Methods and systems for quality inspection of industrial pipelines and headers in thermal power plants

Abstract

This invention belongs to the field of production quality inspection technology for thermal power plant equipment, and relates to a method and system for inspecting the production quality of industrial pipelines and headers in thermal power plants. The invention acquires on-site environmental information and raw point cloud data of the components to be inspected; processes the raw point cloud data to generate a real three-dimensional model that characterizes the actual physical form of the components; compares the real three-dimensional model with a preset design model to identify and quantify the dimensional deviations between the real three-dimensional model and the design model; separates the point cloud data of the inner and outer walls of the pipeline based on the raw point cloud data, and obtains the wall thickness distribution based on the raw point cloud data of the inner and outer walls and the on-site environmental information; and generates an inspection report to evaluate the production quality of the pipelines and headers based on the dimensional deviations and the wall thickness distribution. This improves the accuracy and efficiency of production quality inspection of industrial pipelines and headers, and provides more comprehensive and intuitive inspection results.

Description

Technical Field

[0001] This invention belongs to the field of production quality inspection technology for thermal power plant equipment, and relates to a method and system for production quality inspection of industrial pipelines and headers in thermal power plants. Background Technology

[0002] Industrial pipelines and headers in thermal power plants are core components of boiler systems, primarily responsible for the transportation, heat exchange, and distribution of high-temperature, high-pressure media. Their manufacturing precision directly affects the safe and stable operation of the power plant unit, as well as its efficiency and service life. Thermal power plant pipelines and headers operate in high-temperature, high-pressure, and high-stress environments. If the actual dimensions differ from the design dimensions, or if the wall thickness is uneven or does not meet design requirements, problems such as media leakage, localized stress concentration, and fatigue damage can easily occur. In severe cases, this can even lead to major safety accidents such as boiler explosions, causing enormous economic losses and casualties.

[0003] Currently, after the production of industrial pipelines and headers in thermal power plants, dimensional verification and wall thickness measurement are still mainly done manually, using tools such as tape measures, calipers, and ultrasonic thickness gauges. This method has many shortcomings: insufficient accuracy, unable to accurately capture subtle dimensional deviations such as pipe bending angles, pipe end concentricity, and header pipe end spacing; low efficiency of manual measurement, especially for complex headers and long-distance pipelines, resulting in long inspection cycles, high labor intensity for workers, and difficulty in achieving comprehensive and thorough inspection; wall thickness measurement is easily affected by human operation, and the inspection data is mostly recorded in text and tables, making it difficult to compare with the design model intuitively and quickly find the location and cause of dimensional deviations; and it cannot meet the higher requirements for production quality inspection accuracy brought about by the development of high-parameter and large-capacity units. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for quality inspection of industrial pipelines and headers in thermal power plants, in order to solve the technical problems that the size verification and wall thickness measurement of industrial pipelines and headers in thermal power plants are all done manually, which has the problems of low accuracy, poor efficiency, incomplete inspection and unintuitive data.

[0005] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a method for quality inspection of industrial pipelines and headers in thermal power plants, comprising the following steps: Acquire on-site environmental information and raw point cloud data of the components to be tested, including pipes and headers; The original point cloud data is processed to generate a real three-dimensional model that can characterize the actual physical shape of the component to be detected. The actual 3D model is compared with the preset design model to identify and quantify the dimensional deviations between the actual 3D model and the design model in terms of geometric dimensions. The point cloud data of the inner and outer walls of the pipeline are separated based on the original point cloud data, and the wall thickness distribution is obtained based on the original point cloud data of the inner and outer walls and the on-site environmental information. Based on the dimensional deviations and the wall thickness distribution, an inspection report is generated to evaluate the production quality of the pipes and the headers.

[0006] Furthermore, acquiring the raw point cloud data of the component to be detected includes: Determine the on-site information of the components to be inspected, including the material, specifications, structure, and stacking environment of the pipes and headers; Select the appropriate 3D laser scanning equipment according to the required detection accuracy and complete the calibration and debugging. Plan the scanning route and stations, and fix the 3D laser scanning equipment according to the planned route and stations; Based on the on-site information, the scanning parameters are set, and the focal length and angle of the 3D laser scanning equipment are adjusted for pipe wall thickness measurement. The 3D laser scanning equipment is activated to complete the point cloud data acquisition of the entire range of the pipeline and header, and to perform detailed scanning of the pipeline bends, interfaces and header openings, while recording the on-site environmental information.

[0007] Furthermore, the selection of appropriate 3D laser scanning equipment based on the required detection accuracy includes: For dimension verification and wall thickness measurement where the accuracy requirement is higher than the preset threshold, a high-precision 3D laser scanning device is selected. For pipes exceeding a preset threshold in length, select a suitable long-distance 3D laser scanning device.

[0008] Furthermore, when planning the scanning route and stations, it is necessary to ensure that the areas scanned by different stations have a preset degree of overlap; The scanning parameters include at least scanning accuracy and point cloud density; wherein... The scanning accuracy is determined according to the preset accuracy requirements for wall thickness measurement; The point cloud density is set according to the specifications of the pipes and headers, so that the number of point clouds per unit area meets the resolution requirements for dimensional measurement.

[0009] Furthermore, the processing of the original point cloud data to generate a real three-dimensional model capable of representing the actual physical form of the component to be detected includes: The original point cloud data is denoised, redundant removed, and multi-site stitched together to obtain a complete point cloud model. The inner wall point cloud data and outer wall point cloud data of the pipe are separated from the complete point cloud model. The deviation of the separated inner wall point cloud data is corrected to eliminate systematic errors caused by scanning angle or occlusion, and the corrected point cloud model is obtained. Based on the modified point cloud model, the actual 3D model of the pipe and header is constructed using a surface reconstruction algorithm.

[0010] Furthermore, the step of comparing the actual 3D model with the preset design model to identify and quantify the dimensional deviations between the actual 3D model and the design model in terms of geometric dimensions includes: Obtain the original design models of the pipes and headers; The actual 3D model is spatially aligned with the original design model so that they are in the same coordinate system. The algorithm automatically identifies and quantifies the dimensional deviations between the actual 3D model and the design model. The deviation locations and quantified dimensional deviations are marked on the actual 3D model to generate a visual deviation distribution map.

[0011] Furthermore, the dimensional deviations include the pipe diameter, bending angle, pipe end concentricity and length, as well as the deviations in the pipe end spacing and body size of the header.

[0012] Furthermore, the step of separating the point cloud data of the inner and outer walls of the pipe based on the original point cloud data, and obtaining the wall thickness distribution based on the original point cloud data of the inner and outer walls and on-site environmental information, includes: The point cloud data of the inner and outer walls of the pipe are separated based on the point cloud data. Based on the separated point cloud data of the inner and outer walls of the pipeline, the initial wall thickness of each part of the pipeline under the stated field environment information is calculated. The initial wall thickness value is corrected based on the on-site environmental information to eliminate the influence of environmental factors on the accuracy of laser measurement and obtain the actual wall thickness value of each part of the pipeline. Based on the actual wall thickness values ​​of various parts of the pipeline, a wall thickness distribution map is generated. Compare the actual wall thickness values ​​of each part of the pipeline with the designed wall thickness values ​​and wall thickness tolerance range; When the actual wall thickness exceeds the wall thickness tolerance range, the area is determined to be a non-compliant area. Mark the location and deviation of the non-compliant wall thickness area on the actual three-dimensional model or wall thickness distribution map.

[0013] Furthermore, the step of generating an inspection report for evaluating the production quality of the pipe and the header based on the dimensional deviation and the wall thickness distribution also includes: By integrating the dimensional deviation and the wall thickness distribution, an inspection report is generated that includes deviation parameters, wall thickness data, and deviation location information. The test report is associated and stored in the thermal power plant production process database, and an index relationship is established in the database between the test report and the corresponding pipeline and header production batch; In response to a query command, historical inspection reports are retrieved from the production process database to enable traceability query of dimensional deviation and wall thickness deviation data.

[0014] Secondly, the present invention provides a production quality inspection system for industrial pipelines and headers in thermal power plants, comprising: The data acquisition module is used to acquire on-site environmental information and raw point cloud data of the components to be tested, including pipes and headers. The data processing module is used to process the raw point cloud data to generate a real three-dimensional model that can characterize the actual physical shape of the component to be detected. The dimension deviation detection module is used to compare the actual 3D model with the preset design model, identify and quantify the dimension deviation between the actual 3D model and the design model in terms of geometric dimensions; The wall thickness detection module is used to separate the point cloud data of the inner wall and the outer wall of the pipeline based on the original point cloud data, and to obtain the wall thickness distribution based on the original point cloud data of the inner wall and the outer wall and the on-site environmental information. The results output module is used to generate an inspection report for evaluating the production quality of the pipe and the header based on the dimensional deviation and the wall thickness distribution.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention acquires on-site environmental information and raw point cloud data of the components to be inspected. By comprehensively collecting basic data, it provides a basis for correction in subsequent point cloud data processing. The raw point cloud data is processed to generate a real three-dimensional model that characterizes the actual physical form of the components to be inspected. Processing the raw point cloud data improves data quality and increases the efficiency of the entire inspection process. The real three-dimensional model is compared with a preset design model to identify and quantify the dimensional deviations between the two models. Precise positioning and quantification of these deviations provide an objective basis for determining whether quality standards are met. Compared to manual comparison, this method can quickly process large amounts of data, reduce human error, make inspection results more reliable, and significantly shorten the inspection cycle. Based on the raw point cloud data, the inner and outer walls of the pipeline are separated. Based on the raw point cloud data of the inner and outer walls and on-site environmental information, the wall thickness distribution is obtained, providing an important basis for evaluating pipeline quality and safety, avoiding interference from human factors, and improving the accuracy and reliability of wall thickness measurement. Based on the dimensional deviations and the wall thickness distribution, an inspection report is generated to evaluate the production quality of the pipeline and the header. This invention's test report comprehensively considers key factors such as dimensional deviations and wall thickness distribution, enabling a complete and objective evaluation of the production quality of pipes and headers. This provides a basis for manufacturers to improve processes and enhance product quality. Detailed recording of test results also provides a basis for quality traceability and management.

[0016] The system of this invention includes: a data acquisition module, a data processing module, a dimensional deviation detection module, a wall thickness detection module, and a result output module. The data acquisition module acquires on-site environmental information and raw point cloud data of the components to be inspected, including pipes and headers. The data processing module processes the raw point cloud data to generate a real three-dimensional model that characterizes the actual physical form of the components. The dimensional deviation detection module compares the real three-dimensional model with a preset design model to identify and quantify the dimensional deviations between the real three-dimensional model and the design model. The wall thickness detection module separates the point cloud data of the inner and outer walls of the pipe based on the raw point cloud data, and obtains the wall thickness distribution based on the raw point cloud data of the inner and outer walls and the on-site environmental information. The result output module generates an inspection report to evaluate the production quality of the pipes and headers based on the dimensional deviations and the wall thickness distribution. The various modules work together to improve the accuracy and efficiency of industrial pipe and header production quality inspection, resulting in more comprehensive and intuitive inspection results. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system module diagram of the present invention.

[0018] Figure 3 This is a flowchart of another embodiment of the method of the present invention; Figure 4 This is a state diagram of the component to be inspected scanned by the 3D laser scanning device in an embodiment of the present invention; Figure 5 for Figure 4 Side view.

[0019] Among them, 1. The component to be tested; 2. 3D laser scanning equipment. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1: See Figure 1 This invention discloses a method for quality inspection of industrial pipelines and headers in thermal power plants, comprising the following steps: S1, acquire on-site environmental information and raw point cloud data of the component to be tested 1, which includes pipes and headers; by comprehensively collecting basic data, it can provide a basis for correction for subsequent point cloud data processing, reduce the impact of environmental interference on measurement results, and ensure that the model can truly reflect the shape of the component.

[0023] In a preferred embodiment of the present invention, obtaining the original point cloud data of the component 1 to be detected includes: Determine the on-site information of the component 1 to be tested, including the material, specifications, structure, and stacking environment of the pipes and headers; Select the appropriate 3D laser scanning equipment 2 according to the detection accuracy requirements and complete the calibration and debugging; Plan the scanning route and stations to ensure full coverage of the scanning area, and fix the 3D laser scanning device 2 according to the planned route and stations; Based on the on-site information, the scanning parameters are set, and the focal length and angle of the 3D laser scanning device 2 are adjusted for pipe wall thickness measurement. The 3D laser scanning device 2 is activated to complete the point cloud data acquisition of the entire range of the pipeline and header, and to perform detailed scanning of the pipeline bends, joints, and header openings. Simultaneously, on-site environmental information is recorded. Figure 4 and Figure 5 Use a 3D laser scanning device to scan the state diagram of the component to be inspected.

[0024] In a preferred embodiment of the present invention, selecting the appropriate 3D laser scanning device 2 according to the detection accuracy requirements includes: For dimension verification and wall thickness measurement where the accuracy requirement is higher than the preset threshold, a high-precision 3D laser scanning device 2 is selected. For pipes exceeding a preset threshold in length, select a suitable long-distance 3D laser scanning device 2.

[0025] In a preferred embodiment of the present invention, when planning the scanning route and stations, it is necessary to ensure that the scanning areas of different stations have a preset degree of overlap in order to avoid data omissions at critical locations such as pipe bends and header openings. The scanning parameters include at least scanning accuracy and point cloud density; wherein... The scanning accuracy is determined according to the preset accuracy requirements for wall thickness measurement; The point cloud density is set according to the specifications of the pipes and headers, so that the number of point clouds per unit area meets the resolution requirements for dimensional measurement.

[0026] S2, the original point cloud data is processed to generate a realistic 3D model that represents the actual physical form of the component 1 to be inspected. Processing the original point cloud data improves data quality, reduces data volume while maintaining model accuracy, lowers the computational complexity of subsequent processing and analysis, and improves the efficiency of the entire inspection process. The realistic 3D model accurately represents the actual physical form of the component 1 to be inspected.

[0027] In a preferred embodiment of the present invention, processing the original point cloud data to generate a real three-dimensional model capable of characterizing the actual physical form of the component 1 to be detected includes: The original point cloud data is denoised, redundant removed, and multi-site stitched together to obtain a complete point cloud model. The inner wall point cloud data and outer wall point cloud data of the pipe are separated from the complete point cloud model. The deviation of the separated inner wall point cloud data is corrected to eliminate systematic errors caused by scanning angle or occlusion, and the corrected point cloud model is obtained. Based on the corrected point cloud model, the actual three-dimensional model of the pipe and header is constructed by the surface reconstruction algorithm. The actual three-dimensional model completely restores the geometry and spatial dimensions of the component 1 to be detected. S3. The actual 3D model is compared with the preset design model to identify and quantify the dimensional deviations between the two models. By accurately locating and quantifying these deviations, the differences between the actual 3D model and the design model are presented intuitively, providing an objective basis for determining whether the quality standards are met. Compared to manual comparison, computer model comparison is faster, more accurate, and can quickly process large amounts of data, reducing human error, making the test results more reliable, and significantly shortening the test cycle.

[0028] In a preferred embodiment of the present invention, comparing the actual 3D model with a preset design model to identify and quantify the dimensional deviations between the actual 3D model and the design model in terms of geometric dimensions includes: Obtain the original design models of the pipes and headers; The actual 3D model is spatially aligned with the original design model so that they are in the same coordinate system. The algorithm automatically identifies and quantifies the dimensional deviations between the actual 3D model and the design model. The deviation locations and quantified dimensional deviations are marked on the actual 3D model to generate a visual deviation distribution map.

[0029] In a preferred embodiment of the present invention, the dimensional deviations include the pipe diameter, bending angle, pipe end concentricity and length, as well as the deviations in the pipe end spacing and body size of the header.

[0030] S4. Based on the original point cloud data, the point cloud data of the inner and outer walls of the pipeline are separated. Based on the original point cloud data of the inner and outer walls and the on-site environmental information, the wall thickness distribution is obtained, resulting in a detailed wall thickness distribution. This identifies problems such as uneven wall thickness, providing an important basis for assessing pipeline quality and safety. Traditional manual wall thickness measurement is easily affected by operational factors. This invention calculates wall thickness based on point cloud data, avoiding interference from human factors and improving the accuracy and reliability of wall thickness measurement. This invention can detect wall thickness along the entire circumference of the pipeline, discovering wall thickness variations at different locations, and promptly identifying potential weak points, providing guidance for pipeline maintenance and repair.

[0031] In a preferred embodiment of the present invention, the step of separating the point cloud data of the inner and outer walls of the pipe based on the original point cloud data, and obtaining the wall thickness distribution based on the original point cloud data of the inner and outer walls and on-site environmental information, includes: The point cloud data of the inner and outer walls of the pipe are separated based on the point cloud data. Based on the separated point cloud data of the inner and outer walls of the pipeline, the initial wall thickness of each part of the pipeline under the stated field environment information is calculated. The initial wall thickness value is corrected based on the on-site environmental information to eliminate the influence of environmental factors on the accuracy of laser measurement and obtain the actual wall thickness value of each part of the pipeline. Based on the actual wall thickness values ​​of various parts of the pipeline, a wall thickness distribution map is generated. Compare the actual wall thickness values ​​of each part of the pipeline with the designed wall thickness values ​​and wall thickness tolerance range; When the actual wall thickness exceeds the wall thickness tolerance range, the area is determined to be a non-compliant area. Mark the location and deviation of the non-compliant wall thickness area on the actual three-dimensional model or wall thickness distribution map.

[0032] S5. Based on the dimensional deviations and wall thickness distribution, an inspection report is generated to evaluate the production quality of the pipes and headers. This invention's inspection report comprehensively considers key factors such as dimensional deviations and wall thickness distribution, enabling a comprehensive and objective evaluation of the pipe and header production quality, providing a basis for manufacturers to improve processes and enhance product quality. Detailed recording of inspection results provides a basis for quality traceability and management.

[0033] In a preferred embodiment of the present invention, generating an inspection report for evaluating the production quality of the pipe and the header based on the dimensional deviation and the wall thickness distribution further includes: By integrating the dimensional deviation and the wall thickness distribution, an inspection report is generated that includes deviation parameters, wall thickness data, and deviation location information. The test report is associated and stored in the thermal power plant production process database, and an index relationship is established in the database between the test report and the corresponding pipeline and header production batch; In response to a query command, historical inspection reports are retrieved from the production process database to enable traceability query of dimensional deviation and wall thickness deviation data.

[0034] This invention employs a method of acquiring raw point cloud data and constructing a 3D model for comparison. This method accurately captures minute dimensional deviations and obtains precise wall thickness distribution, far exceeding the accuracy of traditional manual inspection. It effectively ensures that the actual dimensions of pipes and headers match the design dimensions, guaranteeing that pipe wall thickness meets design requirements and satisfying the accuracy demands of high-parameter, large-capacity unit development. This invention can quickly complete full-range inspection of multiple pipes and headers, significantly shortening the inspection cycle. It is suitable for the quality control needs of large-scale production and reduces the labor intensity of workers. This invention can perform full-range scanning of pipes and headers, avoiding the limitations of traditional manual sampling inspection. It effectively prevents unqualified components from entering subsequent installation stages, reducing the safety risks of unit operation. This invention uses a non-contact inspection method, completing data acquisition without direct contact with the surface of pipes and headers. This avoids damage to high-temperature pipes and precision components after production and prevents workers from entering hazardous environments, improving the safety and stability of inspection operations. This invention enables a direct comparison between the actual model and the design model through 3D modeling, clearly showing dimensional deviations and wall thickness distribution. The detection data can be linked to the production process database for data traceability and querying, facilitating subsequent quality review and process optimization, and driving equipment production towards higher precision and digitalization. Based on the specifications, structure, and detection requirements of thermal power plant pipelines and headers, this invention allows for flexible selection of scanning equipment and parameters, suitable for pipelines and headers with different wall thicknesses, diameters, and structures. It also establishes a standardized detection process that can be directly applied to production quality control, demonstrating high practicality and promotional value.

[0035] See Figure 2 This invention discloses a production quality inspection system for industrial pipelines and headers in thermal power plants, comprising: The data acquisition module is used to acquire on-site environmental information and raw point cloud data of the component to be tested 1, which includes pipes and headers; The data processing module is used to process the raw point cloud data to generate an actual three-dimensional model that can characterize the actual physical shape of the component 1 to be detected. The dimension deviation detection module is used to compare the actual 3D model with the preset design model, identify and quantify the dimension deviation between the actual 3D model and the design model in terms of geometric dimensions; The wall thickness detection module is used to separate the point cloud data of the inner wall and the outer wall of the pipeline based on the original point cloud data, and to obtain the wall thickness distribution based on the original point cloud data of the inner wall and the outer wall and the on-site environmental information. The results output module is used to generate an inspection report for evaluating the production quality of the pipe and the header based on the dimensional deviation and the wall thickness distribution.

[0036] The various modules of this invention work together to improve the accuracy and efficiency of quality inspection of industrial pipelines and headers, and the inspection results are more comprehensive and intuitive.

[0037] Example 2: This invention discloses a production quality inspection method for industrial pipelines and headers in thermal power plants based on 3D laser scanning imaging. It primarily addresses the problem that current methods for verifying dimensions and measuring wall thickness after the production of industrial pipelines and headers in thermal power plants rely on manual labor, resulting in low accuracy, low efficiency, incomplete inspection, and unintuitive data. This invention utilizes the principles of photodetection and ranging, acquiring three-dimensional point cloud data of the produced pipelines and headers using a 3D laser scanning device. After data processing and 3D modeling, the data is precisely compared with the design model to accurately identify and quantify dimensional deviations. Simultaneously, by separating the point cloud data of the inner and outer walls of the pipeline, non-contact, high-precision measurement of the pipeline wall thickness is achieved. Finally, an inspection report is generated, providing data support for production quality rectification and process optimization. This invention offers high inspection accuracy and is significantly more efficient than traditional manual inspection. It enables comprehensive, blind-spot-free inspection, effectively ensuring that the actual dimensions of the pipelines and headers match the design dimensions, guaranteeing that the pipeline wall thickness meets requirements, reducing potential production quality risks, and is suitable for production quality control of high-parameter, large-capacity units in thermal power plants. It has high practicality and promotional value.

[0038] See Figure 1 and Figure 3 The method of the present invention includes the following steps: Step 1, Preliminary Preparation: Clearly define the inspection target as the verification of the dimensions of industrial pipelines and headers after the thermal power plant's production and the measurement of pipeline wall thickness. Inspect the production site to confirm the material, specifications, structural complexity, and stacking environment of the pipelines and headers. Identify factors affecting the scanning, select a suitable 3D laser scanning device and complete calibration and debugging, plan the scanning route and stations, and ensure full coverage of the scanning range.

[0039] The 3D laser scanning device 2 mentioned in step 1 is selected according to the detection accuracy requirements. For high-precision dimension verification and wall thickness measurement, a suitable high-precision scanner is preferred, and for long-distance pipeline scanning, a suitable long-distance scanner is selected.

[0040] When planning the scanning route and stations in step 1, it is necessary to ensure that the scanning areas of different stations have sufficient overlap to avoid missing data at critical locations such as pipe bends and header openings.

[0041] Step 2, Data Acquisition: Fix the scanner according to the planned route and stations, set appropriate scanning parameters, adjust the scanner's focal length and angle for pipe wall thickness measurement, start the scanner to complete the point cloud data acquisition of the entire range of the pipe and header, focus on detailed scanning of key parts such as pipe bends, interfaces and header openings, and record on-site environmental information at the same time.

[0042] The scanning parameters mentioned in step 2 include scanning accuracy and point cloud density. The scanning accuracy used for wall thickness measurement must meet the detection requirements, and the point cloud density is adjusted according to the specifications of the pipe and header to ensure that the required measurement accuracy can be achieved.

[0043] Step 3, Data Preprocessing: The collected raw point cloud data is denoised and redundancy removed, data errors are corrected, point cloud data from multiple stations are stitched together to form a complete point cloud model of the pipe and the header, and the point cloud data of the inner wall and outer wall of the pipe are separated and the deviation of the inner wall point cloud is corrected.

[0044] In step 3, point cloud data preprocessing is performed using specialized point cloud processing software. Noise reduction and redundancy removal are carried out using appropriate methods to ensure that the accuracy of the processed data is not reduced.

[0045] Step 4, 3D modeling and design comparison: Based on the processed point cloud data, construct the actual 3D model of the pipe and header, import the original design model of the pipe and header and align it accurately, automatically find and quantify the size deviation between the actual 3D model and the design model, and mark the deviation location.

[0046] The dimensional deviations mentioned in step 4 include deviations in pipe diameter, bending angle, pipe concentricity, pipe length, header pipe spacing, and header body dimensions, ensuring accurate quantification of deviations.

[0047] Step 5, Wall thickness measurement: Based on the point cloud data of the separated inner and outer walls of the pipe, the wall thickness values ​​of different parts of the pipe are obtained through spatial geometric calculations, a wall thickness distribution map is generated, and the parts and deviation ranges of the wall thickness that do not meet the design requirements are marked. In step 5, the measurement error of the pipe wall thickness is controlled within a reasonable range. For pipes with different wall thicknesses, the corresponding measurement accuracy standard is adopted.

[0048] Step 6, Results Analysis and Application: Combining dimensional deviation and wall thickness measurement data, analyze the production process causes of the deviations, and generate an inspection report containing deviation parameters, wall thickness data, and rectification suggestions to support production quality rectification and process optimization.

[0049] In step 6, the test report can be linked to the thermal power plant's production process database to enable traceability and query of dimensional deviation and wall thickness deviation data, providing support for the continuous optimization of subsequent production processes.

[0050] Example 3: See Figure 1 and Figure 3 This invention discloses a method for quality inspection of industrial pipelines and headers in thermal power plants based on 3D laser scanning imaging, comprising the following steps: S10, Preliminary Preparation: Clearly define the inspection objectives as the verification of dimensions and measurement of pipe wall thickness for industrial pipelines and headers after production in thermal power plants. Inspect the production site to confirm the material, specifications, structural complexity, and stacking environment of the pipelines and headers. Identify obstacles, ambient temperature, and other factors that may affect scanning accuracy, and avoid these factors affecting scanning accuracy. Based on the required inspection accuracy and actual site conditions, select a suitable 3D laser scanning device. For high-precision dimension verification and wall thickness measurement, prioritize a suitable high-precision scanner. For long-distance pipeline scanning, select a suitable long-distance scanner. Complete the calibration and debugging of the scanning equipment to ensure that the measurement accuracy of the equipment meets the inspection requirements. Plan the scanning route and stations to ensure sufficient overlap between the scanning areas of different stations, focusing on covering key areas such as pipe bends, interfaces, and header openings to ensure full coverage of the scanning range and no data omissions.

[0051] S20, Data Acquisition: Following the planned scanning route and stations, place and secure the scanner to prevent data deviation caused by equipment movement during scanning. Set appropriate scanning parameters, including scanning accuracy and point cloud density. The scanning accuracy for wall thickness measurement must meet the inspection requirements, and the point cloud density should be adjusted according to the specifications of the pipes and headers to ensure the required measurement accuracy is achieved. For pipe wall thickness measurement, adjust the scanner's focus and angle to ensure accurate capture of point cloud data from both the inner and outer walls of the pipe, avoiding missing point cloud data from the inner wall. Start the scanner to complete the point cloud data acquisition of the entire range of the pipes and headers after production. Focus on detailed scanning of key areas such as pipe bends, interfaces, and header openings to improve data accuracy in critical areas. Simultaneously record environmental information during scanning, including temperature, humidity, and wind speed, to provide a reference for subsequent data processing, wall thickness calculation, and deviation analysis.

[0052] S30, Data Preprocessing: The collected raw point cloud data is processed using specialized point cloud processing software. First, noise and redundant points are removed using appropriate methods to prevent invalid points caused by dust, debris, etc., from affecting data accuracy, while also correcting data errors. Point cloud data collected from multiple sites are stitched together to form a complete point cloud model of the pipeline and headers, ensuring that the stitched model is free of misalignment and data loss. For pipeline wall thickness measurement, the software separates the point cloud data of the inner and outer walls of the pipeline, correcting deviations in the inner wall point cloud data to ensure the accuracy of wall thickness calculation. While ensuring data accuracy, the stitched point cloud data is smoothed and simplified to reduce data volume and improve the efficiency of subsequent 3D modeling and comparative analysis.

[0053] S40, 3D Modeling and Design Comparison: Using specialized 3D modeling software, based on processed point cloud data, construct actual 3D models of pipes and headers after production. The models must completely reproduce the geometry, spatial dimensions, surface condition, and details of key parts of the pipes and headers. Import the original design models of the pipes and headers, and use the software's comparison function to accurately align the actual 3D model with the design model, ensuring that the alignment error is within a reasonable range. The software automatically identifies and quantifies the dimensional deviations between the actual model and the design model, including deviations in pipe diameter, bending angle, pipe concentricity, pipe length, header pipe spacing, and header body dimensions, while marking the location of the deviations to facilitate quick identification of the deviation areas by staff.

[0054] S50, Wall Thickness Measurement: Based on the point cloud data of the inner and outer walls of the pipeline separated in step S30, the wall thickness values ​​of different parts of the pipeline are accurately obtained through spatial geometric calculations. The influence of on-site environmental factors on the measurement results is corrected during the calculation process. A pipeline wall thickness distribution map is generated to intuitively present the uniformity of the pipeline wall thickness and the deviation of the wall thickness from the design requirements. The parts where the wall thickness does not meet the design requirements are marked, the range of wall thickness deviation is clarified, and the measurement results are ensured to meet the accuracy requirements of the pipeline wall thickness in thermal power plants.

[0055] S60, Result Analysis and Application: Combining the dimensional deviation data from step S40 and the wall thickness measurement data from step S50, analyze the production process causes of dimensional deviation and non-compliance with wall thickness standards, such as unreasonable bending process parameters, welding deformation, insufficient mold precision, and inadequate pipe processing precision; generate a complete inspection report, which includes dimensional deviation parameters, wall thickness measurement data, deviation location, non-compliance wall thickness location, deviation cause analysis, and production quality rectification suggestions; link the inspection report to the thermal power plant production process database to achieve traceability and query of dimensional deviation and wall thickness deviation data, and use the inspection data for production process optimization, adjusting relevant process parameters to gradually reduce the occurrence of dimensional deviation and uneven wall thickness problems, and improve the manufacturing precision of pipes and headers.

[0056] Compared with the prior art, the present invention has the following advantages: High inspection accuracy: Utilizing 3D laser scanning imaging technology, it can accurately capture minute dimensional deviations and uneven wall thickness of pipes and headers, far exceeding the accuracy of traditional manual inspection. This effectively ensures that the actual dimensions of pipes and headers are consistent with the design dimensions, and ensures that the pipe wall thickness meets the design requirements.

[0057] High testing efficiency: The data acquisition speed is very fast, and it can quickly complete the full range testing of multiple thermal power plant pipelines and multiple headers. It is much more efficient than traditional manual testing, greatly shortens the testing cycle, and is suitable for the quality control needs of large-scale production of thermal power plant pipelines and headers, reducing the labor intensity of staff.

[0058] Achieve comprehensive, blind-spot-free inspection: It can perform full-range scanning of pipelines and headers, avoiding the limitations of traditional manual sampling inspection, ensuring no blind spots in inspection, comprehensively identifying potential quality hazards in the production process, effectively preventing unqualified parts from entering the subsequent installation stage, and reducing the safety risks of thermal power plant unit operation.

[0059] Non-contact testing with high safety: Data collection can be completed without direct contact with the surface of pipes and headers, avoiding damage to high-temperature pipes and precision components after production caused by traditional contact measurements. At the same time, it avoids workers entering hazardous environments, improving the safety and stability of testing operations.

[0060] The data is intuitive and highly traceable: 3D modeling enables a direct comparison between the actual model and the design model, clearly showing dimensional deviations and wall thickness distribution. The test data can be linked to the production process database to achieve data traceability and query, facilitating subsequent quality review and process optimization, and promoting the development of thermal power plant equipment production towards high precision and digitalization.

[0061] Highly adaptable and easy to implement: Based on the specifications, structure and testing requirements of thermal power plant pipelines and headers, scanning equipment and parameters can be flexibly selected, suitable for pipelines and headers with different wall thicknesses, diameters and structures. At the same time, a standardized testing process has been formed, which can be directly applied to the production quality control of thermal power plant pipelines and headers, with high practicality and promotion value.

[0062] Example 4: See Figure 1 and Figure 3 This embodiment provides a production quality inspection method for industrial pipelines and headers in thermal power plants based on 3D laser scanning imaging. It is applied to the production quality inspection of high-temperature, high-pressure steam pipelines and boiler headers in a thermal power plant. The specific steps are as follows: S1. Preliminary Preparation: Clearly define the inspection objectives as the verification of the dimensions and wall thickness of the high-temperature and high-pressure steam pipelines and the boiler header dimensions after production at the thermal power plant; inspect the production site to confirm the material and specifications of the pipelines and headers, ensure there are no obvious obstacles in the stacking environment, and that the site environment is stable and free from significant interference factors; based on the required inspection accuracy, select a suitable high-precision fixed scanner for the overall scanning and wall thickness measurement of the pipelines and headers, complete the scanner calibration and debugging, and ensure that the measurement accuracy meets the requirements; plan the scanning route and stations, setting an appropriate number of stations for both pipeline and header scanning, ensuring sufficient overlap between stations, focusing on covering key areas such as pipeline bends, interfaces, and header pipe openings to ensure full coverage of the scanning range.

[0063] S2, Data Acquisition: According to the planned scanning route and stations, fix the fixed scanner on the bracket and adjust the height and angle of the scanner; set appropriate scanning parameters, and for pipe wall thickness measurement, adjust the scanner focal length and scanning angle to ensure accurate capture of point cloud data of the inner and outer walls of the pipe; start the scanner to complete the full-range point cloud data acquisition of the pipe and header, and simultaneously record on-site environmental information to provide a reference for subsequent data processing.

[0064] S3, Data Preprocessing: The raw point cloud data is processed using specialized point cloud processing software to remove noise and redundant points and correct data errors; the point cloud data of each station are stitched together to form a complete point cloud model of the pipeline and header; the point cloud data of the inner and outer walls of the pipeline are separated, and the deviation of the inner wall point cloud data is corrected; while ensuring data accuracy, the stitched point cloud data is smoothed and simplified to reduce the amount of data and improve the efficiency of subsequent modeling and comparative analysis.

[0065] S4, 3D Modeling and Design Comparison: Using specialized 3D modeling software, based on processed point cloud data, construct actual 3D models of pipes and headers; import the original design models of pipes and headers, and use the software's comparison function to accurately align the actual 3D models with the design models; use the software to automatically identify and quantify dimensional deviations, and mark the locations of the deviations.

[0066] S5, Wall Thickness Measurement: Based on the separated point cloud data of the inner and outer walls of the pipeline, the wall thickness values ​​of different parts of the pipeline are accurately obtained through spatial geometric calculations, and the influence of the on-site environment on the measurement results is corrected; a pipeline wall thickness distribution map is generated to intuitively present the uniformity of the pipeline wall thickness; the parts where the wall thickness does not meet the design requirements are marked, the deviation range is clarified, and the measurement results are ensured to meet the accuracy requirements.

[0067] S6. Results Analysis and Application: The analysis revealed the production process causes of dimensional deviations and non-compliant wall thicknesses; a test report was generated, and targeted rectification suggestions were proposed; the test report was linked to the thermal power plant's production process database to achieve data traceability; relevant production process parameters were adjusted based on the test data, resulting in a significant reduction in dimensional deviations and uneven wall thicknesses of pipes and headers in subsequent production, and a substantial improvement in manufacturing precision.

[0068] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A method for quality inspection of industrial pipelines and headers in thermal power plants, characterized in that, Includes the following steps: Acquire on-site environmental information and raw point cloud data of the components to be tested, including pipes and headers; The original point cloud data is processed to generate a real three-dimensional model that can characterize the actual physical shape of the component to be detected. The actual 3D model is compared with the preset design model to identify and quantify the dimensional deviations between the actual 3D model and the design model in terms of geometric dimensions. The point cloud data of the inner and outer walls of the pipeline are separated based on the original point cloud data, and the wall thickness distribution is obtained based on the original point cloud data of the inner and outer walls and the on-site environmental information. Based on the dimensional deviations and the wall thickness distribution, an inspection report is generated to evaluate the production quality of the pipes and the headers.

2. The method for quality inspection of industrial pipelines and headers in thermal power plants according to claim 1, characterized in that, The acquisition of the raw point cloud data of the component to be detected includes: Determine the on-site information of the components to be inspected, including the material, specifications, structure, and stacking environment of the pipes and headers; Select the appropriate 3D laser scanning equipment according to the required detection accuracy and complete the calibration and debugging. Plan the scanning route and stations, and fix the 3D laser scanning equipment according to the planned route and stations; Based on the on-site information, the scanning parameters are set, and the focal length and angle of the 3D laser scanning equipment are adjusted for pipe wall thickness measurement. The 3D laser scanning equipment is activated to complete the point cloud data acquisition of the entire range of the pipeline and header, and to perform detailed scanning of the pipeline bends, interfaces and header openings, while recording the on-site environmental information.

3. The method for quality inspection of industrial pipelines and headers in thermal power plants according to claim 2, characterized in that, The selection of appropriate 3D laser scanning equipment based on detection accuracy requirements includes: For dimension verification and wall thickness measurement where the accuracy requirement is higher than the preset threshold, a high-precision 3D laser scanning device is selected. For pipes exceeding a preset threshold in length, select a suitable long-distance 3D laser scanning device.

4. The method for quality inspection of industrial pipelines and headers in thermal power plants according to claim 2, characterized in that, When planning the scanning route and stations, it is necessary to ensure that the areas scanned by different stations have a preset degree of overlap. The scanning parameters include at least scanning accuracy and point cloud density; wherein... The scanning accuracy is determined according to the preset accuracy requirements for wall thickness measurement; The point cloud density is set according to the specifications of the pipes and headers, so that the number of point clouds per unit area meets the resolution requirements for dimensional measurement.

5. The method for quality inspection of industrial pipelines and headers in thermal power plants according to claim 1, characterized in that, The process of processing the original point cloud data to generate a real three-dimensional model that can characterize the actual physical form of the component to be detected includes: The original point cloud data is denoised, redundant removed, and multi-site stitched together to obtain a complete point cloud model. The inner wall point cloud data and outer wall point cloud data of the pipe are separated from the complete point cloud model. The deviation of the separated inner wall point cloud data is corrected to eliminate systematic errors caused by scanning angle or occlusion, and the corrected point cloud model is obtained. Based on the modified point cloud model, the actual 3D model of the pipe and header is constructed using a surface reconstruction algorithm.

6. The method for quality inspection of industrial pipelines and headers in thermal power plants according to claim 1, characterized in that, The step of comparing the actual 3D model with the preset design model to identify and quantify the dimensional deviations between the actual 3D model and the design model in terms of geometric dimensions includes: Obtain the original design models of the pipes and headers; The actual 3D model is spatially aligned with the original design model so that they are in the same coordinate system. The algorithm automatically identifies and quantifies the dimensional deviations between the actual 3D model and the design model. The deviation locations and quantified dimensional deviations are marked on the actual 3D model to generate a visual deviation distribution map.

7. The method for quality inspection of industrial pipelines and headers in thermal power plants according to claim 1, characterized in that, The dimensional deviations include the pipe diameter, bending angle, pipe end concentricity and length, as well as the deviations in the pipe end spacing and body size of the header.

8. The method for quality inspection of industrial pipelines and headers in thermal power plants according to claim 1, characterized in that, The step of separating the point cloud data of the inner and outer walls of the pipeline based on the original point cloud data, and obtaining the wall thickness distribution based on the original point cloud data of the inner and outer walls and on-site environmental information, includes: The point cloud data of the inner and outer walls of the pipe are separated based on the point cloud data. Based on the separated point cloud data of the inner and outer walls of the pipeline, the initial wall thickness of each part of the pipeline under the stated field environment information is calculated. The initial wall thickness value is corrected based on the on-site environmental information to eliminate the influence of environmental factors on the accuracy of laser measurement and obtain the actual wall thickness value of each part of the pipeline. Based on the actual wall thickness values ​​of various parts of the pipeline, a wall thickness distribution map is generated. Compare the actual wall thickness values ​​of each part of the pipeline with the designed wall thickness values ​​and wall thickness tolerance range; When the actual wall thickness exceeds the wall thickness tolerance range, the area is determined to be a non-compliant area. Mark the location and deviation of the non-compliant wall thickness area on the actual three-dimensional model or wall thickness distribution map.

9. The method for quality inspection of industrial pipelines and headers in thermal power plants according to claim 1, characterized in that, The step of generating an inspection report for evaluating the production quality of the pipe and the header based on the dimensional deviation and the wall thickness distribution further includes: By integrating the dimensional deviation and the wall thickness distribution, an inspection report is generated that includes deviation parameters, wall thickness data, and deviation location information. The test report is associated and stored in the thermal power plant production process database, and an index relationship is established in the database between the test report and the corresponding pipeline and header production batch; In response to a query command, historical inspection reports are retrieved from the production process database to enable traceability query of dimensional deviation and wall thickness deviation data.

10. A production quality inspection system for industrial pipelines and headers in thermal power plants, characterized in that, include: The data acquisition module is used to acquire on-site environmental information and raw point cloud data of the components to be tested, including pipes and headers. The data processing module is used to process the raw point cloud data to generate a real three-dimensional model that can characterize the actual physical shape of the component to be detected. The dimension deviation detection module is used to compare the actual 3D model with the preset design model, identify and quantify the dimension deviation between the actual 3D model and the design model in terms of geometric dimensions; The wall thickness detection module is used to separate the point cloud data of the inner wall and the outer wall of the pipeline based on the original point cloud data, and to obtain the wall thickness distribution based on the original point cloud data of the inner wall and the outer wall and the on-site environmental information. The results output module is used to generate an inspection report for evaluating the production quality of the pipe and the header based on the dimensional deviation and the wall thickness distribution.

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