CN117268799B - Chassis brake detection method and system based on load torque - Google Patents
Chassis brake detection method and system based on load torque Download PDFInfo
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- CN117268799B CN117268799B CN202311570830.3A CN202311570830A CN117268799B CN 117268799 B CN117268799 B CN 117268799B CN 202311570830 A CN202311570830 A CN 202311570830A CN 117268799 B CN117268799 B CN 117268799B
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Abstract
The invention provides a chassis brake detection method and system based on load torque, and relates to the technical field of brake detection, wherein the chassis brake detection method based on load torque comprises the following steps: acquiring original point cloud data of a chassis brake; performing simulation tests on the chassis brake under different load torques, and acquiring test point cloud data of the chassis brake; processing the original point cloud data and the test point cloud data, and calculating the abrasion loss of the chassis brake under different load torques; the performance of the chassis brake is evaluated based on the amount of wear of the chassis brake at different load torques and based on a maximum deviation weighting method. The invention can more accurately detect the abrasion condition of the brake, carries out performance evaluation based on a maximum deviation weighting method, and can help to find and emphasize the most serious problems and defects, thereby helping to improve the product and the quality, and having important value for improving the detection and evaluation effects of the chassis brake.
Description
Technical Field
The invention relates to the technical field of brake detection, in particular to a chassis brake detection method and system based on load torque.
Background
Chassis brakes are one of the important brake system components of an automobile or other motor vehicle for slowing the vehicle or stopping the vehicle completely to ensure safety of travel. It achieves a braking effect by applying friction to the wheel, converting mechanical energy into thermal energy. The chassis brake is generally composed of a brake disc, a brake pad, a brake caliper, brake fluid, a master cylinder, a brake pedal, and the like. When the driver depresses the brake pedal, the master cylinder generates pressure and is transferred to the caliper via brake fluid. The piston in the caliper will press the brake pads against the brake disc, thereby creating friction. The friction will be converted into heat energy, slowing or stopping the wheel.
Load torque refers to torque in a mechanical system caused by the action of an external load. In an automotive chassis brake, load torque refers to the torque generated to the chassis brake during braking due to vehicle mass and braking forces. When the vehicle is traveling, the chassis brakes need to brake the wheels to slow down or stop. The torque generated by the braking force is transferred to the chassis brakes causing them to generate braking force and prevent the wheels from rotating. The magnitude of the load torque depends on the mass of the vehicle and the magnitude of the braking force.
At present, the traditional chassis brake detection method generally can only detect the whole chassis brake, and cannot accurately evaluate the abrasion condition under different load torques, so that the performance and the abrasion condition of the chassis brake are ignored or misjudged under different load conditions, the performance and the safety of the chassis brake are affected, the brake is worn in advance, the performance of the brake is insufficient in some important driving situations, and the risk of traffic accidents is increased.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for detecting a chassis brake based on a load torque, so as to solve the problem that the above-mentioned conventional method for detecting a chassis brake generally only can detect the entire chassis brake, and cannot accurately evaluate the wear condition under different load torques.
In order to solve the problems, the invention adopts the following specific technical scheme:
according to an aspect of the present invention, there is provided a load torque-based chassis brake detection method including the steps of:
s1, acquiring original point cloud data of a chassis brake through a three-dimensional laser scanner;
s2, performing simulation test on the chassis brake under different load torques, and acquiring test point cloud data of the chassis brake through a three-dimensional laser scanner after the test is completed;
s3, processing original point cloud data and test point cloud data based on a point cloud comparison algorithm, and calculating abrasion loss of a chassis brake under different load torques;
and S4, evaluating the performance of the chassis brake based on a maximum deviation weighting method according to the abrasion loss of the chassis brake under different load torques.
Preferably, processing the original point cloud data and the test point cloud data based on a point cloud comparison algorithm, and calculating the wear amount of the chassis brake under different load torques comprises the following steps:
s31, preprocessing original point cloud data and test point cloud data respectively, wherein the preprocessing comprises outlier removal and filtering smoothing;
s32, core point cloud data are selected from the preprocessed original point cloud data and test point cloud data by using a voxel grid method;
s33, calculating the normal direction of a core point in core point cloud data of original point cloud data and test point cloud data respectively;
s34, respectively determining the best fitting planes of core points in the core point cloud data of the original point cloud data and the core point cloud data of the test point cloud data according to the direction of the normal line;
and S35, calculating average positions of core point clouds in the original point cloud data and the test point cloud data according to the normal direction of the core points and the best fit plane, and calculating abrasion loss of the chassis brake under different load torques according to the obtained average positions.
Preferably, selecting core point cloud data from the preprocessed original point cloud data and test point cloud data by using a voxel grid method comprises the following steps:
s321, respectively establishing an axial bounding box for the preprocessed original point cloud data and the preprocessed test point cloud data to obtain side lengths in the x, y and z directions;
s322, equally dividing edges of the bounding box along the x, y and z directions in the axial direction by a preset equally dividing distance to obtain a plurality of voxels with square shapes;
s323, calculating the gravity centers of all points in each voxel, and taking the gravity centers as sampling values of the voxels;
and S324, connecting sampling values of all voxels to obtain core point cloud data.
Preferably, calculating the normal direction of the core point in the core point cloud data of the original point cloud data and the test point cloud data, respectively, includes the following steps:
s331, performing neighbor search on core point cloud data of original point cloud data and test point cloud data respectively, and finding k nearest neighbor points for each core point;
s332, constructing a covariance matrix for each core point and k nearest neighbor points;
s333, calculating eigenvalues and eigenvectors of the covariance matrix, and selecting the eigenvector with the smallest eigenvalue as a normal direction.
Preferably, calculating average positions of the core point clouds in the original point cloud data and the test point cloud data according to the normal direction of the core points and the best fit plane, and calculating the wear amount of the chassis brake under different load torques according to the obtained average positions includes the following steps:
s351, aligning original point cloud data and test point cloud data to the same coordinate system by utilizing an ICP registration algorithm;
s352, dividing areas of the original point cloud data and core point cloud data of the test point cloud data according to the abrasion surface of the chassis brake;
and S353, calculating the region abrasion loss of the core point cloud data in the test point cloud data for each divided region.
Preferably, for each divided area, calculating the area abrasion loss of the core point cloud data in the test point cloud data includes the following steps:
s3531, defining a projection radius r for each divided area, and respectively starting from the best fitting plane along the normal direction of a core point in core point cloud data of original point cloud data and test point cloud data, and determining a cylinder which takes the normal direction as an axis and is intersected with the original point cloud data and the test point cloud data;
s3532, searching all core point clouds containing original point cloud data and test point cloud data in the cylinder, and respectively calculating average positions of all core point clouds containing the original point cloud data and the test point cloud data in the cylinder along the normal direction;
and S3533, calculating the difference value between the average positions of all core point clouds containing the original point cloud data and the average positions of all core point clouds containing the test point cloud data in the cylinder of each area, and obtaining the area abrasion loss of the core point cloud data in the test point cloud data.
Preferably, the evaluation of the performance of the chassis brake based on the maximum deviation weighting method based on the wear amount of the chassis brake under different load torques comprises the following steps:
s41, calculating the wear rate and the wear uniformity of the chassis brake according to the wear amounts of the chassis brake under different load torques;
s42, taking the abrasion loss, abrasion speed and abrasion uniformity of the chassis brake under different load torques as evaluation indexes;
s43, calculating the weight of each evaluation index by using a maximum deviation weighting method;
s44, evaluating the performance of the chassis brake by using an extensible material evaluation method according to the weight of each evaluation index.
Preferably, the evaluation of the performance of the chassis brake by the extensible material evaluation method according to the weight of each evaluation index comprises the following steps:
s441, determining a value range of each evaluation index performance level;
s442, establishing a corresponding extension set according to the value range of each evaluation index performance level, and converting the evaluation index value into the value range of each performance level;
s443, establishing a correlation function for each evaluation index, and calculating the correlation degree of each evaluation index according to the correlation function and the weight of each evaluation index;
and S444, determining the performance grade of the chassis brake through a maximum membership rule.
Preferably, a calculation formula for calculating the association degree of each evaluation index according to the association function and the weight of each evaluation index is as follows:；
in the method, in the process of the invention,Rrepresenting the association degree of the evaluation index;
ω i represent the firstiWeights corresponding to the evaluation indexes;
nrepresenting the number of evaluation indexes;
K j (v i ) Represent the firstiThe first evaluation indexjAn association function of performance levels.
According to another aspect of the present invention, there is provided a load torque based chassis brake detection system comprising: the system comprises an original point cloud data acquisition module, a test point cloud data acquisition module, a wear amount calculation module and a performance evaluation module;
the original point cloud data acquisition module is used for acquiring original point cloud data of the chassis brake through the three-dimensional laser scanner;
the test point cloud data acquisition module is used for carrying out simulation test on the chassis brake under different load torques and acquiring test point cloud data of the chassis brake through the three-dimensional laser scanner after the test is completed;
the abrasion loss calculation module is used for processing the original point cloud data and the test point cloud data based on a point cloud comparison algorithm and calculating the abrasion loss of the chassis brake under different load torques;
and the performance evaluation module is used for evaluating the performance of the chassis brake based on the maximum deviation weighting method according to the abrasion amounts of the chassis brake under different load torques.
Compared with the prior art, the invention provides a chassis brake detection method and system based on load torque, which have the following beneficial effects:
(1) According to the invention, the chassis brake is tested under different load torques, and the three-dimensional laser scanner is used for acquiring the original point cloud data and the test point cloud data, so that the abrasion condition of the brake can be detected more accurately, the performance evaluation is carried out based on the maximum deviation weighting method, and the most serious problems and defects can be found and emphasized, thereby helping to improve the product and the quality, and having important value for improving the detection and evaluation effects of the chassis brake.
(2) According to the invention, the accuracy of data processing can be improved by preprocessing and selecting core point cloud data by using a voxel grid method, then the spatial position of the point cloud can be more accurately determined by calculating the normal direction and the best fitting plane of each core point, a large amount of point cloud data can be automatically processed by using a least square method, an ICP registration algorithm, a voxel grid method and the like, the efficiency of data processing is greatly improved, more comprehensive abrasion information can be obtained by calculating the abrasion amount of each region of the abrasion surface of the chassis brake, and the abrasion condition of the chassis brake is helped to be known.
(3) According to the invention, the wear amount, the wear rate and the wear uniformity of the chassis brake under different load torques are used as evaluation indexes, the performance of the chassis brake can be comprehensively evaluated, the weight of each evaluation index is calculated by using a maximum deviation weighting method, the influence of each index on the performance of the chassis brake can be quantized, so that the evaluation result has a higher reference value, the performance grade can be dynamically adjusted by using an extensible matter evaluation method, the evaluation result is timely adjusted according to the change of the performance grade, and the evaluation flexibility is increased.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart of a method of load torque based chassis brake detection in accordance with an embodiment of the present invention;
FIG. 2 is a functional block diagram of a load torque based chassis brake detection system according to an embodiment of the present invention.
In the figure:
1. an original point cloud data acquisition module; 2. a test point cloud data acquisition module; 3. a wear amount calculation module; 4. and a performance evaluation module.
Detailed Description
In order to make the technical solutions in the present application better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, based on the embodiments herein, which would be apparent to one of ordinary skill in the art without undue burden are intended to be within the scope of the present application.
According to the embodiment of the invention, a chassis brake detection method and system based on load torque are provided.
The invention will now be further described with reference to the drawings and detailed description, as shown in fig. 1, according to an embodiment of the invention, there is provided a method for detecting a chassis brake based on a load torque, the method for detecting a chassis brake based on a load torque comprising the steps of:
s1, acquiring original point cloud data of a chassis brake through a three-dimensional laser scanner.
The three-dimensional laser scanning technology is a non-contact, rapid and high-precision measurement technology. The method can acquire the original point cloud data of the chassis brake, and the data has rich information. The chassis brake needs to be cleaned and positioned within the field of view of the scanner before it can be scanned.
S2, performing simulation test on the chassis brake under different load torques, and acquiring test point cloud data of the chassis brake through a three-dimensional laser scanner after the test is completed.
The chassis brake is subjected to simulation tests under different load torques using appropriate test equipment and test procedures. These tests may include simulating the operation of the brake under different operating conditions, and after the test is completed, scanning the chassis brake using a three-dimensional laser scanner to obtain its test point cloud data.
And S3, processing the original point cloud data and the test point cloud data based on a point cloud comparison algorithm, and calculating the abrasion loss of the chassis brake under different load torques.
As a preferred embodiment, processing the original point cloud data and the test point cloud data based on the point cloud comparison algorithm, and calculating the wear amount of the chassis brake under different load torques includes the following steps:
s31, preprocessing the original point cloud data and the test point cloud data respectively, wherein the preprocessing comprises outlier removal and filtering smoothing.
It should be noted that preprocessing is an important step of three-dimensional point cloud data processing, and aims to improve the quality and the ease of processing the data, so that the quality of the point cloud data can be ensured, and a good data base is provided for subsequent analysis and evaluation.
Outliers, among others, are those points that are too far from most data points, which may be due to equipment errors, environmental noise, etc. Outliers may affect the accuracy of subsequent analysis and therefore need to be identified and deleted. Common outlier detection methods include distance-based methods, density-based methods, and the like.
Filtering smoothing refers to removing noise in point cloud data by some kind of filter so that the data is smoothed. This may generally help to promote the effect of subsequent analysis. Common filtering smoothing methods include mean filtering, median filtering, gaussian filtering, etc.
S32, core point cloud data are selected from the preprocessed original point cloud data and the preprocessed test point cloud data respectively by using a voxel grid method.
Specifically, the selection of the core point cloud data can effectively reduce the number of the point cloud data, and meanwhile, main information of the original data is reserved.
As a preferred embodiment, selecting core point cloud data from the preprocessed original point cloud data and test point cloud data by using a voxel grid method includes the following steps:
s321, respectively establishing an axial bounding box for the preprocessed original point cloud data and the preprocessed test point cloud data to obtain side lengths in the x, y and z directions.
It should be noted that, for each point in the point cloud data, the maximum value and the minimum value of the x, y and z coordinates of the point cloud data are found, boundaries of the point cloud data in the x, y and z directions are formed, and an axial bounding box is established by using the boundary values.
S322, equally dividing edges of the bounding box along the x, y and z directions along the axial direction by a preset equally dividing distance to obtain a plurality of voxels with the shape of a cube.
It should be noted that, a predetermined equal distance is determined, and this distance will determine the size of the voxels, i.e. the side length of each voxel; then, dividing each side length of the bounding box by a preset equal dividing distance to calculate how many voxels are needed in the x, y and z directions; and starting from one corner of the bounding box, carrying out equal division along the directions of x, y and z axes according to preset equal division distances, wherein each equal division interval corresponds to one voxel. Thus, a plurality of voxels having a square shape can be obtained, and a voxel grid can be formed.
S323, calculating the gravity centers of all points in each voxel, and taking the gravity centers as sampling values of the voxels.
It should be noted that for each voxel in the voxel grid, all points falling within this voxel are found. Specifically, if the x, y, z coordinates of a point are all within the boundary of a voxel, then the point is considered to be within that voxel; for these points within a voxel, the average of the x, y and z coordinates is obtained by summing the x, y and z coordinates of each point separately and dividing by the total number of points separately. These three averages constitute the center of gravity of all points within this voxel.
And S324, connecting sampling values of all voxels to obtain core point cloud data.
S33, calculating the normal direction of the core point in the core point cloud data of the original point cloud data and the core point cloud data of the test point cloud data respectively.
As a preferred embodiment, calculating the normal direction of the core point in the core point cloud data of the original point cloud data and the test point cloud data, respectively, includes the steps of:
and S331, respectively carrying out neighbor search on core point cloud data of the original point cloud data and the core point cloud data of the test point cloud data, and finding k nearest neighbor points for each core point.
It should be noted that, for the core point cloud data of the original point cloud data and the test point cloud data, a search structure is constructed by utilizing a spherical nearest neighbor algorithm, and the search structure can help us to quickly find the nearest neighbor of any given point, and for each core point, k nearest neighbor points are found by the search structure, so that we can better understand and compare the structures and the distributions of the two groups of data.
S332, constructing a covariance matrix for each core point and k nearest neighbor points.
It should be noted that, for the core points and k nearest neighbor points thereof, the mean value of the core points in the x, y and z coordinates is calculated, and for each core point, the covariance matrix is obtained by calculating the difference between the coordinates and the mean value, then calculating the outer products of the differences, adding all the outer products, and finally dividing the sum by k.
S333, calculating eigenvalues and eigenvectors of the covariance matrix, and selecting the eigenvector with the smallest eigenvalue as a normal direction.
It should be noted that, the covariance matrix of each core point may be calculated by using a standard linear algebra method, and the eigenvector with the smallest eigenvalue may be selected, because in the 3D space, the eigenvector with the smallest eigenvalue generally represents the smallest variation direction, which is also the plane direction of the point cloud distribution, i.e. the normal direction.
S34, respectively determining the best fitting planes of core points in the core point cloud data of the original point cloud data and the core point cloud data of the test point cloud data according to the direction of the normal line.
It should be noted that, the best fit plane is determined according to the normal direction of the core points and their average values in the x, y, z coordinates, and in the 3D space, a plane may be defined by one point and one normal direction.
And S35, calculating average positions of core point clouds in the original point cloud data and the test point cloud data according to the normal direction of the core points and the best fit plane, and calculating abrasion loss of the chassis brake under different load torques according to the obtained average positions.
As a preferred embodiment, calculating average positions of core point clouds in the original point cloud data and the test point cloud data according to the normal direction of the core points and the best fit plane, and calculating wear amounts of the chassis brake under different load torques according to the obtained average positions includes the steps of:
s351, aligning the original point cloud data and the test point cloud data to the same coordinate system by utilizing an ICP registration algorithm.
It should be noted that, using the iterative closest point (Iterative Closest Point, ICP) registration algorithm is a common method to align or register two point cloud data, specifically including the following steps:
step one, randomly selecting points in original point cloud data as initial corresponding points;
finding a point closest to the initial corresponding point in the cloud data of the test point;
step three, calculating a rigid transformation (comprising rotation and translation) so as to minimize the distance from all points in the original point cloud data to the corresponding points in the test point cloud data;
and step four, applying the transformation to the original point cloud data, and repeating the step two and the step three until a certain termination condition is met, such as the maximum iteration number is reached or the change amount of the transformation matrix is lower than a certain threshold value.
And S352, dividing the areas of the original point cloud data and the core point cloud data of the test point cloud data according to the abrasion surface of the chassis brake.
It should be noted that, using a plane detection algorithm to find a normal line and a point representing a best fit plane of the wear surface, dividing the point cloud data into two regions based on the normal line direction, all points on one side of the normal line belong to one region, and points on the other side belong to the other region, and repeating the division for each region until the termination condition is satisfied.
And S353, calculating the region abrasion loss of the core point cloud data in the test point cloud data for each divided region.
As a preferred embodiment, for each divided area, calculating the area abrasion amount of the core point cloud data in the test point cloud data includes the steps of:
s3531, defining a projection radius r for each divided area, and determining a cylinder which takes the normal direction as an axis and is intersected with the original point cloud data and the test point cloud data along the normal direction of a core point in the core point cloud data of the original point cloud data and the test point cloud data respectively from the best fit plane where the core point is located.
It should be noted that for each core point, a cylinder is created. The axis direction of the cylinder is the normal direction of the core point, and a point on the axis can be selected as a point on the best fit plane where the core point is located. The radius of the cylinder is r. Thus, a cylinder is defined which is oriented in the normal direction and intersects the original point cloud data and the test point cloud data.
S3532, searching all core point clouds containing the original point cloud data and the test point cloud data in the cylinder, and respectively calculating average positions of all core point clouds containing the original point cloud data and the test point cloud data in the cylinder along the normal direction.
It should be noted that, for each cylinder, by traversing all points in the original point cloud data and the test point cloud data, it is checked whether each point is within the current cylinder. This can be achieved by calculating the distance of the point from the cylinder axis and comparing it with the radius r. If the distance is less than r, the point is considered to be within the cylinder. For each point in the cylinder, projecting the point in the normal direction of the cylinder, which can be realized by calculating the intersection point of the point and the axis of the cylinder and taking the intersection point as a projection point; and respectively calculating the average value of all the projection points to obtain the average positions of the original point cloud data and the test point cloud data in each cylinder.
And S3533, calculating the difference value between the average positions of all core point clouds containing the original point cloud data and the average positions of all core point clouds containing the test point cloud data in the cylinder of each area, and obtaining the area abrasion loss of the core point cloud data in the test point cloud data.
Specifically, the accuracy of data processing can be improved by preprocessing and selecting core point cloud data by using a voxel grid method, then the spatial position of the point cloud can be more accurately determined by calculating the normal direction and the best fitting plane of each core point, a large amount of point cloud data can be automatically processed by using a least square method, an ICP registration algorithm, a voxel grid method and the like, the efficiency of data processing is greatly improved, more comprehensive abrasion information can be obtained by calculating the abrasion amount of each region of the abrasion surface of the chassis brake, and the abrasion condition of the chassis brake is helped to be known.
And S4, evaluating the performance of the chassis brake based on a maximum deviation weighting method according to the abrasion loss of the chassis brake under different load torques.
As a preferred embodiment, the evaluation of the performance of the chassis brake based on the maximum deviation weighting method according to the wear amount of the chassis brake under different load torques comprises the following steps:
s41, calculating the wear rate and the wear uniformity of the chassis brake according to the wear amount of the chassis brake under different load torques.
The wear rate is the amount of wear of the chassis brake per unit load torque. In particular, this may be calculated by dividing the total wear amount by the total load torque.
The uniformity of wear is typically expressed by calculating the standard deviation or variance of the amount of wear. If the standard deviation or variance of the amount of wear is small, we can consider the wear to be relatively uniform; conversely, if the standard deviation or variance is large, the wear is uneven.
S42, taking the abrasion loss, abrasion speed and abrasion uniformity of the chassis brake under different load torques as evaluation indexes.
S43, calculating the weight of each evaluation index by using a maximum deviation weighting method.
The maximum deviation of each evaluation index, that is, the maximum difference between all values of the index and the average value thereof is calculated first. Then we calculate the sum of the maximum deviations of all the indices. Finally, the weight of each indicator is the ratio of the maximum deviation of that indicator to the total maximum deviation.
S44, evaluating the performance of the chassis brake by using an extensible material evaluation method according to the weight of each evaluation index.
As a preferred embodiment, the evaluation of the performance of the chassis brake by the extensible material evaluation method according to the weight of each evaluation index includes the steps of:
s441, determining a value range of each evaluation index performance level.
It should be noted that, first, several performance levels are defined. For example, four grades of excellent, good, general, and bad may be used; then, a value range of each performance level is set for each evaluation index, for example, for the wear rate, the excellent range may be 0 to 0.2, the good range may be 0.2 to 0.4, the general range may be 0.4 to 0.6, and the bad range may be 0.6 or more.
S442, establishing a corresponding extension set according to the value range of each evaluation index performance grade, and converting the evaluation index value into the value range of each performance grade.
In the extension evaluation method, the extension set is a fuzzy set, which is used to represent the degree of ambiguity of an object on a certain attribute. Here, the value range of each performance level may be regarded as an extension set, and we may convert the evaluation index value into the value range of each performance level, so as to represent the membership degree of the index on each performance level.
S443, establishing a correlation function for each evaluation index, and calculating the correlation degree of each evaluation index according to the correlation function and the weight of each evaluation index.
The association function is a function that maps an index value to an association value. By defining the association function, if the index value falls in the value range of each performance grade, the association value can be positive number, otherwise, the association value is negative number; if the index value is equal to the range of values for each performance level, the associated value may take a suitably small value, such as 0.02.
In a preferred embodiment, the calculation formula for calculating the association degree of each evaluation index from the association function and the weight of each evaluation index is:；
in the method, in the process of the invention,Rrepresenting the association degree of the evaluation index;
ω i represent the firstiWeights corresponding to the evaluation indexes;
nrepresenting the number of evaluation indexes;
K j (v i ) Represent the firstiThe first evaluation indexjAn association function of performance levels.
And S444, determining the performance grade of the chassis brake through a maximum membership rule.
It should be noted that the maximum membership rule is a fuzzy logic decision rule, which selects the class with the highest membership as a decision result, and calculates the membership of each performance class of the chassis brake, and if the membership of the good class is the highest, the performance class of the chassis brake is good.
Specifically, the performance of the chassis brake can be comprehensively evaluated by taking the wear amount, the wear rate and the wear uniformity of the chassis brake under different load torques as evaluation indexes, the weight of each evaluation index is calculated by using a maximum deviation weighting method, the influence of each index on the performance of the chassis brake can be quantized, the evaluation result has a higher reference value, the performance grade can be dynamically adjusted by an extensible matter evaluation method, the evaluation result is timely adjusted according to the change of the performance grade, and the evaluation flexibility is improved.
According to another embodiment of the present invention, as shown in fig. 2, there is provided a load torque-based chassis brake detection system including: the system comprises an original point cloud data acquisition module 1, a test point cloud data acquisition module 2, a wear amount calculation module 3 and a performance evaluation module 4;
the original point cloud data acquisition module 1 is used for acquiring original point cloud data of the chassis brake through a three-dimensional laser scanner;
the test point cloud data acquisition module 2 is used for carrying out simulation test on the chassis brake under different load torques and acquiring test point cloud data of the chassis brake through a three-dimensional laser scanner after the test is completed;
the abrasion loss calculation module 3 is used for processing the original point cloud data and the test point cloud data based on a point cloud comparison algorithm and calculating the abrasion loss of the chassis brake under different load torques;
and the performance evaluation module 4 is used for evaluating the performance of the chassis brake based on the maximum deviation weighting method according to the abrasion amounts of the chassis brake under different load torques.
In summary, by means of the technical scheme, the invention can more accurately detect the abrasion condition of the brake by testing the chassis brake under different load torques and acquiring the original point cloud data and the test point cloud data by using the three-dimensional laser scanner, and can help to find and emphasize the most serious problems and defects by performing performance evaluation based on a maximum deviation weighting method, thereby helping to improve products and quality, and having important value for improving the detection and evaluation effects of the chassis brake; according to the invention, the accuracy of data processing can be improved by preprocessing and selecting core point cloud data by using a voxel grid method, then the spatial position of the point cloud can be more accurately determined by calculating the normal direction and the best fitting plane of each core point, a large amount of point cloud data can be automatically processed by using a least square method, an ICP registration algorithm, a voxel grid method and the like, the efficiency of data processing is greatly improved, more comprehensive abrasion information can be obtained by calculating the abrasion amount of each region of the abrasion surface of the chassis brake, and the abrasion condition of the chassis brake is helped to be known; according to the invention, the wear amount, the wear rate and the wear uniformity of the chassis brake under different load torques are used as evaluation indexes, the performance of the chassis brake can be comprehensively evaluated, the weight of each evaluation index is calculated by using a maximum deviation weighting method, the influence of each index on the performance of the chassis brake can be quantized, so that the evaluation result has a higher reference value, the performance grade can be dynamically adjusted by using an extensible matter evaluation method, the evaluation result is timely adjusted according to the change of the performance grade, and the evaluation flexibility is increased.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. The chassis brake detection method based on the load torque is characterized by comprising the following steps of:
s1, acquiring original point cloud data of a chassis brake through a three-dimensional laser scanner;
s2, performing simulation test on the chassis brake under different load torques, and acquiring test point cloud data of the chassis brake through a three-dimensional laser scanner after the test is completed;
s3, processing original point cloud data and test point cloud data based on a point cloud comparison algorithm, and calculating abrasion loss of a chassis brake under different load torques;
s4, evaluating the performance of the chassis brake based on a maximum deviation weighting method according to the abrasion loss of the chassis brake under different load torques;
the method for processing the original point cloud data and the test point cloud data based on the point cloud comparison algorithm and calculating the abrasion loss of the chassis brake under different load torques comprises the following steps:
s31, preprocessing original point cloud data and test point cloud data respectively, wherein the preprocessing comprises outlier removal and filtering smoothing;
s32, core point cloud data are selected from the preprocessed original point cloud data and test point cloud data by using a voxel grid method;
s33, calculating the normal direction of a core point in core point cloud data of original point cloud data and test point cloud data respectively;
s34, respectively determining the best fitting planes of core points in the core point cloud data of the original point cloud data and the core point cloud data of the test point cloud data according to the direction of the normal line;
s35, calculating average positions of core point clouds in the original point cloud data and the test point cloud data according to the normal direction of the core points and the best fit plane, and calculating abrasion loss of the chassis brake under different load torques according to the obtained average positions;
calculating average positions of core point clouds in the original point cloud data and the test point cloud data according to the normal direction of the core points and the best fit plane, and calculating wear amounts of the chassis brake under different load torques according to the obtained average positions, wherein the method comprises the following steps:
s351, aligning original point cloud data and test point cloud data to the same coordinate system by utilizing an ICP registration algorithm;
s352, dividing areas of the original point cloud data and core point cloud data of the test point cloud data according to the abrasion surface of the chassis brake;
s353, calculating the region abrasion loss of the core point cloud data in the test point cloud data for each divided region;
for each divided area, calculating the area abrasion loss of the core point cloud data in the test point cloud data comprises the following steps:
s3531, defining a projection radius r for each divided area, and respectively starting from the best fitting plane along the normal direction of a core point in core point cloud data of original point cloud data and test point cloud data, and determining a cylinder which takes the normal direction as an axis and is intersected with the original point cloud data and the test point cloud data;
s3532, searching all core point clouds containing original point cloud data and test point cloud data in the cylinder, and respectively calculating average positions of all core point clouds containing the original point cloud data and the test point cloud data in the cylinder along the normal direction;
and S3533, calculating the difference value between the average positions of all core point clouds containing the original point cloud data and the average positions of all core point clouds containing the test point cloud data in the cylinder of each area, and obtaining the area abrasion loss of the core point cloud data in the test point cloud data.
2. The method for detecting the chassis brake based on the load torque according to claim 1, wherein the selecting core point cloud data from the preprocessed original point cloud data and the preprocessed test point cloud data by using a voxel grid method comprises the following steps:
s321, respectively establishing an axial bounding box for the preprocessed original point cloud data and the preprocessed test point cloud data to obtain side lengths in the x, y and z directions;
s322, equally dividing edges of the bounding box along the x, y and z directions in the axial direction by a preset equally dividing distance to obtain a plurality of voxels with square shapes;
s323, calculating the gravity centers of all points in each voxel, and taking the gravity centers as sampling values of the voxels;
and S324, connecting sampling values of all voxels to obtain core point cloud data.
3. The method for detecting the chassis brake based on the load torque according to claim 1, wherein the calculating the normal direction of the core point in the core point cloud data of the original point cloud data and the test point cloud data, respectively, comprises the steps of:
s331, performing neighbor search on core point cloud data of original point cloud data and test point cloud data respectively, and finding k nearest neighbor points for each core point;
s332, constructing a covariance matrix for each core point and k nearest neighbor points;
s333, calculating eigenvalues and eigenvectors of the covariance matrix, and selecting the eigenvector with the smallest eigenvalue as a normal direction.
4. The method for detecting the chassis brake based on the load torque according to claim 1, wherein the evaluating the performance of the chassis brake based on the maximum deviation weighting method according to the wear amount of the chassis brake under different load torques comprises the steps of:
s41, calculating the wear rate and the wear uniformity of the chassis brake according to the wear amounts of the chassis brake under different load torques;
s42, taking the abrasion loss, abrasion speed and abrasion uniformity of the chassis brake under different load torques as evaluation indexes;
s43, calculating the weight of each evaluation index by using a maximum deviation weighting method;
s44, evaluating the performance of the chassis brake by using an extensible material evaluation method according to the weight of each evaluation index.
5. The method for detecting the chassis brake based on the load torque according to claim 4, wherein the evaluating the performance of the chassis brake by the extensible matter evaluation method according to the weight of each evaluation index comprises the following steps:
s441, determining a value range of each evaluation index performance level;
s442, establishing a corresponding extension set according to the value range of each evaluation index performance level, and converting the evaluation index value into the value range of each performance level;
s443, establishing a correlation function for each evaluation index, and calculating the correlation degree of each evaluation index according to the correlation function and the weight of each evaluation index;
and S444, determining the performance grade of the chassis brake through a maximum membership rule.
6. The method for detecting a chassis brake based on a load torque according to claim 5, wherein the calculation formula for calculating the association degree of each evaluation index according to the association function and the weight of each evaluation index is:
；
in the method, in the process of the invention,Rrepresenting the association degree of the evaluation index;
ω i represent the firstiWeights corresponding to the evaluation indexes;
nrepresenting the number of evaluation indexes;
K j (v i ) Represent the firstiThe first evaluation indexjAn association function of performance levels.
7. A load torque based chassis brake detection system for implementing the load torque based chassis brake detection method of any one of claims 1-6, comprising: the system comprises an original point cloud data acquisition module, a test point cloud data acquisition module, a wear amount calculation module and a performance evaluation module;
the original point cloud data acquisition module is used for acquiring original point cloud data of the chassis brake through the three-dimensional laser scanner;
the test point cloud data acquisition module is used for carrying out simulation test on the chassis brake under different load torques and acquiring test point cloud data of the chassis brake through the three-dimensional laser scanner after the test is completed;
the abrasion amount calculation module is used for processing the original point cloud data and the test point cloud data based on a point cloud comparison algorithm and calculating abrasion amounts of the chassis brake under different load torques;
the performance evaluation module is used for evaluating the performance of the chassis brake based on a maximum deviation weighting method according to the abrasion loss of the chassis brake under different load torques;
the processing of the original point cloud data and the test point cloud data based on the point cloud comparison algorithm, and the calculation of the abrasion loss of the chassis brake under different load torques comprises the following steps:
preprocessing original point cloud data and test point cloud data respectively, wherein the preprocessing comprises outlier removal and filtering smoothing;
selecting core point cloud data from the preprocessed original point cloud data and test point cloud data by using a voxel grid method;
respectively calculating the normal direction of a core point in core point cloud data of the original point cloud data and the test point cloud data;
respectively determining the best fitting planes of core points in the core point cloud data of the original point cloud data and the core point cloud data of the test point cloud data according to the direction of the normal line;
calculating average positions of core point clouds in the original point cloud data and the test point cloud data according to the normal direction of the core points and the best fit plane, and calculating abrasion loss of the chassis brake under different load torques according to the obtained average positions;
calculating average positions of core point clouds in the original point cloud data and the test point cloud data according to the normal direction of the core points and the best fit plane, and calculating wear amounts of the chassis brake under different load torques according to the obtained average positions comprises the following steps:
aligning the original point cloud data and the test point cloud data to the same coordinate system by utilizing an ICP registration algorithm;
according to the wear surface of the chassis brake, carrying out region division on core point cloud data of original point cloud data and test point cloud data;
for each divided area, calculating the area abrasion loss of core point cloud data in the test point cloud data;
for each divided area, calculating the area abrasion loss of the core point cloud data in the test point cloud data includes:
defining a projection radius r for each divided area, and respectively starting from the best fitting plane of the core point in the core point cloud data of the original point cloud data and the test point cloud data along the normal direction of the core point, and determining a cylinder which takes the normal direction as an axis and is intersected with the original point cloud data and the test point cloud data;
searching all core point clouds containing original point cloud data and test point cloud data in the cylinder, and respectively calculating average positions of all core point clouds containing the original point cloud data and the test point cloud data in the cylinder along the normal direction;
and calculating the difference value between all core point cloud average positions containing original point cloud data and all core point cloud average positions containing test point cloud data in the cylinder of each area to obtain the area abrasion loss of the core point cloud data in the test point cloud data.
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