A method for detecting the height of the oil return line of an oil seal

Abstract

This application relates to the technical field of oil seal detection, and specifically relates to a method for detecting the height of the oil return line of an oil seal. The method includes: collecting all point cloud data on the inner ring of the oil seal; constructing a cylinder corresponding to the oil seal and dividing the bottom surface of the cylinder into multiple sector blocks; calculating the axis distance; calculating the deformation characterization values of each sector block; determining the local concentration degree of each point cloud data; determining the discrimination coefficient of each point cloud data to obtain the point cloud data corresponding to the oil return line; clustering all the point cloud data corresponding to the oil return line to determine the adjustment coefficient of each point cloud data corresponding to the oil return line; determining the filtering scale of each point cloud data corresponding to the oil return line, combining with a filtering algorithm for filtering, and performing three-dimensional reconstruction on all the filtered point cloud data to measure the height of the oil return line of the oil seal. This application dynamically selects an appropriate filtering scale of the filtering algorithm, enhances the filtering effect, and improves the accuracy of detecting the height of the oil return line of the oil seal.

Description

Technical Field

[0001] This application relates to the technical field of oil seal detection, and specifically relates to a method for detecting the height of the oil return line of an oil seal. Background Art

[0002] The oil return line on the oil seal is located at the main lip part of its inner side wall in a threaded structure. When the shaft rotates, the pumping action generated by the oil return line effectively prevents the loss of lubricating oil and the intrusion of external contaminants, thus ensuring the normal operation of the machinery and extending the service life of the machine. The height of the oil return line thread affects the sealing performance of the oil seal. Insufficient height may lead to seal failure or poor grease backflow, while excessive height will increase frictional losses.

[0003] During the production process of oil seals, traditional methods for detecting the height of the oil return line generally use contact measurement methods such as product slicing and silicone printing. These methods are not only cumbersome and inefficient in operation, but also can only perform sampling inspections, unable to meet the full inspection requirements of the oil seal production line. Ultra-high-speed profilometers can meet the full inspection requirements for mass production, but they need to perform filtering and denoising processing on the 3D point cloud data on the oil return line of the oil seal. When performing K-nearest neighbor mean filtering, generally, the point cloud data within a fixed filtering scale is directly selected as the neighborhood point set, which is extremely likely to destroy the directional texture features of the point cloud data on the threaded oil return line and is difficult to retain the edge detail features of the threaded oil return line, resulting in large errors in the detection of the oil return line height. Summary of the Invention

[0004] In order to solve the above technical problems, a method for detecting the height of the oil return line of an oil seal is provided to solve the existing problems.

[0005] The solution of this application to solve the technical problem is to provide a method for detecting the height of the oil return line of an oil seal, including the following steps:

[0006] Collect all the point cloud data on the inner ring of the oil seal;

[0007] Perform three-dimensional fitting on all the point cloud data to construct a cylinder corresponding to the oil seal. Divide the bottom surface of the cylinder into multiple sector blocks, and divide each point cloud data into each sector block according to the distribution of the polar angle in the polar coordinates of each point cloud data in each sector block;

[0008] Based on the distance between each point cloud data and the cylinder axis, combined with the radius of the bottom surface of the cylinder, calculate the axis distance; calculate the deformation characterization value of each sector block through the discrete situation and mutation characteristics of the axis distance of the point cloud data between each sector block and the other sector blocks;

[0009] Analyze the distribution density of all the point cloud data in the neighborhood of each point cloud data to determine the local concentration degree of each point cloud data; combine the axis distance and the deformation characterization value to determine the discrimination coefficient of each point cloud data, screen the point cloud data, and obtain the point cloud data corresponding to the oil return line;

[0010] Cluster all the point cloud data corresponding to the oil return line, analyze the deviation between the point cloud data corresponding to the oil return line in each clustering cluster and the remaining point cloud data in its neighborhood, as well as the distribution of the point cloud data in its neighborhood, and determine the adjustment coefficient of each point cloud data corresponding to the oil return line in combination with the deformation characterization value;

[0011] Based on the adjustment coefficient, determine the filtering scale of each point cloud data corresponding to the oil return line, perform filtering in combination with the filtering algorithm, perform three-dimensional reconstruction on all the filtered point cloud data, and measure the height of the oil return line of the oil seal.

[0012] Preferably, the dividing of each point cloud data into each sector block includes:

[0013] Taking the center of the bottom surface of the cylinder as the origin and the cylinder axis as the z-axis, construct a three-dimensional coordinate system; map all the point cloud data on the cylinder to the xOy plane of the three-dimensional coordinate system, and calculate the polar coordinates of each point cloud data;

[0014] According to the sector block area where the polar angle in the polar coordinates of each point cloud data is distributed, divide all the point cloud data into different sector blocks.

[0015] Preferably, the calculation process of the axis distance is: calculate the closest distance from each point cloud data to the cylinder axis; take the ratio between the closest distance and the radius of the cylinder bottom surface as the axis distance of each point cloud data.

[0016] Preferably, the calculation of the deformation characterization value of each sector block includes:

[0017] Calculate the dispersion degree of the axis distances of all the point cloud data in each sector block; calculate the ratio between the dispersion degree of each sector block and the average value of the dispersion degrees of all the sector blocks, and denote it as the relative dispersion ratio;

[0018] Perform mutation point detection on the dispersion degrees of all the sector blocks to obtain the mutation probability of each sector block;

[0019] The deformation characterization value is the normalized result of the product of the relative dispersion ratio and the mutation probability.

[0020] Preferably, the determination of the local concentration degree of each point cloud data includes:

[0021] Taking each point cloud data as the center, denote the neighborhood range with a preset radius as the local neighborhood;

[0022] Calculate the range of values of the position coordinates corresponding to all the point cloud data in the local neighborhood on the x-axis, y-axis, and z-axis respectively; calculate the average value of the range of values corresponding to all the axes;

[0023] The local concentration ratio is the ratio between the average value and the diameter of the local neighborhood.

[0024] Preferably, determining the discrimination coefficient of each point cloud data includes: taking the deformation characterization value of the sector block to which each point cloud data belongs as a weight, and performing a weighted sum of the reciprocal of the local concentration ratio and the reciprocal of the axis distance as the discrimination coefficient of each point cloud data.

[0025] Preferably, obtaining the point cloud data corresponding to the oil return line includes: obtaining the segmentation threshold of the discrimination coefficients of all point cloud data; and marking the point cloud data with the discrimination coefficient greater than the segmentation threshold as the point cloud data corresponding to the oil return line.

[0026] Preferably, determining the adjustment coefficient of each point cloud data corresponding to the oil return line includes:

[0027] By clustering all the point cloud data corresponding to the oil return line, obtaining the local density of each point cloud data corresponding to the oil return line, where the truncation distance of the clustering algorithm is a preset value;

[0028] From the neighborhood corresponding to the truncation distance of any point cloud data corresponding to the oil return line, selecting the mean value of the position coordinates of all the remaining point cloud data corresponding to the oil return line within the same clustering cluster as the neighborhood center coordinate;

[0029] Calculating the distance between the position coordinate of the any point cloud data and the neighborhood center coordinate, and marking it as the center deviation degree;

[0030] Calculating the product value between the local density of each point cloud data corresponding to the oil return line and the deformation characterization value of the sector block to which it belongs;

[0031] The adjustment coefficient is the normalized result of the ratio of the product value to the center deviation degree.

[0032] Preferably, for the th point cloud data corresponding to the oil return line, the filtering scale The calculation formula is: , where is a preset value, is the preset initial filtering scale, is the adjustment coefficient of the th point cloud data corresponding to the oil return line.

[0033] Preferably, measuring the oil return line height of the oil seal includes: according to the three-dimensional cylinder after three-dimensional reconstruction, calculating the minimum distance from each point cloud data corresponding to the oil return line on the three-dimensional cylinder to the axis of the three-dimensional cylinder, and taking the mean value of the difference between the radius of the inner circle of the oil seal and the minimum distance of all point cloud data corresponding to the oil return line as the oil return line height of the oil seal.

[0034] The present application has at least the following beneficial effects:

[0035] In the present application, by fitting all the point cloud data, a cylinder is constructed, and multiple sector blocks are divided on the bottom surface of the cylinder. Based on the distribution of the polar angles of different point cloud data in the sector blocks in the polar coordinates, all the point cloud data within each sector block are obtained. Based on the distances from the respective point cloud data to the cylinder axis, combined with the radius of the bottom surface of the cylinder, the axis distance is calculated, and the deformation characterization value of each sector block is calculated. The beneficial effect lies in considering classifying the point cloud data within the same range of polar angles into one category, so as to analyze the distance between the point cloud data at the position of the sector block and the axis of the cylinder, and to evaluate whether there is a deformation phenomenon at the position of the sector block. Secondly, through the distribution density of all the point cloud data within the neighborhood of each point cloud data, the local concentration degree of each point cloud data is determined, and then the discrimination coefficient of each point cloud data is determined, and the point cloud data corresponding to the oil return line is obtained. The beneficial effect lies in considering the distribution of the point cloud data on the sector blocks in the deformation region. When the point cloud data is distributed on the sector blocks in the deformation region, at this time, it is impossible to distinguish whether the point cloud data is distributed on the oil return line through the inter-axis distance, and it can be judged by the local concentration degree. When the point cloud data is not in the deformation region, at this time, it can be distinguished by the inter-axis distance, improving the recognition accuracy of the point cloud data inside the oil seal inner ring. By separating the point cloud data distributed on the oil return line, the height of the oil return line can be measured subsequently; further, by clustering all the point cloud data corresponding to the oil return line, the beneficial effect lies in classifying the point cloud data on the oil return line belonging to the same circle into one category. Secondly, the adjustment coefficient of each point cloud data corresponding to the oil return line is determined; based on the adjustment coefficient, the filtering scale of each point cloud data corresponding to the oil return line is determined, and filtering is performed in combination with the filtering algorithm. Three-dimensional reconstruction is performed on all the filtered point cloud data, and the height of the oil return line of the oil seal is measured. The beneficial effect lies in dynamically selecting an appropriate filtering scale of the filtering algorithm when filtering each point cloud data corresponding to the oil return line, enhancing the filtering effect while retaining the spiral texture and edge detail features of the oil return line of the oil seal inner ring, reducing the detection error of the height of the oil return line, and improving the accuracy of detecting the height of the oil return line of the oil seal. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The following further details a method for detecting the height of the oil return line of an oil seal according to the present application with reference to the accompanying drawings.

[0037] Figure 1 is a flowchart of the steps of a method for detecting the height of the oil return line of an oil seal provided by an embodiment of the present application;

[0038] Figure 2 is a flowchart of the steps of a method for obtaining the deformation characterization value of each sector block provided by an embodiment of the present application;

[0039] Figure 3 Schematic diagram of the division of fan-shaped blocks in the cylinder corresponding to the inner ring of the oil seal provided by the embodiment of the present application;

[0040] Figure 4 Schematic diagram of the projection of point cloud data onto the xOy plane provided by the embodiment of the present application. Detailed implementation manners

[0041] In order to make the objectives, technical solutions and advantages of the present application clearer, the following further elaborates on a method for detecting the height of the oil return line of an oil seal proposed in the present application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs.

[0043] Please refer to Figure 1 , which shows a flowchart of the steps of a method for detecting the height of the oil return line of an oil seal provided by an embodiment of the present application. The method includes the following steps:

[0044] Step 1, collect all the point cloud data on the inner ring of the oil seal.

[0045] An oil seal is a sealing element used in mechanical equipment to prevent lubricating oil from leaking and external contaminants from entering. It is usually installed between stationary parts, rotating shafts or reciprocating moving parts. The oil return line is a special structure on the inclined surface of the lip of the oil seal, which forms a pumping effect during rotation to pump the lubricating oil flowing into the lip part back to one side of the oil seal, playing a sealing role.

[0046] Therefore, use an automatic pneumatic fixture to fix the oil seal, and use a profile measuring instrument with a rotary scanner probe. Insert the scanner probe into the inner ring of the oil seal and perform a 360° rotary scan to collect all the point cloud data on the inner ring of the oil seal.

[0047] So far, all the point cloud data on the inner ring of the oil seal has been obtained.

[0048] Step 2, perform three-dimensional fitting on all the point cloud data, construct a cylinder corresponding to the oil seal, divide the bottom surface of the cylinder into multiple fan-shaped blocks, and divide each point cloud data into each fan-shaped block according to the distribution of the polar angle in the polar coordinates of each point cloud data; based on the distance between each point cloud data and the cylinder axis, combined with the radius of the bottom surface of the cylinder, calculate the axis distance; calculate the deformation characterization value of each fan-shaped block through the discrete situation and mutation characteristics of the axis distance of the point cloud data between each fan-shaped block and the other fan-shaped blocks.

[0049] The inner ring of the oil seal usually contacts the rotating shaft and its outer shape is roughly cylindrical. The purpose is to form a tight seal with the rotating shaft. Although there may be oil return lines on the surface of the inner ring of the oil seal, these oil return lines are usually local optimizations of the cylindrical surface. On the whole, the inner ring of the oil seal still maintains the basic shape of the cylinder, that is, the three-dimensional geometric shape of the inner ring of the oil seal is approximately cylindrical as a whole.

[0050] Based on the above analysis, all point cloud data are fitted, specifically:

[0051] Perform cylinder fitting on all point cloud data to obtain the cylinder axis;

[0052] In this embodiment, the Random Sample Consensus (RANSAC) algorithm is used for cylinder fitting. By loading all point cloud data into the PCL library, a SACSegmentation object is created, and the model type is set to cylinder SACMODEL_CYLINDER and the method type is set to RANSAC for cylinder fitting. The RANSAC algorithm is a well-known technology and will not be described in detail here.

[0053] Secondly, the unraised portion of the oil seal inner ring surface is the reference surface. The oil seal inner ring mainly consists of a smooth reference surface and a noticeably raised oil return line in the shape of a thread. The point cloud data on the reference surface is farther from the cylinder axis, while the oil return line in the shape of a thread has a certain height, so the point cloud data on the oil return line is closer to the cylinder axis. Therefore, the distance from each point cloud data to the cylinder axis is analyzed, specifically:

[0054] Calculate the shortest distance between each point cloud data and the cylinder axis;

[0055] The ratio between the closest distance and the radius of the cylinder base is used as the axis distance of each point cloud data;

[0056] Furthermore, due to factors such as uneven clamping force of the pneumatic clamp and radial runout of the scanner probe, the point cloud data of the inner ring of the oil seal is deformed and cannot maintain a uniform circular contour. Compared with the distance from the point cloud data on the reference surface to the cylindrical axis, the distance from the point cloud data of the oil return line to the cylindrical axis is greater, making it difficult to distinguish the point cloud data corresponding to the reference surface and the oil return line points on the deformed inner ring of the oil seal by the axis distance. Based on this, the deformation of the position of the point cloud data on the inner ring of the oil seal is analyzed, and the deformation characterization value is calculated. The step flow chart of the method for obtaining the deformation characterization value of each sector block provided in the embodiment of the present application is shown as follows: Figure 2 As shown, specifically including:

[0057] Construct a three-dimensional coordinate system with the center of the bottom of the cylinder as the origin and the axis of the cylinder as the z-axis;

[0058] Map all the point cloud data on the cylinder to the xOy plane of the three-dimensional coordinate system, and calculate the polar coordinates of each point cloud data;

[0059] Divide the bottom surface of the cylinder into multiple sector blocks, and divide all the point cloud data into different sector blocks according to the sector blocks in which the polar angles in the polar coordinates of each point cloud data are distributed;

[0060] In this embodiment, the circumference of the bottom surface of the cylinder is 360°, which is divided into 24 intervals, that is, each interval is 15° as an interval, and each interval corresponds to a sector block, so the bottom surface of the cylinder is divided into 24 sector blocks. The sector blocks corresponding to the intervals where the polar angles in the polar coordinates of all the point cloud data on the cylinder are located are used to divide all the point cloud data into different sector blocks. As other implementation manners, the implementer can set it by himself according to the actual situation.

[0061] It should be noted that for the convenience of understanding, assuming that the bottom surface of the cylinder is divided into 8 sector blocks as an example, the division schematic diagram of the sector blocks corresponding to the inner oil seal ring in the cylinder provided in this embodiment is as Figure 3 shown; the schematic diagram of the point cloud data projected onto the xOy plane provided in this embodiment is as Figure 4 shown, where 1 represents sector block 1, 2 is sector block 2, A is the point where the point cloud data is projected onto the xOy plane. If the polar angle in the polar coordinates of the Figure 3 th point cloud data is 10°, then the polar angle of this point cloud data is distributed in the corresponding sector block 2, then the

[0062] th point cloud data belongs to sector block 2.

[0063] Calculate the dispersion degree of the axis distances of all the point cloud data in each sector block;

[0064] Detect the mutation points of the dispersion degrees in all the sector blocks to obtain the mutation probabilities of each sector block;

[0065] In this embodiment, after arranging the dispersion degrees in all the sector blocks in ascending order according to the polar angle ranges of the corresponding intervals, mutation detection is performed through the Bayesian change point detection algorithm. Among them, the Bayesian change point detection algorithm is a well-known technology and will not be elaborated here.

[0066] Calculate the ratio of the dispersion degree of each sector block to the mean value of the dispersion degrees of all the sector blocks, which is denoted as the relative dispersion ratio;

[0067] The normalized result of the product of the relative dispersion ratio and the mutation probability is used as the deformation representation value of each sector block;

[0068] In this embodiment, the Taking the deformation representation value of a sector block as an example, the calculation formula is:

[0069]

[0070] in, For the The deformation representation value of a fan-shaped block, For the The degree of discreteness of the fan-shaped blocks, is the mean value of the discrete degree of all fan-shaped blocks, For the The mutation probability of a sector block, It is a normalization function. In this embodiment, the sigmoid function is used for normalization. The sigmoid function is a well-known technology and will not be described here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the tanh function, etc. This embodiment does not impose any special restrictions on this.

[0071] It should be noted that, the larger the relative discrete ratio is, the greater the unevenness of the point cloud data of the inner ring of the oil seal in the sector block compared to the overall unevenness when there is a distance difference between the reference plane and the oil return line itself, the greater the mutation probability is, the more abrupt the unevenness difference of the inner ring of the oil seal in the sector block is, and the larger the obtained deformation characterization value is, the more likely the inner ring of the oil seal in the sector block is the location area where the oil seal is deformed due to factors such as uneven clamping force of the fixture.

[0072] At this point, the deformation representation value of each sector block is obtained.

[0073] Step 3: Analyze the distribution density of all point cloud data in the neighborhood of each point cloud data to determine the local concentration of each point cloud data; combine the axis distance and deformation representation value to determine the discrimination coefficient of each point cloud data, screen the point cloud data, and obtain the point cloud data corresponding to the oil return line.

[0074] The datum surface of the oil seal inner ring is the remaining part of the oil seal inner ring surface except for the raised oil return line. The distance between the point cloud data in the neighborhood is small, the coordinate values are similar, and the local distribution concentration is high. The oil return line is usually distributed in a regular spiral shape with rich texture details. When subjected to uneven clamping force of the pneumatic fixture or radial runout of the scanner probe, the three-dimensional shape characteristics of the oil return line point cloud are more easily destroyed, and the distribution concentration of the point cloud data in the neighborhood is low. Therefore, the distribution concentration of each point cloud data in the neighborhood is analyzed and the local concentration is calculated. Specifically,

[0075] Taking each point cloud data as the center, the neighborhood range with a preset radius is denoted as the local neighborhood;

[0076] In this embodiment, the preset radius is taken as 0.1 mm. As other implementation manners, the implementer can set it by himself according to the actual situation.

[0077] Calculate the range of the position coordinates corresponding to all the point cloud data in the local neighborhood on the x-axis, y-axis, and z-axis respectively;

[0078] For each point cloud data, calculate the average value of the ranges corresponding to all the axes in the local neighborhood; the ratio between the average value and the diameter of the local neighborhood is used as the local concentration degree of each point cloud data.

[0079] It should be noted that for the convenience of understanding, taking three point cloud data as an example, assume that there are three point cloud data in the local neighborhood, which are respectively denoted as A, B, and C, and their position coordinates are A , B , C . Among them, the maximum values of each axis in the position coordinates of the three point cloud data A, B, and C are respectively , , , and the minimum values are respectively , , . Therefore, the range of the x-axis is ; the range of the y-axis is ; the range of the z-axis is ; secondly, since the preset radius is 0.1, the diameter of the local neighborhood is 0.2.

[0080] It should be noted that the smaller the local concentration degree, the higher the distribution concentration of the point cloud data in the local neighborhood, and the more likely it belongs to the point cloud data at the reference plane in the inner ring of the oil seal.

[0081] Furthermore, for the point cloud data in the fan-shaped block area of the inner ring of the oil seal with a large deformation degree, the point cloud data is distinguished by the local concentration degree to avoid the situation where the distance difference between the oil return line and the point cloud data on the reference plane is larger; for the point cloud data in the fan-shaped block area of the inner ring of the oil seal with a small deformation degree, the distance difference between the point cloud data on the reference plane and the oil return line is significant, and the point cloud data is distinguished by the difference feature of the axis distance. Therefore, based on the axis distance and the local concentration degree, combined with the deformation characterization value, a discrimination coefficient is determined to distinguish the possibility that the point cloud data is the corresponding point cloud data on the reference plane or the oil return line, specifically:

[0082] Using the deformation characterization value of each sector block to which the point cloud data belongs as a weight, a weighted sum of the reciprocal of the local concentration and the reciprocal of the axis distance is calculated as the discrimination coefficient of each point cloud data;

[0083] In this embodiment, taking the discrimination coefficient of the th point cloud data as an example, its calculation formula is:

[0084]

[0085] where, is the discrimination coefficient of the th point cloud data, is the deformation characterization value of the nth sector block to which the th point cloud data belongs, is the axis distance of the th point cloud data, is the local concentration of the th point cloud data.

[0086] It should be noted that since the deformation characterization value has been normalized by the sigmoid function as described above, the value range of the deformation characterization value is .

[0087] It should be noted that when the deformation characterization value is larger, it indicates that there is a deformation phenomenon in the position area of the sector block to which each point cloud data belongs. At this time, the local concentration is used for judgment. The larger the reciprocal of the local concentration, the more likely the point cloud data is distributed on the oil return line of the inner ring of the oil seal; when the deformation characterization value is smaller, it indicates that there is no deformation in the position area of the sector block to which each point cloud data belongs. At this time, the axis distance is used for judgment. The larger the reciprocal of the axis distance, the closer the point cloud data is to the cylindrical axis, and the more likely the point cloud data is distributed on the oil return line of the inner ring of the oil seal, and the larger the discrimination coefficient is.

[0088] Further, based on the discrimination coefficient, all point cloud data are screened to obtain the point cloud data corresponding to the oil return line, specifically:

[0089] Using a threshold segmentation algorithm, the segmentation threshold of the discrimination coefficient of all point cloud data is obtained;

[0090] The point cloud data with the discrimination coefficient greater than the segmentation threshold is recorded as the point cloud data corresponding to the oil return line;

[0091] In this embodiment, the Otsu threshold segmentation algorithm is used to obtain the segmentation threshold. Among them, the Otsu threshold segmentation algorithm is a well-known technology and will not be elaborated here. As other implementation manners, implementers can use other methods of existing technologies, such as the cross-validation method, etc. This embodiment does not make special restrictions on this.

[0092] At this point, the point cloud data corresponding to the oil return line is obtained.

[0093] Step 4: Cluster all point cloud data corresponding to the oil return line, analyze the deviation between each point cloud data corresponding to the oil return line in each cluster and the rest of the point cloud data in its neighborhood, and the distribution of the point cloud data in its neighborhood, and determine the adjustment coefficient of each point cloud data corresponding to the oil return line in combination with the deformation representation value; based on the adjustment coefficient, determine the filtering scale of each point cloud data corresponding to the oil return line, filter it with the filtering algorithm, perform three-dimensional reconstruction on all filtered point cloud data, and measure the oil return line height of the oil seal.

[0094] Furthermore, the oil return lines on the inner ring surface of the oil seal are distributed in a regular spiral shape. By clustering all the point cloud data corresponding to the oil return lines, the point cloud data on the oil return lines belonging to the same circle are divided into one category, specifically:

[0095] Cluster all point cloud data corresponding to the oil return line to obtain multiple clusters and the local density of each point cloud data corresponding to the oil return line, where the cutoff distance of the clustering algorithm is a preset value;

[0096] In this embodiment, a density peak clustering algorithm (DPC) is used for clustering. The DPC clustering algorithm is a well-known technology and will not be described in detail here. To prevent point cloud data on similar oil return lines from being segmented into the same cluster, the cutoff distance of the DPC clustering algorithm is set to half of the oil return line spacing. The oil return line spacing refers to the spacing between two corresponding spiral lines, which is obtained based on the oil seal production specification data. In this embodiment, the oil return line spacing is set to 2 mm. As other implementation methods, the implementer can set it according to actual conditions.

[0097] Secondly, in the process of collecting point cloud data on the inner ring of the oil seal, due to the interference of factors such as light, the oil return line structure of the inner ring of the oil seal will be incomplete, the point cloud data corresponding to the oil return line will be intermittent, and outlier noise will exist. Therefore, it is necessary to filter the point cloud data corresponding to the oil return line. While removing the noise, repair the missing point cloud and enhance the quality of the point cloud, retain the spiral texture and edge structure of the oil return line as much as possible, so that the distribution of the point cloud data corresponding to the oil return line on the inner ring of the oil seal has certain symmetrical characteristics and is evenly distributed on the oil return line.

[0098] Based on the above analysis, it is necessary to analyze the distribution of the point cloud data corresponding to the oil return lines within the clusters and calculate the center deviation to evaluate the distribution uniformity of the point cloud data corresponding to the oil return lines within the clusters. Specifically:

[0099] Select the mean of the position coordinates of all the remaining point cloud data corresponding to the oil return line within the same clustering cluster as the arbitrary point cloud data corresponding to the oil return line in the neighborhood corresponding to the truncation distance, and denote it as the neighborhood center coordinate;

[0100] In this embodiment, with the arbitrary point cloud data corresponding to the oil return line as the center, calculate the Euclidean distance between the remaining point cloud data corresponding to the oil return line in each clustering cluster and the arbitrary point cloud data, and select all the remaining point cloud data corresponding to the oil return line whose Euclidean distance from the arbitrary point cloud data corresponding to the oil return line is less than the truncation distance; Therefore, calculate the mean of the position coordinates of all the remaining point cloud data corresponding to the oil return line. For the convenience of understanding, assume that there are three remaining point cloud data corresponding to the oil return line, denoted as A, B, and C respectively, and their position coordinates are A , B , C , then the neighborhood center coordinate is .

[0101] Calculate the distance between the position coordinate of the arbitrary point cloud data corresponding to the oil return line and the neighborhood center coordinate, and denote it as the center deviation degree;

[0102] In this embodiment, calculate the Euclidean distance between the position coordinate of the arbitrary point cloud data corresponding to the oil return line and the neighborhood center coordinate, and denote it as the center deviation degree.

[0103] It should be noted that the greater the center deviation degree, the more significant the non-uniformity of the distribution of the remaining point cloud data in the local neighborhood of the point cloud data corresponding to the oil return line.

[0104] Secondly, when filtering the point cloud data corresponding to the oil return line, the point cloud data corresponding to the oil return line has significant directional texture features. Directly using the K-nearest neighbor mean filter may lose the directional texture features of the oil return line and the edge details of the threaded oil return line. Therefore, it is necessary to set different neighborhood scales according to the distribution density of different point cloud data corresponding to the oil return line and the deformation situation of the positions of different point cloud data corresponding to the oil return line, so as to select an appropriate neighborhood scale for filtering, avoiding the blurring of the directional texture of the oil return line caused by too large a neighborhood scale and the non-uniform distribution of the corresponding point cloud data on the oil return line and poor filtering effect of noise points caused by too small a neighborhood scale.

[0105] Therefore, based on the local density and the center deviation degree, combined with the deformation characterization value, determine the adjustment coefficient, specifically:

[0106] Calculate the product value between the local density of each point cloud data corresponding to the oil return line and the deformation characterization value of the sector block to which it belongs, and use the normalized result of the ratio of the product value to the center deviation as the adjustment coefficient of each point cloud data corresponding to the oil return line;

[0107] In this embodiment, the sigmoid function is used for normalization processing. Among them, the sigmoid function is a well-known technology and will not be elaborated here. As other implementation manners, implementers can adopt other methods of the existing technology. For example, the tanh function, etc. This embodiment does not make special restrictions on this; secondly, when calculating the ratio, to avoid the denominator being 0, a preset value greater than 0 is added to the denominator. Among them, the preset value greater than 0 is 0.1. As other implementation manners, implementers can set it according to the actual situation.

[0108] It should be noted that the greater the local density, the more concentrated the point cloud distribution in the neighborhood of the point cloud data. At this time, a larger filtering neighborhood scale should be used to expand more neighborhood points and enhance the filtering effect. The greater the deformation characterization value, the greater the filtering neighborhood scale is given, which can smooth the deformation area of the inner ring of the oil seal and improve the accuracy of subsequent height detection; the center deviation reflects the symmetry characteristics of all point cloud data corresponding to the oil return line within the neighborhood range. The greater the center deviation, the more uneven the distribution of the point cloud data corresponding to the oil return line in the neighborhood. The filtering neighborhood scale should be reduced to retain the direction texture features and the edge details of the threaded oil return line of the point cloud data corresponding to the oil return line.

[0109] Furthermore, based on the adjustment coefficient, determine the filtering scale of the K-nearest neighbor mean filtering algorithm, specifically:

[0110] The calculation formula for the filtering scale of each point cloud data corresponding to the oil return line is:

[0111]

[0112] Among them, is the filtering scale of the th point cloud data corresponding to the oil return line, is a preset value, is the preset initial filtering scale, is the th point cloud data corresponding to the oil return line.

[0113] In this embodiment, the preset initial filtering scale takes the value of 0.1, and the preset value takes the value of 0.5. Among them, the preset value is to balance the filtering intensity and the dynamic adjustment ability. The value range of the adjustment coefficient is , at When it is 0, the filtering scale is the smallest, with a value of 0.05. At When it is 1, the filtering scale is the largest, with a value of 0.15. By presetting a value , the filtering scale of the point cloud data corresponding to the oil return line is adjusted left and right based on the preset initial filtering scale. As other implementation manners, the implementer can set it according to the actual situation by himself.

[0114] It should be noted that for the point cloud data other than the point cloud data corresponding to the oil return line, its filtering scale is set to the preset initial filtering scale;

[0115] Taking each point cloud data of the inner ring of the oil seal as the center, a neighborhood with a radius of the filtering scale is constructed, and the K-nearest neighbor mean filtering algorithm is used to filter all the point cloud data;

[0116] It should be noted that the K-nearest neighbor mean filtering algorithm is a well-known technology and will not be elaborated here.

[0117] Perform three-dimensional reconstruction on all the filtered point cloud data to construct a three-dimensional cylinder of the inner ring of the oil seal;

[0118] In this embodiment, a three-dimensional cylinder of the inner ring of the oil seal is constructed through the Poisson Reconstruction algorithm. Among them, the Poisson Reconstruction algorithm is a well-known technology and will not be elaborated here.

[0119] Obtain the radius of the inner ring of the oil seal;

[0120] It should be noted that the radius of the inner ring of the oil seal is obtained through the oil seal production specification materials.

[0121] Calculate the minimum distance from each point cloud data corresponding to the oil return line on the three-dimensional cylinder to the axis of the three-dimensional cylinder, and take the average value of the difference between the radius of the inner ring of the oil seal and the minimum distance of all the point cloud data corresponding to the oil return line as the oil return line height of the oil seal;

[0122] Select the maximum value and the minimum value of the difference between the radius of the inner ring of the oil seal and the minimum distance of all the point cloud data corresponding to the oil return line as the maximum oil return line height and the minimum oil return line height of the oil seal respectively; and display the measurement result of the oil return line height of the oil seal on the terminal.

[0123] It should be understood that although Figure 1 the steps in the flowchart of Figure 1At least some of the steps may include multiple sub-steps or multiple stages, and these sub-steps or stages do not necessarily need to be completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages does not necessarily need to be sequential, but can be executed alternately or in turns with at least some of the other steps or sub-steps or stages of the other steps.

[0124] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0125] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation to the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made. Therefore, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application all belong to the protection scope of the technical solution of the present application.

Claims

1. A method for detecting the height of the oil return line of an oil seal, characterized in that, The method includes the following steps: Collect all the point cloud data on the inner ring of the oil seal; Perform three-dimensional fitting on all the point cloud data to construct a cylinder corresponding to the oil seal. Divide the bottom surface of the cylinder into multiple sector blocks, and divide each point cloud data into each sector block according to the distribution of the polar angle in the polar coordinates of each point cloud data in each sector block; Calculate the shortest distance from each point cloud data to the cylinder axis; take the ratio between the shortest distance and the radius of the bottom surface of the cylinder as the axis distance of each point cloud data; calculate the deformation characterization value of each sector block through the dispersion condition and mutation characteristics of the axis distances of the point cloud data between each sector block and the other sector blocks; Analyze the distribution density of all the point cloud data in the neighborhood of each point cloud data to determine the local concentration degree of each point cloud data; combine the axis distance and the deformation characterization value to determine the discrimination coefficient of each point cloud data, screen the point cloud data, and obtain the point cloud data corresponding to the oil return line; Cluster all the point cloud data corresponding to the oil return line, analyze the deviation between each point cloud data corresponding to the oil return line in each clustering cluster and the other point cloud data in its neighborhood, and the distribution of the point cloud data in its neighborhood. Combine the deformation characterization value to determine the adjustment coefficient of each point cloud data corresponding to the oil return line; Based on the adjustment coefficient, determine the filtering scale of each point cloud data corresponding to the oil return line, perform filtering in combination with the filtering algorithm, perform three-dimensional reconstruction on all the filtered point cloud data, and measure the height of the oil return line of the oil seal.

2. The height detection method of the oil seal oil return line according to claim 1, characterized in that The step of dividing each point cloud data into each sector block includes: Taking the center of the bottom surface of the cylinder as the origin and the cylinder axis as the z-axis, construct a three-dimensional coordinate system; map all the point cloud data on the cylinder to the xOy plane of the three-dimensional coordinate system, and calculate the polar coordinates of each point cloud data; According to the sector block area where the polar angle in the polar coordinates of each point cloud data is distributed, divide all the point cloud data into different sector blocks.

3. The method for detecting the height of the oil return line of an oil seal according to claim 1, wherein, The step of calculating the deformation characterization value of each sector block includes: Calculate the dispersion degree of the axis distances of all the point cloud data in each sector block; calculate the ratio between the dispersion degree of each sector block and the average value of the dispersion degrees of all the sector blocks, and denote it as the relative dispersion ratio; Perform mutation point detection on the dispersion degrees of all the sector blocks to obtain the mutation probability of each sector block; The deformation characterization value is the normalized result of the product of the relative dispersion ratio and the mutation probability.

4. The height detection method of an oil seal oil return line according to claim 1, wherein The step of determining the local concentration degree of each point cloud data includes: Taking each point cloud data as the center, denote the neighborhood range with a preset radius as the local neighborhood; Calculate the range of the position coordinates corresponding to all the point cloud data in the local neighborhood on the x-axis, y-axis, and z-axis respectively; calculate the average value of the ranges corresponding to all the axes in the local neighborhood; The local concentration degree is the ratio between the average value and the diameter of the local neighborhood.

5. The height detection method of an oil seal oil return line according to claim 1, wherein The step of determining the discrimination coefficient of each point cloud data includes: taking the deformation characterization value of the sector block to which each point cloud data belongs as the weight, performing weighted summation on the reciprocal of the local concentration degree and the reciprocal of the axis distance, and taking it as the discrimination coefficient of each point cloud data.

6. The height detection method of the oil seal oil return line according to claim 1, wherein The obtaining of the point cloud data corresponding to the oil return line includes: obtaining the segmentation threshold of the discrimination coefficient of all the point cloud data; and designating the point cloud data with the discrimination coefficient greater than the segmentation threshold as the point cloud data corresponding to the oil return line.

7. The height detection method of an oil seal oil return line according to claim 1, characterized in that The determining of the adjustment coefficient of each point cloud data corresponding to the oil return line includes: By clustering all the point cloud data corresponding to the oil return line, obtaining the local density of each point cloud data corresponding to the oil return line, wherein the truncation distance of the clustering algorithm is a preset value; From the neighborhood corresponding to the truncation distance of any point cloud data corresponding to the oil return line, selecting the mean value of the position coordinates of all the remaining point cloud data corresponding to the oil return line within the same clustering cluster as the said any point cloud data, and designating it as the neighborhood center coordinate; Calculating the distance between the position coordinate of the said any point cloud data and the neighborhood center coordinate, and designating it as the center deviation; Calculating the product value between the local density of each point cloud data corresponding to the oil return line and the deformation characterization value of the sector block to which it belongs; The adjustment coefficient is the normalized result of the ratio of the product value to the center deviation.

8. The height detection method of an oil seal oil return line according to claim 1, characterized in that The filtering scale of the point cloud data corresponding to the oil return line is calculated as follows: , where is a preset value, is the preset initial filtering scale, is the adjustment coefficient of the point cloud data corresponding to the oil return line.

9. The height detection method of an oil seal oil return line according to claim 1, characterized in that The measuring of the oil return line height of the oil seal includes: according to the three-dimensional cylinder after three-dimensional reconstruction, calculating the minimum distance from each point cloud data corresponding to the oil return line on the three-dimensional cylinder to the axis of the three-dimensional cylinder, and taking the mean value of the difference between the radius of the inner circle of the oil seal and the said minimum distance of all the point cloud data corresponding to the oil return line as the oil return line height of the oil seal.

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