Large-scale wind power generation blade internal defect full-automatic detection method based on x-ray nondestructive testing technology

Abstract

The invention relates to a full-automatic detection method for internal defects of a large wind power generation blade based on an x-ray nondestructive testing technology, and provides a full-automatic detection method for internal defects of a large wind power generation blade based on the x-ray nondestructive testing technology, a magnetic positioning and automatic tracing technology, an intelligent automatic control technology, an image preprocessing technology, an image defect identification technology and an image defect accurate positioning technology. The purpose of rapidly detecting and positioning the internal defects of the blade in the production process of the large-scale wind power generation blade is achieved, and the working reliability and the service life of the large-scale wind power generation blade are ensured. The device has the characteristics of high detection precision, high detection speed, full-automatic intelligent detection, no radiation contact of operators and low detection cost.

Description

Technical Field

[0001] This invention relates to a fully automated detection method for internal defects of large wind turbine blades based on X-ray non-destructive testing technology, and particularly to a fully automated detection technology for internal defects of large wind turbine blades during the quality inspection process of wind turbine blade production. Background Technology

[0002] With the rapid development of wind power technology, the manufacturing of large wind turbine blades has become a crucial field. In the research and design of wind turbine units, the enlargement of wind turbine blades is a key technology for increasing single-unit capacity and an important path to improving power generation efficiency. Blade size and shape determine energy conversion efficiency and directly determine the unit's power and performance, making them core components of wind turbine units. Currently, in China, reinforcing fibers and matrix resins account for over 60% of the raw materials used in wind turbine blades, and the various components of the blade are fixed using adhesives, with adhesives and core materials accounting for over 10%. Due to various factors such as manufacturing processes, environmental factors, human operation, and material quality, various internal defects and damages can occur during production and use. The presence of these defects not only affects blade performance but can also lead to blade damage and turbine shutdown in severe cases. Therefore, internal adhesive defect detection of blades is crucial, directly determining the reliability and lifespan of the wind turbine unit.

[0003] Currently, methods for detecting and locating internal defects in large wind turbine blades suffer from low detection accuracy, difficulty in determining defect location and size, and low detection efficiency, failing to meet the needs of large-scale production. Furthermore, these methods require highly skilled operators, necessitating experienced professionals. Therefore, the industry urgently needs a fast, accurate, and safe non-destructive testing method.

[0004] This invention provides a fully automated inspection method based on X-ray nondestructive testing technology to solve the problem of detecting and locating internal defects in the quality inspection process of large wind turbine blades. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a fully automated method for detecting internal defects in large wind turbine blades based on X-ray non-destructive testing technology. The method includes the following steps: Fully automatic tracking and positioning: Based on the shape, size and detection position requirements of the blade, magnetic positioning marks are fixed on the blade; the 102 host sends tracking and control commands, and the 104 automatic tracking control system automatically finds and positions the magnetic positioning marks, and automatically controls the measurement system to accurately run to the detection position.

[0006] Fully automatic detection and control: After the measurement system moves to the detection position, the 102 host computer sends detection and control commands to the 105 high-frequency constant pressure X-ray source horizontal adjustment system, the 107 high-frequency constant pressure X-ray source and detector vertical adjustment system, and the 108 dynamic detector and horizontal adjustment system, respectively. It automatically detects relevant parameter information such as the height, thickness, and detection position of the blade under test, and automatically adjusts the position and distance of the X-ray source, detector and blade to meet the optimal imaging requirements.

[0007] Fully automatic non-destructive imaging: The 102 host computer sends commands and control instructions to the 106 high-frequency constant-pressure X-ray source system and the 108 dynamic detector, respectively. According to the working parameters, the high-frequency constant-pressure X-ray source is automatically controlled to perform X-ray scanning on the inspection area of ​​the blade, and the raw X-ray non-destructive imaging data collected by the detector is read.

[0008] Digital image preprocessing: The 102 host computer performs preprocessing on the acquired raw X-ray image data, such as noise reduction, contrast enhancement, and image binarization, to improve the image clarity and readability.

[0009] Intelligent defect identification and detection: The detection system uses image processing algorithms such as deep learning, histogram enhancement, edge sharpening, image segmentation, and color analysis to analyze defects in preprocessed X-ray images and automatically identify internal defects in the blades.

[0010] Precise defect detection and location: After the system identifies the defect, it will control the detection system to adjust the distance between the X-ray source system, the dynamic detector and the blade, take a second picture of the defect location on the blade, and use a coordinate transformation algorithm to transform the defect location information from the image coordinate system to the blade coordinate system, so as to accurately locate the internal defect of the blade being tested.

[0011] Automatic Report Generation: The inspection system automatically generates inspection reports based on the location and size of the detected defects and customer requirements, making it easier for customers to assess product quality.

[0012] The beneficial effects of this invention are as follows: Utilizing magnetic positioning and automatic tracking technology, the entire inspection process is fully automated and unmanned, improving radiation safety for operators and reducing the requirements for the size of the inspection site; by acquiring non-destructive images of the blade through X-ray scanning, and automatically preprocessing and identifying and detecting defects, internal defects of the blade can be detected quickly and accurately; combining the geometric model of the blade to locate defects improves the accuracy of location; and an inspection report containing detailed defect information is automatically generated, facilitating quality assessment and defect analysis. It features non-destructive measurement, fully automated unmanned operation, accurate detection, fast detection speed, high detection efficiency, convenience, and safety. Attached Figure Description

[0013] Figure 1This is a schematic diagram of the detection system used in this invention; Figure 2 This is a cross-sectional view of a large wind turbine blade structure; Figure 3 This is a mathematical model diagram of the present invention; In the diagram: 101 is the working indicator light section; 102 is the main operating unit section; 103 is the electrical and control system section; 104 is the automatic tracking control system section; 105 is the high-frequency constant-voltage X-ray source horizontal adjustment system section; 106 is the high-frequency constant-voltage X-ray source system section; 107 is the X-ray source and detector vertical adjustment system section; 108 is the dynamic detector and horizontal adjustment system section. 201 is the blade sparsity; 202 is the shear web; 203 is the blade root; 204 is the core material. Detailed Implementation

[0014] The purpose of this invention is to provide a fully automated method for detecting internal defects in large wind turbine blades based on X-ray non-destructive testing technology, achieving the goal of rapid, accurate, non-destructive, unmanned, and automated defect detection and location.

[0015] The specific technical measures taken to solve the technical problem are as follows: Large wind turbine blades are bulky and have multiple adhesive bonding points. First, based on the shape, size, and detection location requirements of the blade, magnetic positioning marks are fixed on the blade, and the running trajectory of the detection system is preset for fully automatic detection.

[0016] After the system is powered on, the host (102) controls the work indicator light (101) to turn on. The automatic tracking control system (104) measures the magnetic field strength of the magnetic positioning mark in the detection area through the magnetic sensor. The host (102) calculates the current position of the measurement system based on the known magnetic field model, measurement data, and position coordinates of the measurement system, and calculates the motion trajectory information of the detection system. The host (102) and the automatic tracking system (104) automatically control the detection system to move to the detection target position.

[0017] After the measurement system reaches the detection position, the 102 host computer sends instructions to the 105 high-frequency constant-pressure X-ray source horizontal adjustment system, the 107 high-frequency constant-pressure X-ray source and detector vertical adjustment system, and the 108 dynamic detector and horizontal adjustment system to adjust the position and distance parameters of the X-ray source, detector and blade to achieve the best imaging effect.

[0018] After the X-ray source and detector are positioned, the 102 operating host sends a command to the 103 electrical and control system to control the 101 to turn on the radiation working indicator light; the 102 operating host sends a control command to the 106 high-frequency constant-pressure X-ray source system to automatically adjust the high-frequency constant-pressure X-ray source working parameters and perform X-ray scanning on the blade detection area; and sends a command to the 108 dynamic detector to read the raw image data of the X-ray scanning imaging.

[0019] The 102 host system utilizes an image processing system to preprocess the acquired X-ray images. This includes median filtering for noise reduction, histogram equalization for contrast enhancement, and threshold segmentation for image binarization. Simultaneously, the system performs defect detection on the preprocessed images. Edge detection is used to identify linear defects such as cracks; threshold segmentation is used to identify regional defects such as bubbles; and texture analysis is used to identify complex defects such as fiber breaks.

[0020] If the system identifies a defect, it will automatically control the system to perform two more scans of the defect location. In the first scan, the system automatically adjusts the X-ray generator and the flat panel detector to be close to the top and bottom of the blade to capture the image. In the second scan, the system automatically adjusts the X-ray generator to move downwards by L2cm and captures the image again. By adjusting the image capture distance, two images with different defect sizes are obtained. The horizontal length of the defect in the two images is defined as A1 and A2, respectively; the distance between the X-ray generator and the flat panel detector during the two image captures is defined as L1 and L1 + L2, respectively; the actual size of the defect is defined as A; and the distance from the defect to the X-ray generator is defined as L. Based on the principle of X-ray imaging: A / A1 = L / L1; A / A2 = (L + L2) / (L1 + L2); The derivation yields: (L*A1) / L1=(L+ L2)*A2 / (L1+ L2); L= L1*L2*A2 / (L1*A1+L2*A1-L1*A2); Using the above methods and combining the geometric model of the blade, the system employs a feature matching algorithm to match the X-ray image with the geometric model of the blade; a three-dimensional reconstruction algorithm to obtain the three-dimensional structural information of the blade; and a coordinate transformation algorithm to transform the location information of the defect from the image coordinate system to the blade coordinate system, thereby achieving accurate localization of the internal defects of the tested blade.

[0021] After completing the scanning and detection of one location, the automatic tracking system automatically controls the detection system to move to the next detection location, performs imaging detection and analysis on the new detection location, until the detection and analysis of all preset detection locations are completed.

[0022] Finally, after completing the imaging inspection of all inspection points, the 102 host computer issues control commands to reset the 106 high-frequency constant-pressure X-ray source and the 108 dynamic detector, and sequentially shuts down the 106 high-frequency constant-pressure X-ray source, the 108 dynamic detector, the 101 working indicator light, and the radiation warning light. The inspection system automatically generates an inspection report based on the location and size information of the detected defects and customer requirements, providing a basis for customers to evaluate product quality.

Claims

1. A fully automated method for detecting internal defects in large wind turbine blades based on X-ray nondestructive testing technology, characterized in that: The main operating unit (102), automatic control indicator light unit (101), electrical and control system unit (103), automatic tracking control system unit (104), high-frequency constant-pressure X-ray source horizontal adjustment system unit (105), high-frequency constant-pressure X-ray source system unit (106), X-ray source and detector vertical adjustment system unit (107), and dynamic detector and horizontal adjustment system unit (108) complete the entire process of detecting, identifying, and locating internal defects in the blade. The specific control method is as follows: First, based on the shape, size, and detection position requirements of the blade, magnetic positioning marks are fixed on the blade, and the running trajectory of the fully automatic detection system is preset. When the test begins, the main unit (102) sends control commands to the electrical and control system (103) and the automatic tracking control system (104) to automatically locate the magnetic positioning mark and control the test equipment to run to the test position of the blade. When the equipment is moved to the detection position, the main unit (102) sends control commands to the high-frequency constant pressure x-ray source horizontal adjustment system (105), the x-ray source and detector vertical adjustment system (107), and the dynamic detector and horizontal adjustment system (108) to adjust the distance and position parameters between the x-ray source, detector and the tested blade. After the distance and position parameters of the X-ray source, detector and the blade under test are adjusted, the main operating unit (102) sends a command to the electrical and control system (103) to control the activation of the radiation working indicator (101); the main operating unit (102) sends a control command to the high-frequency constant pressure X-ray source system (106) to automatically adjust the working parameters of the high-frequency constant pressure X-ray source and perform X-ray scanning on the blade detection area; the main operating unit (102) sends a command to the dynamic detector system (108) to read the raw image data of the X-ray scanning imaging. The host unit 102 uses the image processing system to preprocess the acquired X-ray images to obtain high-quality X-ray images; The detection software performs defect detection, identification, and location on the pre-processed images; based on the detected defect information, the detection software automatically generates a detection report to complete the detection of internal defects in the blades.

2. The fully automated detection method for internal defects of large wind turbine blades based on X-ray nondestructive testing technology according to claim 1, characterized in that: The pre-set detection system's fully automatic detection trajectory is defined as follows: According to the shape, size and detection position requirements of the blade, magnetic positioning marks are fixed on the blade, and the running trajectory of the fully automatic detection system is preset. After the system is started, the automatic tracking control system (104) measures the magnetic field strength of the magnetic positioning marks in the detection area through the magnetic sensor. The host (102) calculates the current position of the measurement system and the motion trajectory information of the detection system based on the known magnetic field model, measurement data and the position coordinates of the measurement system. The host (102) and the automatic tracking system (104) automatically control the detection system to move to the detection target position and start the imaging detection based on X-ray non-destructive testing technology at the detection position. After completing the detection of a position, the automatic tracking system exits the current detection position, calculates the coordinate information of the next detection position according to the blade detection sequence requirements, controls the detection system to run automatically to the detection position, and detects the magnetic field strength information of the magnetic positioning marks at the next detection position. The system controls the detection system to run precisely to the new detection position and automatically starts the imaging detection at the new position, quickly completing the fully automatic detection of all detection positions of the entire blade.

3. The fully automated detection method for internal defects of large wind turbine blades based on X-ray nondestructive testing technology according to claim 1, characterized in that: The specific method for accurately detecting and locating internal defects in blades is as follows: After identifying a defect, the defect detection software system automatically adjusts the distance between the X-ray source, dynamic detector, and the blade being tested, performing two re-scanning images of the defect location. In the first image capture, the system automatically adjusts the X-ray generator and flat-panel detector to be close to the upper and lower surfaces of the blade. In the second image capture, the system automatically adjusts the X-ray generator to move downwards by L2cm and captures another image. By adjusting the image capture distance, two images with different defect sizes are obtained. The horizontal lengths of the defect in the two images are defined as A1 and A2, respectively. The distances between the X-ray generator and the flat-panel detector during the two image captures are defined as L1 and L1 + L2, respectively. The actual size of the defect is defined as A, and the distance from the defect to the X-ray generator is defined as L. L= L1*L2*A2 / (L1*A1+L2*A1-L1*A2); According to the above formula, the defect detection software system calculates the ratio of change between the two images, performs three-dimensional reconstruction, feature matching, and coordinate transformation calculation on the blade, and transforms the defect location information from the image coordinate system to the blade coordinate system, so as to accurately calculate the size and accurately locate the internal defect of the blade under test.

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