Method and system for detecting defects of inner wall of small-diameter pipeline

Abstract

The invention discloses a method and system for detecting inner wall defects of a small-diameter pipeline, and the method comprises the steps: carrying out the full-circle scanning of the inner wall of a to-be-detected pipeline, and obtaining a full-circle inner wall point cloud data set; performing segmented optimization on the point cloud data of the to-be-detected pipeline according to the full-circumference inner wall point cloud data set; splicing the optimized point cloud data of each section of pipeline to obtain a full-circumference pipeline inner wall three-dimensional model; according to the full-circumference pipeline inner wall three-dimensional model, the maximum opening defect on the surface of the inner wall is recognized, and the position and size of the maximum opening defect are obtained. According to the invention, small-caliber inner wall defect identification, positioning and millimeter-level dimension detection are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of inner wall detection of small-diameter cylindrical structures, and in particular to a method and a system for detecting inner wall defects of small-diameter pipelines. Background Art

[0002] The storage and transportation facilities of oil and gas, such as pipelines and tank bottoms, are usually made of ferromagnetic materials with high strength and good toughness. However, the working conditions of these facilities are very harsh, and most of them have been buried in the soil for a long time and suffered from corrosion damage. Corrosion slowly evolves into defects, which may appear on the inside and outside of the ferromagnetic material, that is, inner and outer wall defects. Inner wall defects include bulges, scratches, cracks, etc. The size of the inner wall defects of the workpiece directly determines the normal realization of its function and service life. At present, production-oriented enterprises often inspect the inner wall of the cylindrical type to be tested by experienced employees. Although there are relevant guiding standard documents that specify the methods and environment for defect detection, manual visual inspection still faces problems such as strong subjectivity, poor repeatability, low efficiency, inability to accurately quantify, and low reliability. It is often difficult to form a unified set of standards for the detection standards. Due to the small space inside the cylinder, large-scale detection equipment such as panoramic cameras cannot be placed inside the cylinder. The existing methods for detecting the inner surface of equipment mainly include: radiographic detection, ultrasonic detection, etc. These methods require multiple transmission and reception of signals on the outer surface of the device to complete the internal surface defect detection of the device, and have the characteristics of low detection efficiency and invisibility.

[0003] At present, the method for detecting surface defects at the micron level uses a high-resolution displacement sensor head to perform discrete or continuous measurements on the surface to be inspected to obtain spatial position coordinate data, and then detects defects based on the coordinate data. For example, a three-coordinate measuring instrument relies on the spherical or needle-shaped contacts on the top of the probe to contact the surface of the part, and obtains the detection results through the data of changes in the motion trajectory. This method has high detection accuracy and is not affected by factors such as external light intensity and the color of the inner wall surface to be tested. However, it has high requirements on the detection environment, slow detection speed, and causes secondary damage to the inner wall to be tested.

[0004] Non-contact detection mainly uses non-contact sensors to scan the surface to be tested, such as endoscopic imaging, microscopic imaging, interference, structured light, fringe reflection, etc. The endoscopic imaging method benefits from the large viewing angle and innovative optical design of the industrial endoscope, which is designed for photographing the bottom of the inner hole and its vertical inner wall. It can be used to photograph inner walls of various diameters to be tested, and can be used to detect foreign objects on the inner wall, such as rust, black spots and other defects with color characteristics. For defects with small heights and no obvious color characteristics, it does not have certain recognition capabilities due to the lens imaging principle.

[0005] The prior art (CM112834606B) proposes a method based on focusing leakage magnetic composite detection to realize inner and outer wall defect identification, wherein the magnetic focusing iron yoke is in an eccentric position and an air gap is left between the magnetic focusing iron yoke and the tested sample to realize magnetic focusing detection, and the magnetic field measurement sensor on the inner and outer wall defect identification device based on focusing leakage magnetic composite detection obtains the tangential component and normal component of the leakage magnetic field at the optimal leakage magnetic composite detection position point, wherein the tangential direction is the magnetization direction of the surface of the tested sample, and the normal direction is the direction perpendicular to the surface of the tested sample; and the tested sample is judged whether there is a defect according to the tangential component and the normal component. However, this method is not suitable for non-magnetic tested materials, and the defect detection device proposed by this method is only suitable for fixed detection of the tested object, which is not conducive to defect identification and detection of the tested tube bundle at the production site.

[0006] The prior art (CM115541697B) proposes a solution that uses a magnetic flux leakage detection probe to contact the pipeline through a magnetizing mechanism to form a magnetic field inside the pipeline, and then observes the magnetic field inside the pipeline through a detection mechanism. If magnetic flux leakage occurs, it proves that the pipe wall at the magnetic flux leakage location is abnormal. However, this method is not suitable for non-magnetic materials to be tested, and this method cannot accurately describe the morphology and size of defects on the inner wall of the pipeline.

[0007] The prior art (CM114113145B) also proposes a solution, which builds a visual environment based on the size of the inner wall to be measured, places a stripe light source at the bottom of the inner wall to be measured, and uses a CCD camera to obtain the inner wall imaging of the stripe light source in the inner wall from the other end. When there is a defect in the inner wall, the defect image at the corresponding position is deflected; by designing the stripe shape, mechanical structure and CCD camera image processing method, the deflected defect image is used to realize online detection of micron-level defects on small-aperture inner walls based on the ray tracing method.

[0008] In addition, Taylor Hobson in the UK has developed the LuphoScan detection system based on multi-wavelength interference technology. The system uses a multi-wavelength probe for spiral scanning and analyzes defect detection through scanning data. However, the system structure requires advance planning of the scanning trajectory during detection, which is complicated to operate and cannot achieve online rapid detection in a small space. In addition, the holographic interference detection method uses holographic photography to obtain the interference fringe pattern generated by the mutual interference of the light wave fronts before and after the deformation of the object, which is an interference measurement method for analyzing the deformation of the object. The detection accuracy can reach 1 / 100 wavelength, but this method has poor versatility, strict requirements on working conditions and environment, and is expensive. For the interference method, its detection accuracy is high, but the single field of view is small, similar to single-point detection, the overall beat is low and limited by space. The structured light method uses a specific projector to project light onto the inner wall, and realizes the defect detection of the inner wall of the tube by comparing the phase offset of the actual fringe in the ideal detection surface and the surface to be detected. Due to the limitations of the equipment itself, this method is suitable for large-diameter low-precision defect detection, and cannot achieve small-diameter high-precision online detection.

[0009] In summary, the prior art needs to provide a solution that can effectively solve technical problems such as the narrow inner wall space of the tube, the high demand for defect detection accuracy, and the realization of online detection in an industrial environment. Summary of the invention

[0010] The purpose of the present invention is to provide a solution that can solve one or more of the problems of small-caliber (≤4 cm) inner wall surface defects commonly found in the petrochemical and refining fields being difficult to locate and detect, quantitatively measuring the three-dimensional dimensions of the defects, having low measurement accuracy, and having low inspection efficiency.

[0011] In order to solve the above technical problems, an embodiment of the present invention provides a method for detecting defects on the inner wall of a small-diameter pipe, comprising: performing a full-circle scan of the inner wall of the pipe to be inspected to obtain a full-circle inner wall point cloud data set; based on the full-circle inner wall point cloud data set, segmented optimization of the point cloud data of the pipe to be inspected; splicing the optimized point cloud data of each section of the pipe to obtain a full-circle pipe inner wall three-dimensional model; based on the full-circle pipe inner wall three-dimensional model, identifying the maximum opening defect on the inner wall surface, and obtaining the position and size of the maximum opening defect.

[0012] Preferably, the process of performing a full-circle scan of the inner wall of the pipeline to be inspected includes: determining the optimal scanning field of view for obtaining the full-circle inner wall defect characteristics and the optimal distance between the emission source and the pipeline port, based on which, a single-line laser is used to perform a full-circle scan of the inner wall under specified conditions to obtain the full-circle inner wall point cloud data set.

[0013] Preferably, before segmentally optimizing the point cloud data of the pipeline to be inspected, the method further comprises: extracting a point cloud data set of the area of ​​interest from the full-circumference inner wall point cloud data set, so as to perform segmented optimization processing of the point cloud data using the point cloud data set of the area of ​​interest.

[0014] Preferably, in the process of segmented optimization of the point cloud data of the pipeline to be inspected, it includes: judging whether there is burr data in each section of the pipeline, and deleting the burr data if there is burr data; and / or judging whether there is contour data in each section of the pipeline, and enhancing the contour data if there is contour data.

[0015] Preferably, in the process of determining whether there is burr data in each section of the pipeline and performing corresponding processing, the following steps are included: setting a minimum point count threshold for the point cloud data of each section of the pipeline; if the minimum point count threshold is met, This indicates that the current pipe segment The point cloud data within the area is burr data and is removed, where D represents the standard diameter of the inner wall, ε represents the wall thickness, N represents the number of point cloud points within the specified position range, and M represents the minimum point count threshold; if N{[ε,D+2ε]}≤M is satisfied, it indicates that the point cloud data within the [ε,D+2ε] area in the current pipe segment is burr data and is removed.

[0016] Preferably, in the process of determining whether there is contour data for each pipe segment and performing corresponding processing, the following steps are included: setting a minimum point count threshold for the point cloud data of each pipe segment; if the minimum point count threshold is met, This indicates that the current pipe segment The point cloud data within the area is contour data, and it is enhanced, where D represents the standard diameter of the inner wall, ε represents the wall thickness, N represents the number of point cloud points within the specified position range, M represents the minimum point count threshold, m represents the coefficient, and m is an integer greater than 1; if N{[ε,D+2ε]}≥m×M is satisfied, it indicates that the point cloud data within the [ε,D+2ε] area in the current pipe segment is contour data, and it is enhanced.

[0017] Preferably, the process of carrying out the enhancement treatment includes: The point cloud data in the area is assigned a preset enhancement coefficient; the point cloud data in the area [ε, D+2ε] is assigned the preset enhancement coefficient, and the preset enhancement coefficient is preferably 2-10.

[0018] Preferably, the method further comprises marking three splicing identification points on the pipe end face of the full-circumference inner wall point cloud data set before carrying out the segmented optimization processing of the point cloud data, wherein, in the process of splicing the optimized point cloud data of each section of the pipeline to obtain the full-circumference pipeline inner wall three-dimensional model, it comprises: splicing the point cloud data of adjacent pipe sections according to the three splicing identification points at both ends of each pipe section; determining the splicing validity of the point cloud data of the current adjacent pipe sections by diagnosing whether the sum of the splicing errors of the three splicing identification points of the corresponding end faces does not exceed a preset threshold, wherein if the sum of the three splicing errors does not exceed the preset threshold, it indicates that the splicing is successful, otherwise the splicing fails and the point cloud data of the current adjacent pipe sections are re-spliced.

[0019] Preferably, in the step of identifying the maximum opening defect on the inner wall surface according to the three-dimensional model of the inner wall of the full-circumference pipeline, and obtaining the position and size of the maximum opening defect, it includes: traversing each inner wall defect and comparing the defect openings to locate the defect opening with the maximum length; recording the center coordinates of the defect with the maximum length, wherein the center coordinates are the intersection of the perpendicular bisector of the opening line and the opening plane; and recording the opening size of the defect with the maximum length.

[0020] On the other hand, an embodiment of the present invention provides a system for detecting defects on the inner wall of a small-diameter pipe, wherein the system is used to implement the method described above, wherein the system includes: a point cloud acquisition module, configured to perform a full-circumference scan of the inner wall of the pipe to be inspected to obtain a full-circumference inner wall point cloud data set; a point cloud preprocessing module, configured to perform segmented optimization of the point cloud data of the pipe to be inspected based on the full-circumference inner wall point cloud data set; a point cloud splicing module, configured to splice the optimized point cloud data of each section of the pipe to obtain a full-circumference pipe inner wall three-dimensional model; a maximum opening positioning module, configured to identify the maximum opening defect on the inner wall surface based on the full-circumference pipe inner wall three-dimensional model, and obtain the position and size of the maximum opening defect.

[0021] Preferably, the point cloud acquisition module is also configured to determine the optimal scanning field of view and the optimal distance between the emission source and the pipe port for obtaining the full-circumference inner wall defect characteristics. Based on this, a single-line laser is used to carry out a full-circumference scan of the inner wall under specified conditions to obtain the full-circumference inner wall point cloud data set.

[0022] Preferably, the point cloud preprocessing module includes: a point cloud screening unit, which is configured to extract the point cloud data set of the area of ​​interest from the full-circumference inner wall point cloud data set, so as to use the point cloud data set of the area of ​​interest to carry out segmented optimization processing of the point cloud data.

[0023] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0024] The present invention proposes a method and system for detecting defects on the inner wall of small-diameter pipes. The method and system are aimed at small-caliber equipment parts in the petrochemical and refining fields. Due to the limitation of the caliber size, conventional defect detection equipment is difficult to measure defects in the caliber, and the measurement scheme cannot realize the positioning, identification and detection of the three-dimensional morphology of the inner wall defects. The method and system realizes the identification, positioning and millimeter-level size detection of small-caliber inner wall defects. The present invention can be widely used in the positioning, identification and measurement of defect morphology of small-caliber inner walls. It has no requirements on the material of the equipment to be inspected, is suitable for all common materials of devices and equipment in the petrochemical and refining fields, and can effectively improve the work efficiency of defect detection. The beneficial effects produced are as follows:

[0025] 1. For small-caliber equipment parts in the petrochemical and refining fields, due to the limitation of the caliber size, the inner wall morphology is affected by the medium environment and light, and the radial distance of manual visual inspection is very limited, limited to within 2 to 3 cm from the opening end face. The present invention replaces the traditional manual visual method and adopts a single-line laser non-contact scanning of the inner wall to measure the inner wall morphology of the small-caliber inner wall within 20 cm from the opening end face, and the measurement range is increased by 6 to 10 times;

[0026] 2. The inspection efficiency of manual visual inspection of small-diameter inner walls is 0.5 pcs / min. The inspection efficiency of the present invention is 120 pcs / min, which is 240 times higher than that of traditional inspection, greatly improving the inspection efficiency.

[0027] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0029] Figure 1 This is a schematic diagram of the steps of a method for detecting inner wall defects of a small diameter pipe according to an embodiment of the present application.

[0030] Figure 2 This is a schematic diagram of a specific process of a method for detecting inner wall defects of a small diameter pipe according to an embodiment of the present application.

[0031] Figure 3 It is an overall schematic diagram of a small-diameter pipeline in a method for detecting inner wall defects of a small-diameter pipeline in an embodiment of the present application.

[0032] Figure 4 It is a partial schematic diagram of a small-diameter pipeline in a method for detecting inner wall defects of a small-diameter pipeline according to an embodiment of the present application.

[0033] Figure 5 A schematic diagram of the locations of the splicing identification points in the method for detecting inner wall defects of a small-diameter pipe according to an embodiment of the present application.

[0034] Figure 6 This is a schematic diagram of a method for detecting inner wall defects of a small-diameter pipeline according to an embodiment of the present application, in which the pipeline to be inspected is a tube sheet and a tube bundle.

[0035] Figure 7 This is a schematic diagram of a pipe to be inspected in a method for detecting inner wall defects of a small-diameter pipe according to an embodiment of the present application, in which the pipe to be inspected is a threaded inner surface.

[0036] Figure 8 This is a schematic diagram of a method for detecting inner wall defects of a small-diameter pipeline in an embodiment of the present application, in which the pipeline to be detected is the inner wall of a heat exchanger cylinder.

[0037] Fig. 9 This is a schematic diagram of the inner wall of a heat exchanger pipe in the method for detecting inner wall defects of a small-diameter pipe according to an embodiment of the present application, in which the pipe to be detected is the inner wall of the pipe.

[0038] Fig.10 This is a schematic diagram of the structure of a system for detecting inner wall defects of small-diameter pipes according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] The following will describe the implementation methods of the present invention in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that as long as there is no conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other, and the technical solutions formed are all within the protection scope of the present invention.

[0040] In addition, the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. Also, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in a sequence different from that here.

[0041] The terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms "one", "one" and "item" used herein are also intended to include plural numbers. It should also be understood that the terms "include" and / or "comprise" used herein specify the existence of stated features, integers, steps, operations, units and / or components, without excluding the existence or addition of one or more other features, integers, steps, operations, units, components and / or combinations thereof.

[0042] In order to solve the technical problems in the above-mentioned background technology, the embodiment of the present application proposes a method and system for detecting defects on the inner wall of a small-diameter pipe. The method and system can directly measure the inner wall of the target pipe by adjusting the measurement distance and angle, without removing the pipe to be measured from the equipment for measurement; after scanning the inner wall, all point cloud data on the inner wall surface are directly generated, and the size of the defect can be automatically obtained based on the point cloud data, without the need to additionally track the defect area image and light deflection within the inner wall range.

[0043] Embodiment 1

[0044] Figure 1 This is a schematic diagram of the steps of a method for detecting inner wall defects of a small diameter pipe according to an embodiment of the present application. Figure 2 This is a schematic diagram of a specific process of the method for detecting inner wall defects of small diameter pipes according to an embodiment of the present application. Figure 1 and Figure 2 , the specific steps and processes of the method for detecting inner wall defects of small-diameter pipes (also referred to as "inner wall defect detection method") described in an embodiment of the present invention are described.

[0045] Step S110 performs a full-circle scan of the inner wall of the pipeline to be inspected to obtain a full-circle inner wall point cloud data set. In an embodiment of the present invention, the pipeline to be inspected is a small-diameter pipeline with a diameter not exceeding 4 cm. For example, the pipeline to be inspected can be a single tube sheet, multiple tube bundles, threads, heat exchanger cylinders, heat exchanger pipes, etc.

[0046] Figure 3 Schematic diagram of a small-diameter pipeline in a method for detecting inner wall defects of a small-diameter pipeline according to an embodiment of the present application. Figure 3 As shown, D represents the inner wall diameter of the pipeline to be tested, and L represents the length of the pipeline.

[0047] In step S110, it is necessary to first determine the optimal scanning field of view for obtaining the full-circle inner wall defect characteristics and the optimal distance between the emission source and the pipe port, and then use a single-line laser to carry out a full-circle scan of the inner wall under specified conditions (i.e., the combination of the optimal scanning field of view and the optimal group distance) based on the determined optimal scanning field of view and the optimal distance between the emission source and the pipe port to obtain a full-circle inner wall point cloud data set.

[0048] Use a single-line laser to perform a full-circle scan of the inner wall of the pipe in a rotating scanning manner. In actual application, the single-line laser will be a certain distance away from the end face of the pipe mouth, and the single-line laser will also be deflected along the pipe opening. When the pipe bundle irradiates the inner wall surface of the pipe, the inner wall surface of the small-diameter pipe bundle is scanned clockwise along the axial direction of the pipe with a specific scanning field of view, thereby obtaining the corresponding three-dimensional point cloud image.

[0049] During the full-circle scanning process, first, the relative angle between the scanning field of view and the cylindrical inner surface of the pipeline to be tested is continuously adjusted to obtain three-dimensional point cloud images at different relative angles, and the optimal scanning field of view is determined by selecting a suitable three-dimensional point cloud image; then, the distance between the single-line laser emission source and the end face of the pipe mouth is continuously adjusted to obtain three-dimensional point cloud images under different distance conditions, so as to determine the optimal distance by selecting a suitable three-dimensional point cloud image; finally, according to the determined optimal scanning field of view and the optimal distance between the emission source and the pipe port, the full-circle inner wall surface of the cylinder of the pipeline to be tested is scanned, so as to obtain a full-circle inner wall point cloud data set containing the full-circle inner wall surface features of the small-diameter pipeline to be tested. In the process of obtaining the three-dimensional point cloud image at each relative angle, the scanning single-line laser light source is rotated at a specific rotation angle so that the scanning field of view is switched to a specific position on the inner surface of the cylinder of the pipeline to be tested.

[0050] Step S120 performs segmented optimization on the point cloud data of the pipeline to be inspected based on the full-circumference inner wall point cloud data set obtained in step S110.

[0051] In order to obtain effective point cloud information, before performing segmentation optimization on the point cloud data of the pipeline to be inspected, the embodiment of the present invention also includes: extracting a point cloud data set of the area of ​​interest from the full-circumference inner wall point cloud data set obtained in step S110, so as to perform segmentation optimization processing of the point cloud data using the point cloud data set of the area of ​​interest in step S120.

[0052] In one embodiment, the area of ​​interest refers to the circumferential surface within a range of 0 to 20 cm from the end face of the pipe port. This is because the point cloud data within this range is consistent with the environmental medium conditions such as corrosion and scouring of the circumferential inner wall within this range and the middle section of the inner wall of the pipe. Specifically, the area of ​​interest is the inner wall of the circumference of the area within a range of 0 to 20 cm from the pipe port.

[0053] In the process of extracting the point cloud data set of the area of ​​interest, it includes: denoting all the point cloud data sets obtained as A all , the point cloud data set of the area of ​​interest is A focused , and the point cloud data set outside the area of ​​interest is A outside; Select the area point cloud dataset A of interest from all current point cloud datasets focused ; Perform point cloud dataset operations: A outside =A all -A focused , thereby removing the point cloud data set outside the area of ​​interest from the full inner wall point cloud data set.

[0054] Next, the point cloud data of the area of ​​interest in the full-circumference inner wall point cloud data set is segmented and optimized. In the embodiment of the present invention, the standard diameter of the inner wall of the pipeline to be inspected is D, the wall thickness is ε, and the central axis of the inner and outer walls is O. Figure 4 , the defect depression depth is Δd1, where Δd1 satisfies: The height of the defect protrusion is denoted as Δd2, where Δd2 satisfies: ε≤Δd2+ε≤D+2ε.

[0055] In step S120, the pipeline to be evaluated needs to be divided into multiple pipeline sections, and then the burr data and / or contour data of each pipeline section is identified and processed accordingly to complete the segmented optimization of the point cloud data of the pipeline to be inspected.

[0056] In the process of identifying the burr data of each section of the pipeline and performing corresponding processing, it is determined whether there is burr data in each section of the pipeline, and if there is burr data, the identified burr data is deleted.

[0057] Specifically, in the process of determining whether there is burr data in each section of the pipeline and performing corresponding processing, first, a minimum point count threshold M of the point cloud data of each section of the pipeline is set. Then, it is determined whether And / or the condition N{[ε,D+2ε]}≤M (where N represents the number of point cloud points within the specified position range and M represents the minimum point count threshold). If satisfied, it means that the point cloud data within the current range is burr data, and the point cloud identified as burr data is deleted from the (concerned) point cloud data of the current segment pipeline.

[0058] In one embodiment, if Condition, it indicates that the current pipe segment The point cloud data within the area is burr data, and is removed from the point cloud data of interest in the current segment pipeline.

[0059] In another embodiment, if the condition N{[ε, D+2ε]}≤M is satisfied, it indicates that the point cloud data within the [ε, D+2ε] region in the current pipe section is burr data, and it is removed from the point cloud data of interest in the current section of the pipeline.

[0060] It should be noted that the range of the minimum point threshold is preferably 1 to 100. In the embodiment of the present invention, the minimum point threshold can be divided into multiple data segments of 1, 2-10, 11-20, 21-30, 31-40, 41-50, 51-60, 61-70, 71-80, 81-90 and 91-100. The minimum point threshold can be selected from the corresponding data segment range from the aforementioned multiple data segments according to the inner wall diameter of the pipeline to be tested.

[0061] In the process of identifying the contour data of each section of the pipeline and performing corresponding processing, it is determined whether there is contour data for each section of the pipeline, and if there is contour data, the identified contour data is enhanced.

[0062] Specifically, in the process of determining whether there is contour data for each pipe segment and performing corresponding processing, first, a minimum point count threshold M of the point cloud data of each pipe segment is set. Then, it is determined whether And / or N{[ε,D+2ε]}≥m×M condition (where m represents a coefficient and is an integer greater than 1). If satisfied, it indicates that the point cloud data within the current range is contour data, and the point cloud identified as contour data is enhanced.

[0063] The point cloud data within the corresponding area is assigned a preset enhancement coefficient n, which is preferably 2-10.

[0064] In one embodiment, if Condition, it indicates that the current pipe segment The point cloud data within the area is the contour data, and it is enhanced. The point cloud data in the area is assigned a preset enhancement factor n.

[0065] In another embodiment, if the condition N{[ε, D+2ε]}≥m×M is satisfied, it indicates that the point cloud data in the region [ε, D+2ε] in the current pipe segment is contour data, and enhancement processing is performed on it. Specifically, the point cloud data in the region [ε, D+2ε] is assigned a preset enhancement coefficient n.

[0066] After the burr data and contour data in the point cloud data of each section of the pipeline (pipe segment) are identified and processed accordingly, the process proceeds to step S130.

[0067] Step S130 splices the optimized point cloud data of each section of the pipeline obtained in step S120 to obtain a three-dimensional model of the inner wall of the entire pipeline.

[0068] Before the implementation of step S130, the present invention will also perform segment optimization processing of the point cloud data, such as Figure 5As shown in the figure, three splicing identification points are marked on the end face of the pipe mouth of the full-circumference inner wall point cloud data set, which are recorded as B1, B2, and B3. Among them, the north direction facing the end face of the pipe mouth is marked as B1, and then B1 is used as the starting point to arrange B2 and B3 points in a counterclockwise direction. In this way, by setting three splicing identification points on the end face of the pipe mouth, and then extending the three splicing identification points on the side wall of the pipe along the axial direction of the pipe, three splicing identification points located on the end face of the pipe mouth are arranged for each pipe section.

[0069] In the process of obtaining the 3D model of the inner wall of the full-circumference pipeline, firstly, the point cloud data of adjacent pipe sections are spliced ​​according to the three splicing identification points at the ends of the pipe openings at both ends of each pipe section. After scanning the target inner wall surface, the point cloud data is spliced ​​based on the positions of the three splicing identification points.

[0070] Then, the effectiveness of the splicing of each pair of splicing points in adjacent pipe sections is evaluated. When the splicing of all adjacent pipe sections is effective, it is considered that the point cloud splicing of the current pipeline to be tested is successful, thereby forming a three-dimensional model of the inner wall of the full-circumference pipeline.

[0071] In the process of evaluating the effectiveness of splicing, the effectiveness of the splicing of the point cloud data of the current adjacent pipe segments is determined by diagnosing whether the sum of the splicing errors of three pairs of splicing identification points of the opposite end faces (point B1 of the first pipe segment and point B1 on the surface connected to the first pipe segment, point B2 of the first pipe segment and point B2 on the surface connected to the first pipe segment, and point B3 of the first pipe segment and point B3 on the surface connected to the first pipe segment) does not exceed a preset threshold.

[0072] The splicing error of a single splicing identification point is recorded as Δbx, x = 1, 2, 3. Specifically, if the sum of the three splicing errors does not exceed the preset threshold, it indicates that the splicing is successful; otherwise, it indicates that the splicing fails, and the point cloud data of the current adjacent pipe segment is re-spliced. In an embodiment of the present invention, the preset threshold is preferably one percent of the wall thickness.

[0073] For example, based on the cumulative error of splicing of three splicing identification points: Δb1+Δb2+Δb3≤δ, if the cumulative error satisfies the above expression, the splicing is considered successful.

[0074] Step S140 identifies the largest opening defect on the inner wall surface based on the three-dimensional model of the inner wall of the entire circumference of the pipeline obtained in step S130, and obtains the position and size of the largest opening defect.

[0075] Since the three-dimensional model of the inner wall of the full-circumference pipeline obtained in step S140 is obtained through point cloud data cropping, optimization and splicing based on three-dimensional point cloud measurement, step S140 can directly identify the distribution pattern of each defect on the inner wall surface and the three-dimensional size of each defect based on a series of optimized point cloud data sets.

[0076] In step S140, first, each inner wall defect is traversed and the defect opening size is compared to locate the defect opening with the maximum length. Then, the center coordinates of the defect with the maximum length are recorded. The center coordinates are the intersection of the perpendicular bisector of the opening connection line and the opening plane. Finally, the opening size of the defect with the maximum length is recorded, so that the coordinates and opening size of the defect with the maximum length are output and fed back.

[0077] When locating the defect opening with the maximum length, the first step is to identify the contours of the concave defects and convex defects based on the point cloud enhanced by the edges of each defect. The second step is to select the defect closest to the end face of the pipe mouth as the first defect, and then record them as the second, ..., K defects in order from the position of the defect to the end face of the pipe mouth (where i represents the defect number). The third step is to select the two point clouds at the farthest position of the edge of the first defect (that is, the distance between these two point clouds represents the opening length of the current defect), and the distance between them is recorded as d1; the fourth step is to select the two point clouds at the farthest position of the edge of the i-th defect, and the distance between them is recorded as di; the fifth step is to record d i -d1=Δdi, if Δdi>0, the maximum length opening of the i-th defect is selected as the comparison benchmark d for the maximum length of the (i+1)-th defect i+1 -d i =Δdi; if Δd<0, the maximum length of the first defect is selected as the comparison benchmark for the maximum length of the (i+1)th defect, and so on, until the opening lengths of all defects are traversed. Step 6: After comparing the maximum lengths of all defects on the inner wall surface of interest, record the opening length of the defect with the maximum length, that is, d i-max value.

[0078] In recording the coordinates of the defect with the largest length, first, according to the recorded d i-max value, take the perpendicular bisector l of the longest line (the opening length line segment of the current defect); then, compare the perpendicular bisector l with d i-max The intersection point coordinates are defined as the coordinates of the defect (x i ,y i ). Among them, y i Indicates the distance from the end face of the pipe, x i Indicates the counterclockwise circumferential distance from the B1 splicing identification point.

[0079] When the pipeline to be tested consists of multiple tube bundles, the embodiment of the present invention not only outputs and feeds back the opening size and position coordinates of the longest defect, but also outputs and feeds back the tube bundle group number, tube bundle number, and position of the defect in the target tube bundle where the current longest defect is located.

[0080] Embodiment 2

[0081] Based on the above-mentioned embodiment 1, a single pipe bundle is used as a pipe to be tested, and its inner wall surface is tested. The specific steps involved in the inner wall defect detection method are as follows:

[0082] like Figure 3 As shown, the standard diameter of the small-diameter pipe to be tested is D = 2 to 4 cm;

[0083] S1 First, three splicing identification points are set on the end face of the pipe mouth, marked as B1, B2, and B3;

[0084] The S2 single-line laser is at a certain distance from the end face of the pipe opening, and the single-line laser is deflected along the pipe opening. The pipe bundle irradiates the inner wall surface of the pipe, and scans the inner wall surface of the small-diameter pipe bundle clockwise along the axial direction of the pipe with a specific scanning field of view, and obtains a corresponding three-dimensional point cloud image;

[0085] S3 rotates the scanning single-line laser light source at a specific rotation angle so that the scanning field of view is switched to a specific position on the inner surface of the cylinder of the object to be measured;

[0086] S4 adjusts the relative angle between the scanning field of view and the inner surface of the cylinder of the object to be measured, and executes the above steps after each adjustment to determine the optimal scanning field of view;

[0087] S5 adjusts the distance between the single-line laser emission source and the end face of the pipe port, and executes the above steps after each adjustment to determine the optimal distance between the emission source and the pipe port;

[0088] S6 scans the inner wall surface of the cylinder of the object to be tested in full circumference according to the optimal scanning field of view and the optimal distance between the emission source and the pipe port, so as to obtain a complete scan of the inner wall surface of the small-diameter pipe to be tested in full circumference;

[0089] S7 3D point cloud data clipping of inner wall surface: Select the inner wall surface area of ​​interest and perform point cloud data set operations: A outside =A all -A focused ;

[0090] Optimization of 3D point cloud data of S8 inner wall surface: According to the formula: Culling Point cloud data within the area; according to the formula: N{[ε,D+2ε]}≤M, the point cloud data within the area of ​​[ε,D+2ε] is eliminated;

[0091] S9 strengthens the contour data of the target area: according to the formula: Endow The point cloud data enhancement coefficient n within the region is assigned according to the formula: N{[ε,D+2ε]}≥m×M.

[0092] S10 performs full-circle pipe inner wall surface splicing, and performs point cloud data splicing based on the positions of the three splicing identification points. If the splicing cumulative error is: Δb1+Δb2+Δb3≤δ, the next step is executed; if the above formula is not satisfied, S8-S10 is executed repeatedly until the above inequality is satisfied;

[0093] S11 is based on the full inner wall surface of the successful splicing. Within this range, the maximum defect length d is identified i-max value;

[0094] S12 takes the perpendicular bisector l of the longest line connecting the edges of the longest defect to obtain the longest defect position;

[0095] S13 feeds back the longest value and the longest defect position of the longest defect to the user.

[0096] Embodiment 3

[0097] Based on the above-mentioned embodiment 1, multiple pipe bundles are used as pipes to be tested, and their inner wall surfaces are tested. The specific steps involved in the inner wall defect detection method are as follows:

[0098] S1 Figure 6 As shown, on the tube sheet surface, every 1 / 4Q tube bundles form a group, where Q is the total number of tube bundles on the tube sheet surface to be tested;

[0099] Three splicing identification points are set on the surface of each tube sheet of S2, which are marked as B1, B2, and B3, among which B1 is located in the north direction facing the tube sheet surface;

[0100] The S3 single-line laser is at a certain distance from the end face of the i-th (i=1-4) group of tube bundles. The single-line laser is deflected along the pipeline opening, and the tube bundle irradiates the inner wall surface of the pipeline. The inner wall surfaces of all tube bundles in the i-th group are scanned clockwise along the axial direction of the pipeline with a specific scanning field of view, and a corresponding three-dimensional point cloud image is obtained;

[0101] S4 rotates the scanning single-line laser light source at a specific rotation angle so that the scanning field of view is switched to a specific position on the inner surface of the tube bundle to be tested;

[0102] S5 adjusts the relative angle between the scanning field of view and the inner surface of the tube bundle of the object to be measured, and executes the above steps after each adjustment to determine the optimal scanning field of view;

[0103] S6 adjusts the distance between the single-line laser emission source and the end face of the pipe opening of each pipe bundle, and executes the above steps after each adjustment to determine the optimal distance between the emission source and the pipe port;

[0104] S7 scans the inner wall surface of the entire circumference of the i-th group of tube bundles according to the optimal scanning field of view and the optimal distance between the emission source and the pipeline port, so as to obtain a complete scan of the inner wall surface of the entire circumference of all tube bundles in the group area to be tested;

[0105] S8: Clipping of the 3D point cloud data of the inner wall surface of all the tube bundles in the i-th group: Select the inner wall surface area of ​​interest and perform point cloud data set calculation: ∑A outside =∑A all -∑A focused ;

[0106] The spacing between tube bundles on the S9 tube sheet surface is 2ε, and the three-dimensional point cloud data of the inner wall surface is optimized: according to the formula: Culling Point cloud data within the area; according to the formula: N{[ε,D+2ε]}≤M, the point cloud data within the area of ​​[ε,D+2ε] is eliminated;

[0107] S10 strengthens the contour data of the target area: according to the formula: Endow The point cloud data enhancement coefficient n within the region is assigned according to the formula: N{[ε,D+2ε]}≥m×M.

[0108] S11 performs full-circle splicing of the inner wall surfaces of all tube bundles within the i-th group, and performs point cloud data splicing based on the positions of the three splicing identification points within the i-th group surface range. If the splicing cumulative error: Δb1+Δb2+Δb3≤δ, the next step is executed; if the above formula is not satisfied, S8-S11 is executed repeatedly until the above inequality is satisfied;

[0109] S12 is based on the full circumferential inner wall surface of all tube bundles in the i-th group that are successfully spliced. Within this range, the maximum defect length d of the inner wall of each tube bundle is identified. i-max value;

[0110] S12 takes the perpendicular bisector l of the longest line connecting the edges of the longest defect to obtain the longest defect position;

[0111] S13 feeds back to the user the longest value of the longest defect, the bundle group number where the longest defect is located, the bundle number, and the position of the defect in the target bundle.

[0112] Embodiment 4

[0113] Based on the above-mentioned first embodiment, the internal thread is used as the pipeline to be tested, and its inner wall surface is tested.

[0114] like Figure 7 As shown, the inner wall defect detection of internal threads: the laser single line emitted by the laser is parallel to the plane of the bolt port to be tested. In actual scenarios, defects may appear at any position on the inner wall of the bolt, so the equipment needs to have the ability to detect defects at different positions. Because defects are generated randomly, it is difficult to find the state of defects with the same characteristics at different positions. Each time the angle of the laser parallel line is rotated and the distance from the bolt port. After algorithm processing and data analysis, the specific position of the internal thread where the defect is located is extracted. The specific steps are described as follows:

[0115] S1 First, three splicing identification points are set on the end face of the bolt with internal threads, marked as B1, B2, and B3;

[0116] The S2 single-line laser is at a certain distance from the end face of the pipe mouth, and the single-line laser is deflected along the pipe opening. The pipe bundle irradiates the inner thread surface, and the inner wall surface of the small-diameter pipe bundle is scanned clockwise along the axial direction of the pipe with a specific scanning field of view, and a corresponding three-dimensional point cloud image is obtained;

[0117] S3: rotating the scanning single-line laser light source at a specific rotation angle so that the scanning field of view is switched to a specific position on the inner surface of the bolt to be tested;

[0118] S4 adjusts the relative angle between the scanning field of view and the internal thread surface, and executes the above steps after each adjustment to determine the optimal scanning field of view;

[0119] S5 adjusts the distance between the single-line laser emission source and the bolt end face, and executes the above steps after each adjustment to determine the optimal distance between the emission source and the pipe port;

[0120] S6 scans the entire circumference of the inner thread surface of the bolt to be tested according to the optimal scanning field of view and the optimal distance between the emission source and the pipeline port, so as to obtain a complete scan of the entire circumference of the inner wall surface of the bolt to be tested;

[0121] S7 3D point cloud data clipping of internal thread surface: Select the internal thread surface area of ​​interest and perform point cloud data set operations: A outside =A all -A focused ;

[0122] Optimization of 3D point cloud data of S8 internal thread surface: According to the formula: Culling Point cloud data within the area; according to the formula: N{[ε,D+2ε]}≤M, the point cloud data within the area of ​​[ε,D+2ε] is eliminated;

[0123] S9 strengthens the contour data of the target area: according to the formula: Endow The point cloud data enhancement coefficient n within the region is assigned according to the formula: N{[ε,D+2ε]}≥m×M.

[0124] S10 performs full-circle thread surface splicing, and performs point cloud data splicing based on the positions of the three splicing identification points. If the splicing cumulative error: Δb1+Δb2+Δb3≤δ, the next step is executed; if the above formula is not satisfied, S8-S10 is executed repeatedly until the above inequality is satisfied;

[0125] S11 is based on the full circumference of the successfully spliced ​​thread surface. Within this range, the maximum defect length d is identified. i-max value;

[0126] S12 takes the perpendicular bisector l of the longest line connecting the edges of the longest defect to obtain the longest defect position;

[0127] S13 feeds back the longest value and the longest defect position of the longest defect to the user.

[0128] Embodiment 5

[0129] Based on the above-mentioned first embodiment, the heat exchanger cylinder is used as a pipeline to be tested, and its inner wall surface is tested.

[0130] like Figure 8 As shown, defect detection on the inner wall of the heat exchanger cylinder: In actual scenarios, defects may appear at any position on the inner wall of the heat exchanger cylinder, so the equipment needs to have the ability to detect defects at different positions. Because defects are generated randomly, it is difficult to find the state of defects with the same characteristics at different positions. Each time the angle of the laser parallel line is rotated and the distance from the port of the heat exchanger cylinder is determined. After algorithm processing and data analysis, the specific position of the inner wall of the heat exchanger cylinder where the defect is located is extracted. The specific detection steps are as follows:

[0131] S1 First, three splicing identification points are set on the end surface of the heat exchanger cylinder, marked as B1, B2, and B3;

[0132] The S2 single-line laser is at a certain distance from the end face of the tube mouth, and the single-line laser is deflected along the opening of the cylinder. The tube beam irradiates the inner wall surface of the cylinder, and scans the inner wall surface of the small-diameter cylinder clockwise along the axial direction of the cylinder with a specific scanning field of view, and obtains the corresponding three-dimensional point cloud image;

[0133] S3 rotates the scanning single-line laser light source at a specific rotation angle so that the scanning field of view is switched to a specific position on the inner surface of the cylinder to be measured;

[0134] S4 adjusts the relative angle between the scanning field of view and the inner surface of the cylinder to be measured, and executes the above steps after each adjustment to determine the optimal scanning field of view;

[0135] S5 adjusts the distance between the single-line laser emission source and the end face of the cylinder, and executes the above steps after each adjustment to determine the optimal distance between the emission source and the pipeline port;

[0136] S6, scanning the entire inner wall surface of the cylinder to be measured according to the optimal scanning field of view and the optimal distance between the emission source and the pipeline port, so as to obtain a complete scan of the entire inner wall surface of the cylinder to be measured;

[0137] S7 3D point cloud data clipping of the inner wall surface of the cylinder: select the inner wall surface area of ​​interest and perform point cloud data set operations: A outside =A all -A focused ;

[0138] Optimization of 3D point cloud data of the inner wall surface of S8 cylinder: According to the formula: Culling Point cloud data within the area; according to the formula: N{[ε,D+2ε]}≤M, the point cloud data within the area of ​​[ε,D+2ε] is eliminated;

[0139] S9 strengthens the contour data of the target area: according to the formula: Endow The point cloud data enhancement coefficient n within the region is assigned according to the formula: N{[ε,D+2ε]}≥m×M.

[0140] S10 performs full-circle cylinder inner wall surface splicing, and performs point cloud data splicing based on the positions of the three splicing identification points. If the splicing cumulative error: Δb1+Δb2+Δb3≤δ, then execute the next step; if the above formula is not satisfied, execute S8-S10 repeatedly until the above inequality is satisfied;

[0141] S11 is based on the inner wall surface of the whole cylinder that has been successfully spliced. Within this range, the maximum defect length d is identified. i-max value;

[0142] S12 takes the perpendicular bisector l of the longest line connecting the edges of the longest defect to obtain the longest defect position;

[0143] S13 feeds back the longest value and the longest defect position of the longest defect to the user.

[0144] Embodiment 6

[0145] Based on the above-mentioned first embodiment, the short pipe of the heat exchanger is used as a pipe to be tested, and its inner wall surface is tested.

[0146] like Fig. 9 As shown, defect detection on the inner wall of the short pipe of the heat exchanger: a single-line laser is parallel to the plane of the port of the small pipe to be tested. As shown in the figure, the incident end face can be extended from the inside of the cylinder and then scanned parallel to the inner end face of the small pipe, or it can be scanned parallel to the outer end face of the small pipe. In actual scenarios, defects can appear at any position on the inner wall of the small pipe, so the equipment is required to have the ability to detect defects at different positions. Because defects are generated randomly, it is difficult to find the state of defects with the same characteristics at different positions. Each time the angle of the laser parallel line is rotated and the distance from the inner and outer end faces of the pipe is changed. After algorithm processing and data analysis, the specific position of the inner wall of the small pipe where the defect is located is extracted. The specific steps are as follows:

[0147] S1 First, three splicing identification points are set on the end face of the pipe mouth, marked as B1, B2, and B3;

[0148] The S2 single-line laser is at a certain distance from the end face of the pipe opening. The single-line laser is deflected along the pipe opening, and the light beam is irradiated into the inner wall surface of the pipe. The inner wall surface of the pipe is scanned clockwise along the axial direction of the pipe with a specific scanning field of view, and a corresponding three-dimensional point cloud image is obtained;

[0149] S3 rotates the scanning single-line laser light source at a specific rotation angle so that the scanning field of view is switched to a specific position on the inner surface of the tube of the object to be tested;

[0150] S4 adjusts the relative angle between the scanning field of view and the inner surface of the pipe to be tested, and executes the above steps after each adjustment to determine the optimal scanning field of view;

[0151] S5 adjusts the distance between the single-line laser emission source and the end face of the pipe, and executes the above steps after each adjustment to determine the optimal distance between the emission source and the pipe port;

[0152] S6: Scan the entire inner wall surface of the pipe according to the optimal scanning field of view and the optimal distance between the emission source and the pipe port, so as to obtain a complete scan of the entire inner wall surface of the pipe to be tested;

[0153] S7 takes over the 3D point cloud data clipping of the inner wall surface: select the inner wall surface area of ​​interest and perform point cloud data set operations: A outside =A all -A focused ;

[0154] Optimization of 3D point cloud data of the inner wall surface of S8 nozzle: According to the formula: Culling Point cloud data within the area; according to the formula: N{[ε,D+2ε]}≤M, the point cloud data within the area of ​​[ε,D+2ε] is eliminated;

[0155] S9 strengthens the contour data of the target area: according to the formula: Endow The point cloud data enhancement coefficient n within the region is assigned according to the formula: N{[ε,D+2ε]}≥m×M.

[0156] S10 performs the splicing of the inner wall surface of the full-circle pipe, and performs point cloud data splicing based on the positions of the three splicing identification points. If the splicing cumulative error is: Δb1+Δb2+Δb3≤δ, the next step is executed; if the above formula is not satisfied, S8-S10 is executed repeatedly until the above inequality is satisfied;

[0157] S11 is based on the inner wall surface of the successfully spliced ​​full-circle pipe. Within this range, the maximum defect length d is identified. i-max value;

[0158] S12 takes the perpendicular bisector l of the longest line connecting the edges of the longest defect to obtain the longest defect position;

[0159] S13 feeds back the longest value and the longest defect position of the longest defect to the user.

[0160] Embodiment 7

[0161] Based on the inner wall defect detection method described in the above embodiments 1 to 6, the present invention also provides a system for detecting inner wall defects of small diameter pipes (also called "inner wall defect detection system"). The inner wall defect detection system is used to implement the detection and early warning method described in the above embodiments 1 to 6.

[0162] Fig.10 Schematic diagram of the structure of the system for detecting inner wall defects of small diameter pipes according to an embodiment of the present application. Fig.10 As shown, the inner wall defect detection system described in the embodiment of the present invention includes: a point cloud acquisition module 101, a point cloud preprocessing module 102, a point cloud stitching module 103 and a maximum opening positioning module 104.

[0163] The point cloud acquisition module 101 is implemented according to the method described in the above step S110, and is configured to perform a full-circle scan of the inner wall of the pipeline to be inspected to obtain a full-circle inner wall point cloud data set; the defect identification module 52 is implemented according to the method described in the above step S120, and is configured to perform segmented optimization of the point cloud data of the pipeline to be inspected based on the full-circle inner wall point cloud data set; the statistical analysis module 53 is implemented according to the method described in the above step S130, and is configured to splice the optimized point cloud data of each segment of the pipeline to obtain a full-circle pipeline inner wall three-dimensional model; the risk assessment module 54 is implemented according to the method described in the above step S140, and is configured to identify the maximum opening defect on the inner wall surface based on the full-circle pipeline inner wall three-dimensional model, and obtain the position and size of the maximum opening defect.

[0164] In one embodiment, the point cloud acquisition module 101 is also configured to determine the optimal scanning field of view and the optimal distance between the emission source and the pipe port for obtaining the full-circle inner wall defect characteristics. Based on this, a single-line laser is used to perform a full-circle scan of the inner wall under specified conditions to obtain a full-circle inner wall point cloud data set.

[0165] In one embodiment, the point cloud preprocessing module 102 includes: a point cloud screening unit 1021 and a point cloud optimization unit 1022 .

[0166] The point cloud screening unit 1021 is configured to extract the point cloud data set of the concerned area from the full circumference inner wall point cloud data set, so as to use the point cloud data set of the concerned area to carry out segmented optimization processing of the point cloud data. The point cloud optimization unit 1022 performs segmented optimization processing on the point cloud data of the concerned area in the full circumference inner wall point cloud data set.

[0167] The present invention discloses a method and system for detecting defects on the inner wall of a small-diameter pipe. The method and system are aimed at the small-caliber equipment parts in the petrochemical and refining fields. Due to the limitation of the caliber size, conventional defect detection equipment is difficult to measure defects in the caliber, and the measurement scheme cannot realize the positioning, identification and detection of the three-dimensional morphology of the inner wall defects. The method and system realize the identification, positioning and millimeter-level size detection of small-caliber inner wall defects. The present invention can be widely used in the positioning, identification and measurement of defect morphology of small-caliber inner walls. It has no requirements on the material of the equipment to be inspected, is applicable to all common materials of devices and equipment in the petrochemical and refining fields, and can effectively improve the work efficiency of defect detection. The beneficial effects produced are as follows:

[0168] 1. For small-caliber equipment parts in the petrochemical and refining fields, due to the limitation of the caliber size, the inner wall morphology is affected by the medium environment and light, and the radial distance of manual visual inspection is very limited, limited to within 2 to 3 cm from the opening end face. The present invention replaces the traditional manual visual method and adopts a single-line laser non-contact scanning of the inner wall to measure the inner wall morphology of the small-caliber inner wall within 20 cm from the opening end face, and the measurement range is increased by 6 to 10 times;

[0169] 2. The inspection efficiency of manual visual inspection of small-diameter inner walls is 0.5 pcs / min. The inspection efficiency of the present invention is 120 pcs / min, which is 240 times higher than that of traditional inspection, greatly improving the inspection efficiency.

[0170] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person familiar with the technology within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0171] In the description of the present invention, unless otherwise specified, "plurality" means two or more than two; the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0172] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0173] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should be extended to equivalent substitutions of these features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0174] The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment" or "embodiment" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0175] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0176] Although the embodiments disclosed in the present invention are as above, the contents described are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for detecting inner wall defects of a small diameter pipeline, characterized in that: include: Perform a full-circle scan of the inner wall of the pipeline to be inspected to obtain a full-circle inner wall point cloud data set; According to the full-circumference inner wall point cloud data set, segmentally optimize the point cloud data of the pipeline to be inspected; The optimized point cloud data of each section of the pipeline are spliced ​​to obtain a three-dimensional model of the inner wall of the entire pipeline; According to the three-dimensional model of the inner wall of the full-circumference pipeline, the maximum opening defect on the inner wall surface is identified, and the position and size of the maximum opening defect are obtained.

2. The method according to claim 1, characterized in that The process of scanning the inner wall of the pipeline to be inspected includes: The optimal scanning field of view and the optimal distance between the emission source and the pipe port for obtaining the full-circle inner wall defect characteristics are determined. Based on this, a single-line laser is used to perform a full-circle scan of the inner wall under specified conditions to obtain the full-circle inner wall point cloud data set.

3. The method according to claim 1 or 2, characterized in that: Before segmentally optimizing the point cloud data of the pipeline to be inspected, the method further includes: A point cloud data set of the area of ​​interest is extracted from the full-circumference inner wall point cloud data set, so as to use the point cloud data set of the area of ​​interest to carry out segmented optimization processing of the point cloud data.

4. The method according to any one of claims 1 to 3, characterized in that The process of segmentally optimizing the point cloud data of the pipeline to be inspected includes: Determine whether there is burr data in each section of the pipeline, and delete the burr data if there is burr data; and / or It is determined whether there is contour data for each section of the pipeline, and if there is contour data, the contour data is enhanced.

5. The method according to claim 4, characterized in that The process of determining whether there is burr data in each section of the pipeline and performing corresponding processing includes: Set the minimum point count threshold for each pipe segment’s point cloud data; If satisfied This indicates that the current pipe segment The point cloud data within the area is burr data and is removed, where D represents the standard diameter of the inner wall, ε represents the wall thickness, N represents the number of point cloud points within the specified position range, and M represents the minimum point count threshold; If N{[ε,D+2ε]}≤M is satisfied, it indicates that the point cloud data in the [ε,D+2ε] region in the current pipe segment is burr data and is removed.

6. The method according to claim 4, characterized in that The process of determining whether there is contour data for each section of the pipeline and performing corresponding processing includes: Set the minimum point count threshold for each pipe segment’s point cloud data; If satisfied This indicates that the current pipe segment The point cloud data within the area is the contour data, and it is enhanced, where D represents the standard diameter of the inner wall, ε represents the wall thickness, N represents the number of point cloud points within the specified position range, M represents the minimum point count threshold, m represents the coefficient, and m is an integer greater than 1; If N{[ε,D+2ε]}≥m×M is satisfied, it indicates that the point cloud data within the [ε,D+2ε] region in the current pipe segment is contour data, and it is enhanced.

7. The method according to claim 6, characterized in that The process of enhancing the processing includes: Will The point cloud data in the area is assigned a preset enhancement coefficient; The point cloud data in the region [ε, D+2ε] is assigned the preset enhancement coefficient, and the preset enhancement coefficient is preferably 2 to 10.

8. The method according to any one of claims 1 to 7, characterized in that: The method further includes marking three splicing identification points on the pipe end face of the full-circumference inner wall point cloud data set before performing segmented optimization processing of the point cloud data, wherein the process of splicing each segment of the optimized pipeline point cloud data to obtain the full-circumference pipeline inner wall three-dimensional model includes: According to the three splicing identification points at both ends of each pipe segment, the point cloud data of adjacent pipe segments are spliced; The validity of the splicing of the point cloud data of the current adjacent pipe segment is determined by diagnosing whether the sum of the splicing errors of the three splicing identification points of the opposite end faces does not exceed the preset threshold value, wherein if the sum of the three splicing errors does not exceed the preset threshold value, it indicates that the splicing is successful, otherwise the splicing fails and the point cloud data of the current adjacent pipe segment is re-spliced.

9. The method according to any one of claims 1 to 8, characterized in that: The step of identifying the maximum opening defect on the inner wall surface according to the three-dimensional model of the inner wall of the full-circumference pipeline and obtaining the position and size of the maximum opening defect includes: Traversing each inner wall defect and performing defect opening comparison, thereby locating the defect opening with the largest length; Record the center coordinates of the defect with the maximum length, where the center coordinates are the intersection of the perpendicular bisector of the opening line and the opening plane; Record the opening size of the defect with the greatest length.

10. A system for detecting defects on the inner wall of a small diameter pipe, characterized in that: The system is used to implement the method according to any one of claims 1 to 9, wherein the system comprises: A point cloud acquisition module, which is configured to perform a full-circle scan of the inner wall of the pipeline to be inspected to obtain a full-circle inner wall point cloud data set; A point cloud preprocessing module, configured to perform segmented optimization on the point cloud data of the pipeline to be inspected according to the full-circumference inner wall point cloud data set; A point cloud splicing module is configured to splice the optimized point cloud data of each section of the pipeline to obtain a three-dimensional model of the inner wall of the entire pipeline; The maximum opening positioning module is configured to identify the maximum opening defect on the inner wall surface according to the three-dimensional model of the inner wall of the full-circumference pipeline, and obtain the position and size of the maximum opening defect.

11. The system according to claim 10, characterized in that The point cloud acquisition module is also configured to determine the optimal scanning field of view and the optimal distance between the emission source and the pipe port for obtaining the full-circle inner wall defect characteristics. Based on this, a single-line laser is used to perform a full-circle scan of the inner wall under specified conditions to obtain the full-circle inner wall point cloud data set.

12. The system according to claim 10 or 11, characterized in that The point cloud preprocessing module includes: The point cloud screening unit is configured to extract the point cloud data set of the area of ​​interest from the full-circumference inner wall point cloud data set, so as to use the point cloud data set of the area of ​​interest to carry out segmented optimization processing of the point cloud data.

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