A wind power tower drum anti-corrosion coating online monitoring method based on high-order ultrasonic zero group velocity lamb wave and related device

Abstract

The application discloses a kind of wind power tower cylinder anticorrosive coating on-line monitoring method based on high-order ultrasonic zero group velocity lamb wave and related device;Method includes: determining the high-order ZGV mode corresponding to target wind power tower cylinder, determine the deployment position and excitation frequency of sensor array on target wind power tower cylinder;By sensor array, excitation signal satisfying the excitation frequency of high-order ZGV mode is sent to target wind power tower cylinder, ZGV frequency and ZGV amplitude under the position of sensor array at target wind power tower cylinder are collected;Using the preset ZGV-coating thickness parameter associated model associated with target wind power tower cylinder determines the frequency and amplitude corresponding to preset peeling coating and preset good coating;Based on the frequency and amplitude corresponding to preset peeling coating and preset good coating, the coating state of target wind power tower cylinder under different sensor positions is judged by ZGV frequency and ZGV amplitude, so that the anticorrosive coating state of wind turbine tower can be detected with high precision in real time.

Description

Technical Field

[0001] This invention relates to the field of equipment maintenance technology, and in particular to an online monitoring method and related device for the anti-corrosion coating of wind turbine towers based on high-order ultrasonic zero-group velocity Lamb waves. Background Technology

[0002] As the core supporting structure of a wind turbine, the tower bears the generator and rotating blades at the top and is anchored to the foundation at the bottom. During operation, the tower needs to withstand multiple stresses, including strong wind loads and mechanical vibrations. Its structural durability directly determines the overall operating cycle of the turbine and provides a safe platform for daily maintenance. The main body of the tower is usually made of carbon steel or low-alloy steel, with a multi-layer anti-corrosion coating (such as epoxy primer and polyurethane topcoat), typically 200-500 mm thick. The steel structure forms the first line of defense against environmental corrosion. However, the harsh marine atmospheric environment, with its high salt spray, high humidity, strong sunlight, and seawater splash, accelerates the thinning and even partial peeling of the anti-corrosion coating on the tower surface. If coating defects are not detected in time, large-scale repairs or even complete replacement of the tower are required, leading to a surge in maintenance costs. Furthermore, if the anti-corrosion coating fails, the steel is directly exposed to corrosive media, which may cause electrochemical corrosion of the base steel, seriously threatening structural safety and causing catastrophic structural damage (such as tower fracture).

[0003] Existing technologies for monitoring the anti-corrosion coatings of wind turbine towers are mainly divided into traditional offline detection technologies and online monitoring technologies. Traditional offline detection methods include ultrasonic thickness gauges, electrochemical impedance spectroscopy, and visual inspection and tapping methods. Ultrasonic thickness gauges require manual point-by-point measurements, cannot cover large areas, and are affected by the curvature of the tower surface; electrochemical impedance spectroscopy can only assess the overall degradation of the coating and cannot locate local defects; visual inspection and tapping methods rely on experience and are not sensitive to minute thickness changes. Online monitoring technologies include fiber optic grating sensors, conventional ultrasonic guided waves, and infrared thermal imaging. Fiber optic grating sensors are complex to install and susceptible to electromagnetic interference; the group velocity of conventional ultrasonic guided waves varies with frequency, causing signal dispersion and making it difficult to distinguish minute thickness changes; and infrared thermal imaging is greatly affected by ambient temperature and has limited ability to detect internal defects in the coating.

[0004] Therefore, existing monitoring technologies for the anti-corrosion coating of wind turbine towers are insufficient for real-time and high-precision detection of the anti-corrosion coating status of wind turbine towers. Summary of the Invention

[0005] This invention provides an online monitoring method and related device for the anti-corrosion coating of wind turbine towers based on high-order ultrasonic zero-group velocity Lamb waves, which solves the technical problem that existing monitoring technologies for the anti-corrosion coating of wind turbine towers are unable to detect the status of the anti-corrosion coating of wind turbine towers in real time with high precision.

[0006] This invention provides an online monitoring method for the anti-corrosion coating of wind turbine towers based on high-order ultrasonic zero-group velocity Lamb waves, the method comprising:

[0007] Obtain tower data of the target wind turbine tower, and determine the higher-order ZGV mode corresponding to the target wind turbine tower based on the tower data;

[0008] Based on the tower data and the higher-order ZGV mode, the deployment position of the sensor array on the target wind turbine tower and the excitation frequency that satisfies the higher-order ZGV mode are determined.

[0009] The sensor array sends an excitation signal that satisfies the excitation frequency of the higher-order ZGV mode to the target wind turbine tower, and collects the ZGV frequency and ZGV amplitude of each sensor in the sensor array at the position of the target wind turbine tower.

[0010] The frequency and amplitude corresponding to the preset peeling coating and the preset good coating are determined by using a preset ZGV-coating thickness parameter association model associated with the target wind turbine tower;

[0011] Based on the preset frequencies and amplitudes corresponding to the peeling coating and the preset good coating, the coating status of the target wind turbine tower at different sensor positions is determined by the ZGV frequency and ZGV amplitude corresponding to each sensor.

[0012] Optionally, the step of acquiring the tower data of the target wind turbine tower and determining the higher-order ZGV mode corresponding to the target wind turbine tower based on the tower data includes:

[0013] Obtain tower data of the target wind turbine tower, and construct the dispersion curve of the target wind turbine tower based on the tower data;

[0014] The zero group velocity point with a slope of 0 is determined from the dispersion curve, thereby determining the higher-order ZGV mode of the target wind turbine tower.

[0015] Optionally, the step of sending an excitation signal satisfying the excitation frequency of the higher-order ZGV mode to the target wind turbine tower through the sensor array, and acquiring the ZGV frequency and ZGV amplitude of each sensor in the sensor array at the target wind turbine tower location, includes:

[0016] An excitation signal is generated by a signal generator and sent by the sensor array to the target wind turbine tower, which is at an excitation frequency that satisfies the higher-order ZGV mode.

[0017] Lamb wave signals at different locations on the target wind turbine tower are collected using each sensor in the sensor array.

[0018] The ZGV frequency and ZGV amplitude of each sensor in the sensor array are obtained by performing a Fourier transform on the Lamb wave signal.

[0019] Optionally, the process of obtaining the preset ZGV-coating thickness parameter correlation model includes:

[0020] Uniform coating samples with different thickness gradients associated with the target wind turbine tower were constructed, and Lamb wave signals of uniform coating samples with different thickness gradients were collected. Fourier transform was performed on the Lamb wave signals of uniform coating samples with different thickness gradients to extract the frequency and amplitude corresponding to different thicknesses.

[0021] The ZGV frequency-coating thickness parameter correlation curves were fitted according to the frequencies corresponding to different thicknesses, and a ZGV frequency-coating thickness regression equation was established; the ZGV amplitude-coating thickness parameter correlation curves were fitted according to the frequencies corresponding to different thicknesses, and a ZGV amplitude-coating thickness regression equation was established.

[0022] Based on the ZGV frequency-coating thickness regression equation and the ZGV amplitude-coating thickness regression equation, a preset ZGV-coating thickness parameter correlation model is obtained.

[0023] Optionally, the step of determining the coating state of the target wind turbine tower at different sensor locations based on the preset frequencies and amplitudes corresponding to the peeling coating and the preset good coating, using the ZGV frequency and ZGV amplitude corresponding to each sensor, includes:

[0024] When the ZGV frequency corresponding to the sensor is greater than or equal to the preset frequency of the peeling coating, and the ZGV amplitude corresponding to the sensor is greater than or equal to the amplitude of the preset peeling coating, it is determined that the coating of the target wind turbine tower has peeled off at the sensor position.

[0025] When the ZGV frequency corresponding to the sensor is less than the preset frequency of the peeling coating but greater than the preset frequency of the good coating, and the ZGV amplitude corresponding to the sensor is less than the amplitude of the preset peeling coating but greater than the amplitude of the preset good coating, it is determined that the coating of the target wind turbine tower at the sensor location is thinned.

[0026] Optional, also includes:

[0027] The ZGV frequency and ZGV amplitude of each sensor in the coating thinning area of ​​the target wind turbine tower are input into the preset ZGV-coating thickness parameter association model; the first thickness is calculated based on the corresponding ZGV frequency through the ZGV frequency-coating thickness regression equation, and the second thickness is calculated based on the corresponding ZGV amplitude through the ZGV amplitude-coating thickness regression equation.

[0028] Based on preset frequency weights and preset amplitude weights, the coating thickness of each sensor in the coating thinning region of the target wind turbine tower is determined according to the first thickness and the second thickness.

[0029] A thickness distribution map of the coating thinning area of ​​the target wind turbine tower is generated based on the coating thickness corresponding to each sensor in the coating thinning area of ​​all the target wind turbine towers.

[0030] The preset frequency weight is set to 0.3, and the preset amplitude weight is set to 0.7; the calculation method for the coating thickness corresponding to each sensor in the coating thinning area of ​​the target wind turbine tower is as follows:

[0031]

[0032] In the formula: The coating thickness corresponding to the sensor in the coating thinning area of ​​the target wind turbine tower. The first thickness, This is the second thickness.

[0033] Optionally, constructing uniform coating samples with different thickness gradients associated with the target wind turbine tower, and acquiring Lamb wave signals of the uniform coating samples with different thickness gradients; performing Fourier transform on the Lamb wave signals of the uniform coating samples with different thickness gradients to extract the frequencies and amplitudes corresponding to different thicknesses includes the following steps:

[0034] Construct uniform coating samples with different thickness gradients associated with the target wind turbine tower;

[0035] An excitation signal is generated by a function generator, and the excitation signal is amplified by a power amplifier. The amplified excitation signal is then transmitted to a piezoelectric ceramic sensor via a duplexer. The piezoelectric ceramic sensor amplifies the excitation signal to a frequency that satisfies the excitation frequency of the higher-order ZGV mode. This excitation signal, satisfying the excitation frequency of the higher-order ZGV mode, forms a Lamb wave with zero group velocity characteristics in a uniform coating sample with different thickness gradients. The piezoelectric ceramic sensor converts the Lamb wave into a Lamb wave electrical signal. The Lamb wave electrical signal is amplified by a preamplifier and acquired and recorded by a digital oscilloscope to obtain the Lamb wave signal.

[0036] The A-wave signal of continuous resonance in the recorded Lamb wave signal is preprocessed by Hanning window truncation and zero-padding. The preprocessed A-wave signal is then subjected to Fourier transform to extract the frequency and amplitude corresponding to different thicknesses.

[0037] The present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions, when executed by a processor, implement the steps of the online monitoring method for the anti-corrosion coating of wind turbine towers as described above.

[0038] The present invention also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps of the online monitoring method for the anti-corrosion coating of wind turbine towers as described above.

[0039] As can be seen from the above technical solutions, the present invention has the following advantages:

[0040] This invention provides an online monitoring method and related apparatus for anti-corrosion coatings of wind turbine towers based on high-order ultrasonic zero-group velocity Lamb waves. The method includes: acquiring tower data of a target wind turbine tower; determining the corresponding high-order ZGV mode of the target wind turbine tower based on the tower data; determining the deployment position of a sensor array on the target wind turbine tower and the excitation frequency that satisfies the high-order ZGV mode based on the tower data and the high-order ZGV mode; sending an excitation signal that satisfies the excitation frequency of the high-order ZGV mode to the target wind turbine tower through the sensor array; acquiring the ZGV frequency and ZGV amplitude of each sensor in the sensor array at the target wind turbine tower position; using a preset ZGV-coating thickness parameter correlation model associated with the target wind turbine tower to determine the frequency and amplitude corresponding to a preset peeling coating and a preset good coating; and judging the coating state of the target wind turbine tower at different sensor positions based on the frequency and amplitude corresponding to the preset peeling coating and the preset good coating, using the ZGV frequency and ZGV amplitude corresponding to each sensor. This invention combines the high sensitivity and anti-interference characteristics of high-order ZGV modes, and uses a sensor array to collect the ZGV frequency and ZGV amplitude of the wind turbine tower in real time. Based on the ZGV frequency and ZGV amplitude, the coating condition of the wind turbine tower is determined, thereby solving the technical problem that existing monitoring technologies for the anti-corrosion coating of wind turbine towers are difficult to detect the anti-corrosion coating condition of wind turbine towers in real time with high precision. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating the steps of an online monitoring method for anti-corrosion coatings of wind turbine towers based on high-order ultrasonic zero-group velocity Lamb waves, provided in this embodiment of the invention.

[0043] Figure 2A technical execution flowchart of an online monitoring method for anti-corrosion coatings of wind turbine towers based on high-order ultrasonic zero-group velocity Lamb waves is provided in this embodiment of the invention.

[0044] Figure 3 A schematic diagram of a module monitoring method for online monitoring of anti-corrosion coatings of wind turbine towers based on high-order ultrasonic zero-group velocity Lamb waves, provided in an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the wavenumber-frequency dispersion curve of a wind turbine tower provided in an embodiment of the present invention;

[0046] Figure 5 (a) is a time-domain diagram of the excitation signal provided in an embodiment of the present invention. Figure 5 (b) is a spectrum diagram of the excitation signal provided in an embodiment of the present invention;

[0047] Figure 6 A two-dimensional simulation model diagram of the ZGV-coating thickness parameter correlation model constructed according to the embodiments of the present invention;

[0048] Figure 7 (a) is a schematic diagram of the A-scan signals received under different coating thickness models provided in the embodiments of the present invention. Figure 7 (b) is a spectrum of the A-scan signal received under different coating thickness models provided in the embodiments of the present invention;

[0049] Figure 8 (a) is a fitting graph of the A2 / A3-ZGV mode-frequency versus coating thickness provided in an embodiment of the present invention. Figure 8 (b) is a fitting relationship diagram of A2 / A3-ZGV mode-amplitude and coating thickness provided in the embodiment of the present invention;

[0050] Figure 9 This is a schematic diagram of a coating thickness reduction model provided in an embodiment of the present invention;

[0051] Figure 10 This is a schematic diagram of the spectrum signal collected by the deployed sensor array provided in an embodiment of the present invention;

[0052] Figure 11 A visual coating thickness distribution diagram provided for embodiments of the present invention. Detailed Implementation

[0053] This invention provides an online monitoring method and related device for the anti-corrosion coating of wind turbine towers based on high-order ultrasonic zero-group velocity Lamb waves, which solves the technical problem that existing monitoring technologies for the anti-corrosion coating of wind turbine towers are unable to detect the status of the anti-corrosion coating of wind turbine towers in real time with high precision.

[0054] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0055] Please see Figure 1 and Figure 2 This invention provides an online monitoring method for anti-corrosion coatings of wind turbine towers based on high-order ultrasonic zero-group-velocity Lamb waves. This method combines the high sensitivity and anti-interference characteristics of high-order ZGV modes to achieve real-time and accurate detection of coating damage, and is particularly suitable for corrosion protection monitoring of tower structures with large curvature radii. The method includes:

[0056] Step 101: Obtain the tower data of the target wind turbine tower and determine the higher-order ZGV mode corresponding to the target wind turbine tower based on the tower data.

[0057] It should be noted that the specific data for wind turbine towers include: substrate wall thickness and substrate material. Higher-order zero group velocity (ZGV) modes primarily depend on the wall thickness and material properties of the tower substrate, and are less affected by the coating. Therefore, for the same tower substrate, the frequencies of the higher-order ZGV modes under that substrate can be obtained. The coating only has one effect on the higher-order ZGV modes, causing the frequency to fluctuate within a small range. Since the bandwidth of the sensor is generally large, it can cover this frequency range well. Therefore, for tower substrates with the same wall thickness and material parameters, when performing coating testing on the target wind turbine tower, it is only necessary to confirm the higher-order ZGV modes once to ensure that zero group velocity resonance can be achieved at the natural frequency, thereby generating higher-order ZGV modes in the structure.

[0058] This step specifically includes steps S11 to S12:

[0059] Step S11: Obtain the tower data of the target wind turbine tower and construct the dispersion curve of the target wind turbine tower based on the tower data.

[0060] In this embodiment, the dispersion curve of the target wind turbine tower can be solved using the semi-analytical finite element software SAFEDC. Since the ZGV mode is a standing wave and the wind turbine tower has a large radius of curvature, it is only necessary to solve the wavenumber-frequency dispersion curve of the two-dimensional plate-shaped cross-section of the tower.

[0061] Step S12: Determine the zero group velocity point with a slope of 0 from the dispersion curve, thereby determining the higher-order ZGV mode of the target wind turbine tower.

[0062] Since the group velocity of the ZGV mode is 0, that is: ,in For group velocity, Angular frequency, For wave number, The frequency is used to find the ZGV point where the slope of the wavenumber-frequency dispersion curve is 0. Since the sensitivity of higher-order ZGV modes is higher than that of lower-order ZGV modes, the most suitable higher-order ZGV mode can be selected according to actual needs and sensor frequency to ensure that energy is trapped in the area of ​​the coating to be detected.

[0063] Step 102: Determine the deployment location of the sensor array on the target wind turbine tower and the excitation frequency that satisfies the higher-order ZGV mode based on tower data and higher-order ZGV modes.

[0064] Understandably, the sensor array consists of multiple sensors. Based on tower data, the deployment location of the sensor array on the target wind turbine tower can be determined, such as a ring or spiral deployment in the corroded edge region of the coating. Based on higher-order ZGV modes, the excitation frequency that the sensor array excites to satisfy the higher-order ZGV modes can be determined. In practical applications, based on tower data and higher-order ZGV modes, a weather-resistant sensor array satisfying a specified ZGV mode frequency can be deployed inside key corrosion areas on the tower surface (such as the marine atmospheric zone or marine splash zone).

[0065] Step 103: Send an excitation signal that satisfies the excitation frequency of the higher-order ZGV mode to the target wind turbine tower through the sensor array, and collect the ZGV frequency and ZGV amplitude of each sensor in the sensor array at the position of the target wind turbine tower.

[0066] It is understandable that non-destructive high-precision detection of the coating of the target wind turbine tower can be performed by using the excitation signal of the excitation frequency of the high-order ZGV mode. The structural changes of the wind turbine tower coating can be detected with high precision by using the collected ZGV frequency and ZGV amplitude. It has the characteristics of strong anti-interference and high sensitivity.

[0067] This step specifically includes steps S21 to S23:

[0068] Step S21: An excitation signal is generated by a signal generator and sent by the sensor array to the target wind turbine tower, and the excitation signal satisfies the excitation frequency of the higher-order ZGV mode.

[0069] In this embodiment, the excitation signal generated by the signal generator is specifically a narrow-bandwidth pulse signal. The signal generator is electrically connected to the sensor array, and the sensor array excites the excitation signal into an excitation signal that satisfies the excitation frequency of the higher-order ZGV mode, so as to suppress interference from non-ZGV modes and improve the simplicity of subsequent signal processing.

[0070] Step S22: Use the sensors in the sensor array to collect Lamb wave signals at different locations on the target wind turbine tower.

[0071] It is understandable that after the sensor array sends an excitation signal with an excitation frequency that satisfies the higher-order ZGV mode to the target wind turbine tower, the excitation signal forms a Lamb wave with zero group velocity characteristics in the target wind turbine tower.

[0072] To improve signal processing efficiency, please refer to [link / reference]. Figure 3 Multiple Lamb wave signals can be transmitted to the data acquisition module for preprocessing and other operations; the data acquisition module can acquire multiple Lamb wave signals in parallel and then upload them to the processing terminal.

[0073] Step S23: Perform Fourier transform on the Lamb wave signal to extract the ZGV frequency and ZGV amplitude corresponding to each sensor in the sensor array.

[0074] Please see Figure 3 The processing terminal performs a Fourier Transform (FT) on the Lamb wave signal to extract the ZGV frequency and ZGV amplitude corresponding to each sensor in the sensor array.

[0075] Step 104: Use the preset ZGV-coating thickness parameter association model associated with the target wind turbine tower to determine the frequency and amplitude corresponding to the preset peeling coating and the preset good coating.

[0076] In this embodiment, the process of obtaining the preset ZGV-coating thickness parameter correlation model includes steps S31~S33:

[0077] Step S31: Construct uniform coating samples with different thickness gradients associated with the target wind turbine tower, and collect Lamb wave signals of uniform coating samples with different thickness gradients; perform Fourier transform on the Lamb wave signals of uniform coating samples with different thickness gradients to extract the frequency and amplitude corresponding to different thicknesses.

[0078] In this step, after determining the higher-order ZGV mode, calibration experiments can be performed on uniform coating samples with different thickness gradients associated with the target wind turbine tower. The relationship between the frequency and amplitude of the uniform coating sample with known thickness gradient and the ZGV mode is measured. That is, Fourier transform is performed on the Lamb wave signal obtained in different coating thickness gradients, and then the frequency and amplitude of the resonant signal in the spectrum are extracted.

[0079] More specifically, the experimental setup for the calibration experiment includes: a function generator, a piezoelectric ceramic sensor, a power amplifier, a duplexer, a preamplifier, and a digital oscilloscope. The duplexer, as the core switching device, ensures that the piezoelectric ceramic sensor completes a full self-transmission and self-reception detection cycle under single-port conditions.

[0080] First, an excitation signal is generated by a function generator, and the excitation signal is amplified by a power amplifier. Then, the amplified excitation signal is transmitted to the sensor array via a duplexer. Next, the piezoelectric ceramic sensor realizes electromechanical energy conversion, exciting the electrical signal into a mechanical vibration wave of a specific frequency. That is, the piezoelectric ceramic sensor excites the excitation signal into an excitation signal that satisfies the excitation frequency of the higher-order ZGV mode. The excitation signal forms a Lamb wave with zero group velocity characteristics in the target wind turbine tower. The piezoelectric ceramic sensor converts the Lamb wave into a Lamb wave electrical signal through the inverse effect of the piezoelectric ceramic. Finally, the Lamb wave electrical signal is amplified by a preamplifier and acquired and recorded by a digital oscilloscope to obtain the Lamb wave signal.

[0081] The A-wave signal of continuous resonance in the recorded Lamb wave signal is preprocessed by Hanning window truncation and zero-padding. The preprocessed A-wave signal is then subjected to Fourier transform to extract the frequency and amplitude corresponding to different thicknesses.

[0082] Step S32: Fit the ZGV frequency-coating thickness parameter correlation curve according to the frequency corresponding to different thicknesses, and establish the ZGV frequency-coating thickness regression equation; Fit the ZGV amplitude-coating thickness parameter correlation curve according to the frequency corresponding to different thicknesses, and establish the ZGV amplitude-coating thickness regression equation.

[0083] In this step, the correlation curves between the frequency and amplitude of the resonance peak corresponding to different thicknesses are fitted to obtain the corresponding coating thickness and resonance peak frequency and amplitude, thereby fitting the ZGV frequency-coating thickness regression equation and the ZGV amplitude-coating thickness regression equation.

[0084] Step S33: Based on the ZGV frequency-coating thickness regression equation and the ZGV amplitude-coating thickness regression equation, a preset ZGV-coating thickness parameter correlation model is obtained.

[0085] In this embodiment, the dispersion equation is solved using the semi-analytical finite element software (SAFEDC) to plot the wavenumber-frequency dispersion curve. The zero group velocity point with a slope of 0 is found in the curve, and a high-order ZGV mode that meets the actual requirements, is sensitive to thickness, and has energy localization is selected. The monotonic response relationship between coating thickness and ZGV resonant frequency and amplitude is experimentally calibrated, and the regression equations of frequency-coating thickness and amplitude-coating thickness are fitted to achieve rapid and high-precision non-destructive testing of coating thickness.

[0086] After the ZGV-coating thickness parameter correlation model is constructed, the ZGV frequency and amplitude of all coating thicknesses included in both intact and detached coatings can be obtained. Based on experimental calibration, the ZGV frequency and amplitude of the detached coating are set as follows: and The ZGV frequency and amplitude of the intact coating were set to [values ​​to be inserted here]. and It is understandable that, under different tower data characteristics, the calibrated ZGV frequency of the peeling coating will vary. and amplitude and the ZGV frequency of the intact coating and amplitude There should be differences.

[0087] Step 105: Based on the preset frequencies and amplitudes corresponding to the peeling coating and the preset good coating, determine the coating status of the target wind turbine tower at different sensor positions by using the ZGV frequency and ZGV amplitude corresponding to each sensor.

[0088] The frequencies and amplitudes corresponding to the preset peeling coating and the preset good coating (i.e.) and , and The data is stored in the processing terminal, which then determines the coating status of the target wind turbine tower at different sensor locations based on the ZGV frequency and ZGV amplitude corresponding to each sensor.

[0089] Specifically, this step includes steps S41 to S42:

[0090] Step S41: When the ZGV frequency corresponding to the sensor is greater than or equal to the preset frequency of the peeling coating, and the ZGV amplitude corresponding to the sensor is greater than or equal to the preset amplitude of the peeling coating, it is determined that the coating of the target wind turbine tower has peeled off at the sensor position.

[0091] Step S42: When the ZGV frequency corresponding to the sensor is less than the preset frequency of the peeling coating and greater than the preset frequency of the good coating, and the ZGV amplitude corresponding to the sensor is less than the preset amplitude of the peeling coating and greater than the preset amplitude of the good coating, it is determined that the coating of the target wind turbine tower at the sensor position is thinned.

[0092] It is understandable that when the measured ZGV frequency... and ZGV amplitude Simultaneously satisfy: This is considered coating peeling; if the ZGV frequency and ZGV amplitude Simultaneously satisfy: If the coating is thinned, it is considered thinner; otherwise, the coating is considered good. If the coating is determined to be peeling or good, there is no need to reconstruct the coating thinning profile.

[0093] In this embodiment, a narrowband pulse is excited by a weather resistance sensor array to obtain a pure ZGV mode. The signal is collected in real time and the ZGV feature parameters are extracted by Fourier transform. Then, the coating peeling or thinning state is determined according to a preset threshold, thereby enabling high-precision detection of the tower coating state in real time.

[0094] It should be noted that, in order to further determine the minute thickness reduction state of the wind turbine tower coating, after determining that the coating has thinned, it is also necessary to determine the specific state of the coating thinning. Therefore, this method also includes the following steps S51~S53:

[0095] Step S51: Input the ZGV frequency and ZGV amplitude corresponding to each sensor in the coating thinning area of ​​the target wind turbine tower into the preset ZGV-coating thickness parameter association model; calculate the first thickness based on the ZGV frequency through the ZGV frequency-coating thickness regression equation, and calculate the second thickness based on the ZGV amplitude through the ZGV amplitude-coating thickness regression equation.

[0096] Please see Figure 3 During the data signal transmission phase, the data acquisition module uploads the Lamb wave signal to the processing terminal and the cloud server via the wireless communication module. Simultaneously, the cloud server stores a preset ZGV-coating thickness parameter correlation model. Therefore, after determining the coating thinning area, the cloud server can be used to perform coating thickness analysis and calculation on that area, thereby improving data processing efficiency.

[0097] In the cloud server, the ZGV frequency is input into the ZGV frequency-coating thickness regression equation to calculate the first thickness. The ZGV amplitude is input into the ZGV amplitude-coating thickness regression equation to calculate the second thickness. .

[0098] Step S52: Based on preset frequency weights and preset amplitude weights, determine the coating thickness corresponding to each sensor in the coating thinning area of ​​the first and second thickness target wind turbine towers.

[0099] It should be noted that the resonant frequency and signal amplitude of the ZGV mode exhibit high-resolution monotonic response characteristics to changes in coating thickness. Specifically, the frequency of the ZGV mode increases as the tower coating thickness decreases; similarly, the amplitude of the ZGV mode also increases as the tower coating thickness decreases. Although the latter is much more sensitive to coating changes than the former, the ZGV amplitude is relatively more susceptible to edge scattering, thus its anti-interference capability is not as good as the former.

[0100] Therefore, this embodiment sets preset frequency weights and preset amplitude weights, and calculates the estimated final coating thickness at the sensor location using a weighted average method. With the preset frequency weight set to 0.3 and the preset amplitude weight set to 0.7, the calculation method for the coating thickness corresponding to each sensor in the coating thinning area of ​​the target wind turbine tower is expressed as follows:

[0101]

[0102] In this embodiment, the coating thickness is inverted through a regression equation, and then a two-parameter weighted fusion algorithm (weight coefficients: frequency 0.3, amplitude 0.7) is used to calculate the coating thickness. Considering that wave scattering will occur at the edge of the thinned coating, the sensor deployed in the thinned edge area uses the ZGV frequency with strong anti-interference capability to calculate the thickness value, while giving full play to the high sensitivity advantage of the amplitude parameter, thereby improving the detection accuracy of the inverted coating thickness.

[0103] Step S53: Generate a thickness distribution map of the coating thinning region of the target wind turbine tower based on the coating thickness corresponding to each sensor in the coating thinning region of all target wind turbine towers. The coating thickness distribution map can be reconstructed using spatial interpolation techniques.

[0104] This invention provides an online monitoring method for the anti-corrosion coating of wind turbine towers using high-order ultrasonic zero-group-velocity Lamb waves. Combining the high sensitivity and anti-interference characteristics of high-order ZGV modes, this invention can detect minute thickness reduction and local peeling of the tower coating in real time with high precision. It is especially suitable for corrosion protection monitoring of tower structures with large curvature radii in marine salt spray corrosion environments.

[0105] To facilitate understanding, this invention also provides a simulation verification example of an online monitoring method for anti-corrosion coatings of wind turbine towers based on high-order ultrasonic zero-group-velocity Lamb waves.

[0106] This example uses the COMSOL Multiphysics commercial computing platform to build an analysis model for thinning of the anti-corrosion coating on wind turbine towers. Given the inherent limitations of three-dimensional full-scale modeling, such as high computational resource consumption and low solution efficiency, a two-dimensional constitutive model based on solid mechanics is used for dimensional optimization.

[0107] The key geometric parameters of the tower are set as follows: outer diameter of the steel base is 800mm, inner diameter is 780mm, wall thickness of the steel base is 10mm, diameter-to-thickness ratio is 80, and nominal thickness of the complete anti-corrosion coating is 100mm. The material constitutive parameters strictly adhere to engineering specifications: the substrate uses Q355B low-alloy high-strength steel, and the protective system uses polyurethane-based composite materials. It is worth noting that the proposed implementation example only analyzes the topcoat material, as topcoats are generally the first to be corroded by the marine environment during use.

[0108] 1) The wavenumber-frequency dispersion curve was plotted using the semi-analytical finite element software SAFEDC, and the ZGV mode was selected:

[0109] The SAFEDC software was used to solve the dispersion equation based on the material properties and thickness of the steel base and polyurethane topcoat of the tower, in order to plot the required wavenumber-frequency dispersion curve, as shown in the figure. Figure 4 As shown. Specific material parameters are as follows: Q335B steel material parameters: density... It is 7.85 g / cm³ 3 Elastic modulus 210 GPa and Poisson's ratio The value is 0.3; material parameters of polyurethane: density 1.5g / cm 3 Elastic modulus 2.2 GPa and Poisson's ratio It is 0.4.

[0110] The ZGV mode is found by identifying the point with a slope of 0 in the dispersion curve. Figure 4 As can be seen, there are two ZGV points: the S1 / S2-ZGV mode and the A2 / A3-ZGV mode. The S1 / S2-ZGV mode is the lowest order mode; modes of higher order than S1 / S2-ZGV are called higher-order modes. Generally, higher-order ZGV modes have stronger detection capabilities. Therefore, the A2 / A3-ZGV mode is selected in this example.

[0111] A narrow-bandwidth pulse was used as the excitation signal to suppress interference from non-ZGV modes and ensure the simplicity of subsequent signal processing. The excitation signal used a 30-cycle Hanning window modulated sinusoidal pulse with an excitation frequency of 500 kHz, such as... Figure 5 As shown.

[0112] 2) Construct a ZGV-coating thickness parameter correlation model and fit the regression equation:

[0113] To further establish the mapping relationship between coating thickness and ZGV modes, a value of 0 was set. -100 Step size is 20 The coating thickness is used to construct the COMSOL model for the ZGV-coating thickness parameter correlation model, as shown below. Figure 6 As shown, the model uses a point source excitation method to simulate the self-generating and self-receiving working mode of an actual piezoelectric sensor.

[0114] Subsequently, the relationship between the frequency and amplitude of the ZGV mode was measured for a uniformly coated sample with a known thickness gradient. The normal displacement of the receiving point was extracted as the received signal, with the coating thickness starting from 0. With 20 Step size increased to 100 The received Lamb wave signal / A-scan signal is as follows Figure 7 As shown in (a), the first wave packet in the A-scan signal is the initiating wave, which is not of interest. The focus is on the A-wave that continues to resonate afterward. This continuously resonating signal is the excited A2 / A3-ZGV mode. However, it is difficult to see the difference in signal under different coating thicknesses in the time domain. Only a slight time shift phenomenon and amplitude attenuation trend can be observed.

[0115] To extract the frequency domain features of the ZGV modes, 60 -150 After the resonant signal is preprocessed by Hanning window truncation and zero-padding, a fast Fourier transform is performed to obtain the following result: Figure 7 The amplitude spectrum is shown in (b). Spectral analysis reveals that the A2 / A3-ZGV mode exhibits a significant thickness dependence: when the coating thickness increases from 100... Decrease to 0 At that time, the characteristic frequency increased from 480.371 kHz to 479.297 kHz, while the amplitude increased from... Rise to .

[0116] Subsequently, the frequencies and amplitudes of the A2 / A3-ZGV modes at each coating thickness were extracted, as shown in Table 1. Then, linear regression models were established for coating thickness and ZGV frequency and amplitude, respectively.

[0117] Table 1. ZGV frequency and ZGV amplitude measured for uniformly coated samples at different coating thicknesses.

[0118]

[0119] Based on Table 1, the ZGV frequency of the obtained detached coating was calibrated according to the state of the uniformly coated sample. The ZGV amplitude of the peeled coating is 480.371 kHz. for ZGV frequency of intact coating for ZGV amplitude of intact coating for .

[0120] The fitting results show that, for coating thickness and ZGV frequency, the fitted regression equation is: For the relationship between coating thickness and ZGV amplitude, the fitted regression equation is: .in Used to measure the goodness of fit of a regression model to data. For example... Figure 8 As shown, the frequency and amplitude characteristics of the ZGV modes both exhibit a significant linear relationship with the coating thickness, with the amplitude parameter showing a stronger linear correlation (i.e., the ZGV amplitude-coating thickness regression equation has a better fit than the ZGV frequency-coating thickness regression equation). The value increased by 0.39%.

[0121] 3) Visualization of coating thinning thickness distribution:

[0122] After establishing the parametric correlation model, a finite element model of the tower surface coating corrosion condition was constructed to simulate a case of coating thinning in a real environment. The steel substrate dimensions of the tower are the same as before, and the coating thickness after thinning is 70 mm. Its circumferential dimension is 40mm. An array of sensors with a total length of 60mm and a spacing of 2mm is arranged inside the tower to ensure full coverage monitoring of the coating thinning area. The constructed model is as follows: Figure 9 As shown.

[0123] The spectrum of the A-scan signal received by the sensor array located in the coating thickness reduction region is as follows: Figure 10 As shown. To gain a clearer understanding of the coating condition, extract... Figure 10 A2 / A3-ZGV mode frequencies and amplitude The results are shown in Table 2.

[0124] Table 2. Frequency of the sensor array in A2 / A3-ZGV modes and amplitude

[0125]

[0126] It can be seen that the frequencies of A2 / A3-ZGV in the spectrum signal received by the sensor are... and amplitude All conditions are met: Therefore, it was determined to be a coating thickness reduction situation. Subsequently, the frequencies of the extracted A2 / A3-ZGV modes were... and amplitude Substitute the values ​​into the frequency-coating thickness regression equation and the amplitude-coating thickness regression equation respectively to solve for the coating thickness, and then perform a weighted average. The thickness of the fused coating is calculated, but the reconstructed coating thickness for sensors deployed at the thinning edge is determined by the ZGV frequency, which has strong anti-interference capability, in order to reduce the distortion rate at the thinning edge.

[0127] Finally, a visualized coating thickness distribution map was obtained, as shown in the following figure. Figure 11 As shown, the approximate shape of the reconstructed coating thickness profile is similar to the geometry in the model, clearly indicating a reduction in coating thickness. From Figure 11 As can be seen, the main errors occur at the coating thinning edge, due to wave scattering and mode transition at the edge. Quantitative error analysis was conducted on the non-edge scattering effect region. Experimental data shows that in the coating thinning region, the relative error between the coating thickness inverted by this method and the preset model parameters ranges from 1.31% to 9.23%. This invention uses the ZGV frequency, which has strong anti-interference capabilities, to calculate the coating thickness, effectively reducing the measurement error in the coating thinning region. It is evident that the non-destructive testing technology provided by this invention has good quantitative assessment capabilities for coating thickness degradation, significantly improving on-site testing efficiency while ensuring detection accuracy. In summary, this method, through the fusion of ZGV Lamb waves and coating thickness features, provides an innovative solution for the health monitoring of wind turbine tower structures, demonstrating significant engineering application value, particularly in the rapid assessment of coating thickness in complex components with large curvature radii.

[0128] The present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions, when executed by a processor, implement the steps of any of the above-mentioned online monitoring methods for the anti-corrosion coating of wind turbine towers.

[0129] The present invention also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps of any of the above-mentioned online monitoring methods for anti-corrosion coatings of wind turbine towers.

[0130] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for online monitoring of anti-corrosion coatings on wind turbine towers based on high-order ultrasonic zero-group-velocity Lamb waves, characterized in that, The method includes: Obtain tower data of the target wind turbine tower, and determine the higher-order ZGV mode corresponding to the target wind turbine tower based on the tower data; Based on the tower data and the higher-order ZGV mode, the deployment position of the sensor array on the target wind turbine tower and the excitation frequency that satisfies the higher-order ZGV mode are determined. The sensor array sends an excitation signal that satisfies the excitation frequency of the higher-order ZGV mode to the target wind turbine tower, and collects the ZGV frequency and ZGV amplitude of each sensor in the sensor array at the position of the target wind turbine tower. The frequency and amplitude corresponding to the preset peeling coating and the preset good coating are determined by using a preset ZGV-coating thickness parameter association model associated with the target wind turbine tower; Based on the preset frequencies and amplitudes corresponding to the pre-set peeling coating and the preset good coating, the coating state of the target wind turbine tower at different sensor positions is determined by the ZGV frequency and ZGV amplitude corresponding to each sensor. The steps of acquiring tower data of the target wind turbine tower and determining the higher-order ZGV mode corresponding to the target wind turbine tower based on the tower data include: Obtain tower data of the target wind turbine tower, and construct the dispersion curve of the target wind turbine tower based on the tower data; The zero group velocity point with a slope of 0 is determined from the dispersion curve, thereby determining the higher-order ZGV mode of the target wind turbine tower; The step of sending an excitation signal with an excitation frequency satisfying the higher-order ZGV mode to the target wind turbine tower via the sensor array, and acquiring the ZGV frequency and ZGV amplitude of each sensor in the sensor array at the target wind turbine tower location, includes: An excitation signal is generated by a signal generator and sent by the sensor array to the target wind turbine tower, which is at an excitation frequency that satisfies the higher-order ZGV mode. Lamb wave signals at different locations on the target wind turbine tower are collected using each sensor in the sensor array. The ZGV frequency and ZGV amplitude of each sensor in the sensor array are obtained by performing a Fourier transform on the Lamb wave signal.

2. The online monitoring method for the anti-corrosion coating of wind turbine towers according to claim 1, characterized in that, The process of obtaining the preset ZGV-coating thickness parameter correlation model includes: Uniform coating samples with different thickness gradients associated with the target wind turbine tower were constructed, and Lamb wave signals of uniform coating samples with different thickness gradients were collected. Fourier transform was performed on the Lamb wave signals of uniform coating samples with different thickness gradients to extract the frequency and amplitude corresponding to different thicknesses. The ZGV frequency-coating thickness parameter correlation curves were fitted according to the frequencies corresponding to different thicknesses, and a ZGV frequency-coating thickness regression equation was established; the ZGV amplitude-coating thickness parameter correlation curves were fitted according to the frequencies corresponding to different thicknesses, and a ZGV amplitude-coating thickness regression equation was established. Based on the ZGV frequency-coating thickness regression equation and the ZGV amplitude-coating thickness regression equation, a preset ZGV-coating thickness parameter correlation model is obtained.

3. The online monitoring method for the anti-corrosion coating of wind turbine towers according to claim 2, characterized in that, The step of determining the coating state of the target wind turbine tower at different sensor locations based on the preset frequencies and amplitudes corresponding to the pre-set peeling coating and the pre-set good coating, using the ZGV frequency and ZGV amplitude corresponding to each sensor, includes: When the ZGV frequency corresponding to the sensor is greater than or equal to the preset frequency of the peeling coating, and the ZGV amplitude corresponding to the sensor is greater than or equal to the amplitude of the preset peeling coating, it is determined that the coating of the target wind turbine tower has peeled off at the sensor position. When the ZGV frequency corresponding to the sensor is less than the preset frequency of the peeling coating but greater than the preset frequency of the good coating, and the ZGV amplitude corresponding to the sensor is less than the amplitude of the preset peeling coating but greater than the amplitude of the preset good coating, it is determined that the coating of the target wind turbine tower at the sensor location is thinned.

4. The online monitoring method for the anti-corrosion coating of wind turbine towers according to claim 3, characterized in that, Also includes: The ZGV frequency and ZGV amplitude of each sensor in the coating thinning area of ​​the target wind turbine tower are input into the preset ZGV-coating thickness parameter association model; the first thickness is calculated based on the corresponding ZGV frequency through the ZGV frequency-coating thickness regression equation, and the second thickness is calculated based on the corresponding ZGV amplitude through the ZGV amplitude-coating thickness regression equation. Based on preset frequency weights and preset amplitude weights, the coating thickness of each sensor in the coating thinning region of the target wind turbine tower is determined according to the first thickness and the second thickness. A thickness distribution map of the coating thinning area of ​​the target wind turbine tower is generated based on the coating thickness corresponding to each sensor in the coating thinning area of ​​all the target wind turbine towers.

5. The online monitoring method for the anti-corrosion coating of wind turbine towers according to claim 4, characterized in that, The preset frequency weight is set to 0.3, and the preset amplitude weight is set to 0.7; the calculation method for the coating thickness corresponding to each sensor in the coating thinning area of ​​the target wind turbine tower is as follows: ； In the formula: The coating thickness corresponding to the sensor in the coating thinning area of ​​the target wind turbine tower. The first thickness, This is the second thickness.

6. The online monitoring method for the anti-corrosion coating of wind turbine towers according to claim 2, characterized in that, The process involves constructing uniform coating samples with different thickness gradients associated with the target wind turbine tower, and acquiring Lamb wave signals of the uniform coating samples with different thickness gradients. The steps for performing Fourier transform on the Lamb wave signals of uniformly coated samples with different thickness gradients to extract the frequencies and amplitudes corresponding to different thicknesses include: Construct uniform coating samples with different thickness gradients associated with the target wind turbine tower; An excitation signal is generated by a function generator, and the excitation signal is amplified by a power amplifier. The amplified excitation signal is then transmitted to a piezoelectric ceramic sensor via a duplexer. The piezoelectric ceramic sensor amplifies the excitation signal to a frequency that satisfies the excitation frequency of the higher-order ZGV mode. This excitation signal, satisfying the excitation frequency of the higher-order ZGV mode, forms a Lamb wave with zero group velocity characteristics in a uniform coating sample with different thickness gradients. The piezoelectric ceramic sensor converts the Lamb wave into a Lamb wave electrical signal. The Lamb wave electrical signal is amplified by a preamplifier and acquired and recorded by a digital oscilloscope to obtain the Lamb wave signal. The A-wave signal of continuous resonance in the recorded Lamb wave signal is preprocessed by Hanning window truncation and zero-padding. The preprocessed A-wave signal is then subjected to Fourier transform to extract the frequency and amplitude corresponding to different thicknesses.

7. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the online monitoring method for the anti-corrosion coating of wind turbine towers as described in any one of claims 1-6.

8. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the online monitoring method for the anti-corrosion coating of wind turbine towers as described in any one of claims 1-6.

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