A wind turbine blade coating aging online detection method and device

Abstract

The application discloses a wind turbine blade coating aging online detection method and device, and the method comprises the following steps: obtaining blade information of a wind turbine blade to be detected, including blade type, blade parameter, coating composition and content distribution; adjusting each equipment parameter of a built laser-induced breakdown spectroscopy platform by using the blade information, determining a test light path, a focusing radius and target parameters of each equipment; performing online testing on the wind turbine blade by using the adjusted laser-induced breakdown spectroscopy platform, obtaining spectral data of the blade, and analyzing the aging degree of the wind turbine blade coating by using the spectral data. The application uses the laser-induced breakdown spectroscopy technology to analyze the aging degree of the wind turbine blade, and has the advantages of low testing cost, high precision, high efficiency, wide application range and easy popularization.

Description

Technical Field

[0001] This application relates to the field of aging detection technology for power generation equipment, and in particular to an online detection method and device for aging of wind turbine blade coating. Background Technology

[0002] As a key component of wind turbine generators, the condition of wind turbine blades largely determines how the unit will be handled upon reaching the end of its service life. Because the operating environment of the blades is extremely harsh, various media in the air constantly corrode them. High and low temperatures, lightning, hail, rain, snow, and dust storms can all potentially damage the blades, affecting aerodynamic performance to the point of compromising structural safety. The cost of regular maintenance and repair of the blades is only about 3% of the power generation revenue of a single wind turbine. Timely inspection and maintenance of the blades can ensure stable power generation, minimizing losses and risks. Therefore, if it is desired that the unit continue to operate or be upgraded for continued operation, the blades need to be inspected to determine whether they meet the conditions for continued or upgraded operation.

[0003] Currently, common blade inspection methods in the industry include visual inspection, endoscopic robot inspection, drone inspection, and ultrasonic inspection. While visual inspection is simple to operate, its results often depend on the inspector's skill and condition, resulting in low efficiency and unreliable accuracy. Endoscopic robot inspection can examine surface defects in small, inaccessible areas, but its applicability is limited by blade angle, obstacles, and other factors. Drone inspection improves efficiency, but it is very expensive and lacks resistance testing capabilities, requiring manual testing and making it cumbersome. Ultrasonic inspection uses an ultrasonic flaw detector to check the bonding quality in small areas; however, this method is susceptible to medium limitations, requires numerous test points, and has low efficiency when conducted from the air. Summary of the Invention

[0004] The purpose of this application is to provide an online detection method and device for the aging of wind turbine blade coatings, so as to solve the problems of low efficiency, high cost, low testing accuracy, limited applicability, and difficulty in promotion in existing wind turbine blade aging detection methods.

[0005] To achieve the above objectives, this application provides an online detection method for the aging of wind turbine blade coatings, comprising:

[0006] Obtain blade information of the wind turbine blade under test, including blade type, blade parameters, coating composition and content distribution;

[0007] The blade information is used to adjust the parameters of each device in the constructed laser-induced breakdown spectroscopy platform, and to determine the test optical path, focusing radius, and target parameters of each device.

[0008] The adjusted laser-induced breakdown spectroscopy platform was used to conduct online tests on the wind turbine blades to obtain spectral data of the blades. The spectral data was then used to analyze the degree of aging of the wind turbine blade coating.

[0009] Furthermore, the blade parameters include blade installation angle, airfoil, length, and average angle of attack;

[0010] The coating components include glass fiber, resin, balsa wood, and adhesive, wherein the adhesive includes dibutyl phthalate or dioctyl phthalate.

[0011] Furthermore, adjusting the equipment parameters of the constructed laser-induced breakdown spectroscopy platform using the blade information includes:

[0012] The laser-induced breakdown spectral platform was constructed, including the optical path main body composed of a coaxial telescope system, laser, optical components, and spectrometer.

[0013] When a Q-switched laser is used, the laser energy range is selected to be 0W~1000mJ, and the defocus range is selected to be -100mm~+100mm.

[0014] By triggering the spectrometer with a controller, adjusting the delay time and gate width of plasma excitation and spectrometer acquisition, as well as the laser output power and spot size, the range of laser energy parameters and delay time of the spectral signal intensity can be obtained.

[0015] Furthermore, the online detection method for aging of wind turbine blade coatings also includes:

[0016] When using a fiber laser or a semiconductor laser, the wavelength range is selected as 256nm~1024nm.

[0017] Furthermore, based on the coating composition and content distribution of the blade, the focusing radius is selected according to the distance from the laser-induced breakdown spectral platform to the detection position; among which,

[0018] The focusing radius is determined based on the spectral intensity collected by the spectrometer and the average spectral intensity and average standard deviation obtained after multiple repeated measurements. For example, if the average spectral intensity collected exceeds 1000, the average standard deviation is less than 0.5.

[0019] Furthermore, after obtaining the spectral data of the leaf, the process also includes:

[0020] The spectral data is preprocessed, including data cleaning, filtering out null values, removing outliers, and data normalization.

[0021] Furthermore, the analysis of the aging degree of the wind turbine blade coating using spectral data includes:

[0022] Obtain the intensity of atomic emission spectral lines of carbon, hydrogen, oxygen, and nitrogen in spectral data;

[0023] Based on the atomic emission spectral line intensities and ion concentrations of carbon, hydrogen, oxygen, and nitrogen, partial least squares linear regression is performed to obtain the calibration relationship between the spectral data and the coating composition, thereby determining the degree of coating aging.

[0024] This application also provides an online detection device for the aging of wind turbine blade coatings, including:

[0025] The information acquisition unit is used to acquire blade information of the wind turbine blade under test, including blade type, blade parameters, coating composition and content distribution.

[0026] The parameter determination unit is used to adjust the parameters of each device in the constructed laser-induced breakdown spectroscopy platform using the blade information, and to determine the test optical path, focusing radius, and target parameters of each device.

[0027] The aging analysis unit is used to conduct online tests on wind turbine blades using an adjusted laser-induced breakdown spectral platform, obtain spectral data of the blades, and analyze the degree of aging of the wind turbine blade coating using the spectral data.

[0028] This application also provides a terminal device, including:

[0029] One or more processors;

[0030] A memory, coupled to the processor, for storing one or more programs;

[0031] When the one or more programs are executed by the one or more processors, the one or more processors implement the online detection method for aging of wind turbine blade coatings as described in any of the preceding claims.

[0032] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the online detection method for aging of wind turbine blade coating as described in any of the preceding claims.

[0033] Compared to existing technologies, the advantages of this application are as follows:

[0034] This application discloses an online detection method and apparatus for the aging of wind turbine blade coatings. The method includes: acquiring blade information of the wind turbine blade to be tested, including blade type, blade parameters, coating composition and content distribution; adjusting the parameters of each device on a laser-induced breakdown spectroscopy platform using the blade information to determine the test optical path, focusing radius, and target parameters of each device; performing online testing on the wind turbine blade using the adjusted laser-induced breakdown spectroscopy platform to obtain the blade's spectral data; and analyzing the degree of aging of the wind turbine blade coating using the spectral data. This application utilizes laser-induced breakdown spectroscopy technology to analyze the aging degree of wind turbine blades, which not only has low testing cost but also high accuracy and efficiency, wide applicability, and is easy to promote. Attached Figure Description

[0035] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0036] Figure 1 This is a schematic flowchart of an online detection method for the aging of wind turbine blade coatings provided in a certain embodiment of this application;

[0037] Figure 2 This is a schematic diagram of the structure of a laser-induced breakdown spectroscopy platform provided in a certain embodiment of this application;

[0038] Figure 3 This is a schematic diagram of the atomic emission lines of a blade element provided in a certain embodiment of this application;

[0039] Figure 4 This is a schematic diagram of the structure of an online detection device for the aging of wind turbine blade coatings provided in a certain embodiment of this application;

[0040] Figure 5 This is a schematic diagram of the structure of a terminal device provided in a certain embodiment of this application. Detailed Implementation

[0041] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0042] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0043] It should be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0044] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0045] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0046] To aid understanding, we will first explain the common blade inspection methods currently used in the industry, as follows:

[0047] 1) Visual inspection: This method mainly utilizes the visual inspection of the inspectors. The advantage of this method is that it can be carried out at the same time as testing the resistance of the lightning protection system. It is simple to operate and very economical. However, since it is based solely on visual judgment, the inspection results depend on the inspectors' own abilities, emotions, and physical condition. This often results in inconsistent inspection results from different inspectors, making it difficult to guarantee accuracy and resulting in low inspection efficiency.

[0048] 2) Endoscopic robot inspection: This method mainly uses an endoscopic robot to enter a narrow area that is inaccessible to human personnel to inspect the appearance defects of the blade; however, it is limited by factors such as blade angle and obstacles (such as structural glue nodules and lightning protection wires), and cannot inspect some areas of the blade, so its application in the industry is not widespread.

[0049] 3) Drone Inspection: This refers to the use of drones to inspect the exterior of blades, replacing manual labor. Drones can efficiently inspect the exterior of blades without relying on the skills of the inspectors; however, while drone inspection services have developed rapidly in recent years, drone equipment with automatic cruise control systems is expensive, significantly increasing the cost of inspection equipment. Furthermore, this method currently only inspects the exterior of blades and lacks the function of testing resistance, requiring additional manual resistance testing, making it more cumbersome than visual inspection.

[0050] 4) Ultrasonic inspection: This refers to the use of an ultrasonic flaw detector by inspectors to test the bonding quality of the blades. Ultrasonic flaw detectors can inspect the bonding quality in narrow areas that are inaccessible to manual inspection; however, due to the limitations of the medium, this method cannot inspect the trailing edge bonding area containing the core material, limiting the number of areas that can be inspected; at the same time, because it involves multi-point testing, the efficiency of inspection in the air is relatively low.

[0051] Therefore, considering the complex operating conditions and large size of wind turbine blades, this application aims to provide a method for remotely and online detecting the aging characteristics of blades. Specifically, this application provides an online detection method for wind turbine blade coating aging based on laser-induced breakdown spectroscopy (LAS). The detection principle of this method is as follows: LAS focuses a pulsed laser onto the wind turbine blade, causing laser ablation. Under the dual action of the laser shock wave, the coating on the blade surface evaporates or peels off instantaneously, generating atomic emission spectra. These spectra are then absorbed by a spectrometer through a remote lens to analyze defects such as coating peeling, structural layer wrinkles, cracking and delamination of the inner and outer surfaces of the blade, as well as the degree of blade aging.

[0052] Please see Figure 1 This application provides an embodiment of a method for online detection of coating aging in wind turbine blades. For example... Figure 1 As shown, the online detection method for aging of wind turbine blade coating includes steps S10 to S30. The specific steps are as follows:

[0053] S10. Obtain the blade information of the wind turbine blade under test, including blade type, blade parameters, coating composition and content distribution.

[0054] In this embodiment, the first step is to obtain the blade type and corresponding blade parameters of the wind turbine blade to be tested, including the blade installation angle, airfoil, length, and average angle of attack. Secondly, the coating composition and basic content distribution of the wind turbine blade are also obtained. The coating composition includes glass fiber, resin, balsa wood, and adhesive, and the adhesive includes dibutyl phthalate or dioctyl phthalate, etc.

[0055] S20. Using the blade information, adjust the parameters of each device on the constructed laser-induced breakdown spectroscopy platform to determine the test optical path, focusing radius, and target parameters of each device.

[0056] Before performing step S20, the laser-induced breakdown spectroscopy platform used for the test needs to be set up. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 A schematic diagram of the platform's structure is provided.

[0057] It should be noted that the laser-induced breakdown spectroscopy platform is also known as the LIBS platform. In this embodiment, the design concept of a remote LIBS platform is adopted, with a coaxial telescope system as the main optical path. The device mainly consists of a Q-switched laser, optical components, a spectrometer, etc.

[0058] Furthermore, using the coaxial Schwarzschild telescope system as the main optical path, the platform is required to have laser focusing and spectral acquisition capabilities in order to reduce costs, shrink size, and have a variable focal length.

[0059] Furthermore, by controlling the gate width and time delay of the charge-coupled device (CCD), the platform enables the laser, spectrometer, and computer to operate in the required experimental sequence. Adjusting the spectrometer's light intensity gain effectively improves the signal-to-noise ratio. The telescope's focal length is adjusted to maximize the efficiency of receiving the plasma spectrum of the test sample. The plasma spectral data processing can be performed using computer-aided spectral analysis software.

[0060] After the laser-induced breakdown spectroscopy platform is set up, the blade information obtained in step S10 needs to be used to adjust the parameters of each device on the platform and determine the final test plan.

[0061] In one specific embodiment, adjusting the device parameters includes the following:

[0062] 1) When the laser is a Q-switched laser, the laser energy range is selected to be 0W~1000mJ and the defocus range is selected to be -100mm~+100mm; preferably, the laser can be an Nd:YAG pulsed laser.

[0063] 2) Trigger the spectrometer by means of controller, adjust the delay time and gate width of plasma excitation and spectrometer acquisition to obtain the best acquisition effect and signal strength, and conduct tests on the wind turbine blades in operation on site.

[0064] 3) Adjust the delay time and gate width of plasma excitation and spectrometer acquisition, as well as the laser output power and spot size, to obtain a laser energy parameter range and delay time that provides good spectral acquisition effect and spectral signal intensity for the wind turbine blades. Test the adjusted remote testing system on the wind turbine blades in the field to verify the practicality of the field testing system.

[0065] 4) Other types of lasers can be selected based on the thickness, morphology, and coating composition of the wind turbine blades. Additionally, fiber lasers or semiconductor lasers can be chosen depending on the distance between the wind turbine blades and the equipment's detection point. The wavelength range is selectable from 256nm to 1024nm, and the laser output energy is adjustable. The optical lens uses a coaxial telescope system as the main optical path, enabling automatic focusing. The laser's long-range operating range is 0–210 meters.

[0066] 5) Based on the coating composition and content distribution of the blade, the focusing radius is selected according to the distance from the laser-induced breakdown spectral platform to the detection position; wherein, the focusing radius is determined according to the spectral intensity collected by the spectrometer and the average value and average standard deviation of the spectral intensity obtained after multiple repeated measurements, including if the average spectral intensity collected exceeds 1000, then the average standard deviation is less than 0.5.

[0067] S30. The wind turbine blades are tested online using the adjusted laser-induced breakdown spectral platform to obtain spectral data of the blades. The spectral data is then used to analyze the degree of aging of the wind turbine blade coating.

[0068] In this step, the wind turbine blades are tested online using an adjusted laser-induced breakdown spectroscopy platform to obtain their spectral data. Then, based on this spectral data and the relationship between the four elements (C, H, O, and N) in the wind turbine blades, the relative spectral intensities of the LIBS (Lithium-Induced Breakdown Spectroscopy) are detected to identify the samples.

[0069] In one specific embodiment, after obtaining the spectral data of the leaves, it is also necessary to preprocess the spectral data, including data cleaning, filtering out null values, removing outliers, and data normalization.

[0070] Specifically, in this embodiment, outlier removal refers to removing abnormal data that are greater than 60% of the mean based on the cumulative measurement variance; data normalization is performed by taking the logarithm of the data.

[0071] Further, after data preprocessing, based on elements such as glass fiber, resin, and balsa wood in the leaf composition, atomic emission lines of elements with high signal-to-noise ratio and prominent spectral intensity, such as C, H, O, and N, were selected. Then, based on the intensity of the atomic emission spectral lines and ion concentrations of carbon, hydrogen, oxygen, and nitrogen, partial least squares linear regression was performed to obtain the calibration relationship between the spectral data and the coating composition, thus determining the degree of coating aging. The atomic emission lines are as follows: Figure 3 As shown.

[0072] In one specific implementation, aging analysis includes the following:

[0073] For a given element corresponding to a spectral line, its E m , g m and A mn Parameters remain constant. When the environmental and laser parameters remain unchanged... F The parameters are constants. The results show that, in fact, the plasma temperature did not significantly affect the calibration results. bAs a constant, the elemental concentration in the plasma has an ideal linear relationship with the spectral intensity, that is:

[0074] (1)

[0075] Considering that factors such as a certain degree of spectral line self-absorption can cause varying degrees of reduction in spectral line intensity, it is necessary to modify equation (1) by introducing a self-absorption coefficient. a :

[0076] (2)

[0077] in, a It is the self-absorption coefficient. a The value is related to the content of the sample, elemental properties, light source characteristics, and spectral properties.

[0078] When atoms or ions are excited in the high-temperature region of plasma, they emit spectral lines of several wavelengths. Due to the uneven temperature distribution in space, when the emitted spectral line radiation passes through the low-temperature region of the plasma, it is absorbed by its own ions because the energy level of vapor ions in the low-temperature region is lower than that in the high-temperature region. This weakens the intensity at the center of the spectral line, i.e., self-absorption occurs, and the surrounding contours also change. When self-absorption becomes more severe, the spectral intensity decreases instead of increasing. Generally, when the laser energy is not high and the selected spectral line intensity is not very strong, this situation is less likely to occur. a =1. Taking the logarithm, we get:

[0079] (3)

[0080] Based on formula (3), a logarithmic linear relationship fitting curve between C / H / O / N ion concentration and spectral relative intensity was established, where the C / H / O / N ion concentration was obtained by other component detection equipment in the laboratory, such as ICP. The coating thickness was obtained by SEM scanning electron microscopy after repeated measurements.

[0081] Finally, using the logarithms of the emission spectral line intensities of the corresponding C / H / O / N elements as the dependent variable and the C / H / O / N ion concentrations as the independent variables, partial least squares linear regression was performed to obtain the calibration relationship between the spectral data and the coating composition. According to the partial least squares linear regression algorithm:

[0082] (4)

[0083] Where X is the independent variable of spectral data, and Y is the dependent variable of Gigi Lai's concentration. T , U yes p An extracted n×p dimensional matrix, the matrix P Np sum matrix QMp Indicates loading a matrix, matrix E nN sum matrix F It is the residual matrix. The classic form of the PLS algorithm is based on the nonlinear iterative partial least squares (NIPALS) algorithm to find the weight vector. w , c , so that:

[0084] (5)

[0085] in cov ( t,u ) = t T u / n Represents the score vector t and u The sample covariance between them.

[0086] (6)

[0087] in C T = DQ T Now it means p OK M The regression coefficient matrix of the column, F * = HQ T + F It is the residual matrix. D pp It is a diagonal matrix. H The residual matrix is ​​represented. The asymmetric assumption of the predictor-predictor relationship is transformed into a transformation scheme. Equation (6) is used with orthogonal predictors. T Ordinary least squares regression decomposition Y .

[0088] Based on this, a partial least squares relationship between coating composition and spectral intensity was established. As the wind turbine blades are put into service, the concentration of the same element on the surface and the concentration of the same element inside will change. Therefore, according to the analysis results of formula (6), the spectral intensity will also change. Thus, based on the spectral intensity of the coating at different depths under repeated laser shocks, the concentration change of the element at different coating thicknesses can be reflected, thereby realizing the characterization of the aging of the wind turbine blades.

[0089] Furthermore, it should be noted that the above spectral data can also be used to qualitatively or quantitatively reflect the degree of wrinkles and cracks based on the characteristics of defects such as wrinkles in the surface structural layer of the blade, cracks and delamination in the inner and outer surfaces of the blade, and the different reflection and ablation intensities of atomic emission spectra. This provides qualitative or quantitative aging assessment results for the service life of the blade.

[0090] Please see Figure 4 One embodiment of this application also provides an online detection device for the aging of wind turbine blade coatings, comprising:

[0091] Information acquisition unit 01 is used to acquire blade information of the wind turbine blade under test, including blade type, blade parameters, coating composition and content distribution;

[0092] The parameter determination unit 02 is used to adjust the parameters of each device on the constructed laser-induced breakdown spectroscopy platform using the blade information, and to determine the test optical path, focusing radius and target parameters of each device.

[0093] The aging analysis unit 03 is used to conduct online testing on wind turbine blades using an adjusted laser-induced breakdown spectral platform, obtain spectral data of the blades, and analyze the degree of aging of the wind turbine blade coating using the spectral data.

[0094] The online detection device for wind turbine blade coating aging provided in this application embodiment is used to perform the method described in any of the above embodiments. By using laser-induced breakdown spectroscopy technology to analyze the degree of aging of wind turbine blades, it not only has low testing cost, but also high accuracy and efficiency, and has a wide range of applications and is easy to promote.

[0095] Please see Figure 5 One embodiment of this application provides a terminal device, including:

[0096] One or more processors;

[0097] A memory, coupled to the processor, for storing one or more programs;

[0098] When the one or more programs are executed by the one or more processors, the one or more processors implement the online detection method for aging of wind turbine blade coatings as described above.

[0099] The processor controls the overall operation of the terminal device to complete all or part of the steps of the online detection method for wind turbine blade coating aging described above. The memory stores various types of data to support the operation of the terminal device. This data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0100] In an exemplary embodiment, the terminal device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the online detection method for wind turbine blade coating aging as described in any of the above embodiments, and achieve the same technical effect as the above method.

[0101] In another exemplary embodiment, a computer-readable storage medium including a computer program is also provided. When executed by a processor, the computer program implements the steps of the online detection method for wind turbine blade coating aging as described in any of the above embodiments. For example, the computer-readable storage medium may be the aforementioned memory including the computer program, which may be executed by a processor of a terminal device to complete the online detection method for wind turbine blade coating aging as described in any of the above embodiments and achieve the same technical effects as the aforementioned method.

[0102] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for on-line detection of wind turbine blade coating degradation, characterized in that, The method comprises the following steps: obtaining blade information of a wind turbine blade to be tested, including blade type, blade parameters, coating composition and content distribution; the blade parameters include blade installation angle, airfoil shape, length and average angle of attack; the coating composition includes glass fiber, resin, balsa wood and adhesive, and the adhesive includes dibutyl phthalate or dioctyl phthalate; adjusting the device parameters of the built laser-induced breakdown spectroscopy platform using the blade information, determining the test light path, focusing radius and target parameters of each device; using the adjusted laser-induced breakdown spectroscopy platform to test the wind turbine blade online to obtain spectral data of the blade, and analyzing the aging degree of the wind turbine blade coating using the spectral data; wherein adjusting the device parameters of the built laser-induced breakdown spectroscopy platform using the blade information comprises: obtaining the built laser-induced breakdown spectroscopy platform, including the light path main body composed of a coaxial telescope system, a laser, optical elements and a spectrometer; when the laser is a Q-switched laser, the laser energy size range is selected as 0W~1000mJ, and the defocusing amount range is selected as -100mm~+100mm; the laser remote action range is 0~210 meters; triggering the spectrometer through the controller to adjust the delay time and gate width of plasma excitation and spectrometer acquisition, as well as the laser output power and spot size, to obtain the laser energy parameter range and delay time of the spectral signal intensity; based on the coating composition and content distribution of the blade, the focusing radius is selected according to the distance from the laser-induced breakdown spectroscopy platform to the detection position; wherein the focusing radius is determined according to the spectral intensity collected by the spectrometer and the average value and average standard deviation of the spectral intensity obtained after multiple repeated measurements, including if the average spectral intensity collected is more than 1000, then the average standard deviation is less than 0.5; the use of spectral data to analyze the aging degree of the wind turbine blade coating comprises: obtaining the atomic emission spectral line intensity of carbon, hydrogen, oxygen and nitrogen elements in the spectral data; based on the linear relationship between ion concentration and spectral relative intensity, a linear regression of the least squares method is performed on the atomic emission spectral line intensity and ion concentration of carbon, hydrogen, oxygen and nitrogen elements to obtain the calibration relationship between the spectral data and the coating composition, and based on the spectral intensity at different depths of the coating under successive laser impacts, the aging degree of the coating is determined; the linear relationship between ion concentration and spectral relative intensity is expressed as: wherein a is the self-absorption coefficient; is the relative spectral intensity; b is a constant; C s is the ion concentration.

2. The wind turbine blade coating degradation on-line detection method according to claim 1, characterized in that, further comprising: when the laser is a fiber laser or a semiconductor laser, the wavelength range is selected as 256nm~1024nm.

3. The wind turbine blade coating degradation on-line detection method according to claim 1, characterized in that, after obtaining the spectral data of the blade, further comprising: preprocessing the spectral data, including data cleaning, filtering null values, removing outliers and data normalization.

4. An apparatus for on-line detection of wind turbine blade coating degradation, characterized by application of the wind turbine blade coating aging online detection method according to claim 1 comprises: an information acquisition unit for acquiring blade information of a wind turbine blade to be tested, including blade type, blade parameters, coating composition and content distribution; A parameter determination unit is configured to adjust each device parameter of the built laser-induced breakdown spectroscopy platform by using the blade information, determine a test light path, a focusing radius, and a target parameter of each device; An aging analysis unit is configured to perform online testing on the wind turbine blade by using the adjusted laser-induced breakdown spectroscopy platform, obtain spectral data of the blade, and analyze the aging degree of the wind turbine blade coating by using the spectral data.

5. A terminal device, characterized by, The method comprises the steps of: one or more processors; a memory coupled to the processors and storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors realize the wind turbine blade coating aging online detection method according to any one of claims 1-3.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the wind turbine blade coating aging online detection method according to any one of claims 1-3.

Images