of the profile point cloud are converted into a problem of solving the least square optimal solution of the point cloud data P to Q;
the problem is converted to the following computational description:
wherein n is the number of profile point clouds, wiFor weighting point cloud data, take wi＝1；
STEP11 calculates the center point of the point cloud data P, Q, as formula (2);
STEP12 performs center reduction processing on the original data to obtain new point cloud data as formula (3);
STEP13 calculates d × d dimensional covariance matrix S as formula (4);
S＝XWYT(4)
x, Y are d × n dimensional matrices, where d is 1,w is a diagonal matrix, and the diagonal elements are WiI.e. W ═ diag (W)1,w2,w3,...,wn)；
STEP14 is calculated by STEP 13S to obtain SVD decomposition, i.e., SVD (S) can obtain S ═ U Σ VTNamely, obtaining U and V by solution; u and V are respectively a d x d dimensional unitary matrix and an n x n dimensional unitary matrix, and sigma is a singular value matrix; obtaining a rotation matrix R by using the value obtained by SVD decomposition, wherein the calculation method is as shown in (5);
STEP15 can be calculated according to STEP14 to obtain T as formula (6);
STEP15 rotates and translates the profile according to the formula (7) according to the R and T matrixes obtained in the STEP, and a final matching result is obtained;
wherein (x)0,y0) The method comprises the steps that (1) a detection profile point cloud coordinate point is obtained for an original detection profile point cloud, and (x, y) is obtained after rotation and translation;
the profile deformation evaluation method comprises the following steps:
calculating the profile deformation condition by using the result of the steel rail profile matching data;
STEP111 sets profile matching threshold WmaxTraversing the outline point cloud data and traversing each point cloud data piPoint cloud data q corresponding theretoiThe distance is calculated according to the formula (8), and the judgment is completed to obtain N corresponding to each pointiThe value:
Wi＝dst(pi,qi)，
Wiis pi,qiThe Euclidean geometric distance between two points;
STEP112 calculates a profile deformation index D:
2. a sensing plate profile detecting structure applied to the sensing plate profile detecting method according to claim 1, characterized in that: the induction plates comprise a left induction plate and a right induction plate which are respectively arranged on the left track and the right track; two 2D laser measurement assemblies are arranged above the left induction plate and the right induction plate in parallel;
the 2D laser sensor respectively projects laser light bars on the left induction plate and the right induction plate;
the section profile of the induction plate is detected through the 2D laser sensor, the detected profile is compared with the standard design profile of the induction plate, the profile change condition is obtained, and the deformation of the induction plate is evaluated accordingly.
3. The sensing plate profile detecting structure according to claim 2, wherein: the track is an F track.
4. The sensing plate profile detecting structure according to claim 2, wherein: the track is a magnetic suspension track.
5. The sensing plate profile detecting structure according to claim 2, wherein: in order to compare the detected profile of the sensing plate with the standard profile to obtain the profile deformation index of the sensing plate, the detected profile needs to be matched with the standard profile, and the profile deformation needs to be evaluated according to the matching result.
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| CN113200076B (en) * | 2021-06-09 | 2022-12-09 | 中车株洲电力机车有限公司 | Maglev train induction plate falling monitoring method and system |
| CN113551605B (en) * | 2021-09-22 | 2024-02-27 | 湖南凌翔磁浮科技有限责任公司 | Magnetic levitation track induction plate pin relaxation detection device and detection method |
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