Offshore wind power tower health monitoring method and device and storage medium

Abstract

The invention relates to the technical field of wind power generation equipment monitoring, in particular to an offshore wind power tower health monitoring method and device and a storage medium, and the method comprises the steps: collecting environmental noise, recording the environmental noise as a time domain signal, segmenting the time domain signal, and forming multiple segments of time domain signals; all the time domain signals are preprocessed; performing passive inverse filtering on the preprocessed time domain signal to obtain a time domain impulse response matrix; and analyzing modal coupling characteristics of the tower drum, drawing a three-dimensional wave velocity distribution diagram, and performing health monitoring on the tower drum. According to the method, the environmental noise caused by sea wave impact is used as an excitation source, manual intervention is not needed, the time domain impulse response matrix is finally obtained for the collected environmental noise in combination with time domain, frequency domain and spatial domain characteristics, the overall modal coupling characteristics of the tower drum can be analyzed, a high-precision three-dimensional wave velocity distribution diagram can be drawn, and the method is suitable for popularization and application. Therefore, the submillimeter-level detection precision is realized, and the false alarm rate is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation equipment monitoring, and in particular to a method and device for monitoring the health of an offshore wind power tower, and a storage medium. Background Art

[0002] Offshore wind power is an important part of renewable energy, but the offshore environment is complex. The impact and corrosion of tides and the impact of offshore wind loads will affect the operation of wind power generation systems. Therefore, it is necessary to conduct online monitoring of offshore wind power systems. The wind turbine tower is the supporting structure of the wind power generation system and also the structure that is in direct contact with seawater. The stability of its structure directly affects the normal operation of the wind power generation system.

[0003] In the existing technology, the health detection of offshore wind turbine tower structures mainly adopts active guided wave technology, such as acoustic emission monitoring, ultrasonic monitoring, etc., or adopts high-power telescope monitoring, thermal imaging monitoring, etc. This method is limited by manual experience, equipment accuracy and environmental influence, and the effect is not good in actual use, which is mainly reflected in the following aspects: 1) Dependence on stimulus sources restricts long-term monitoring effectiveness: Active waveguide technology generally relies on piezoelectric ceramic arrays to generate artificial excitation signals, which has low energy conversion efficiency. Traditional solutions require high-power power modules, which are difficult to meet the off-grid power supply needs of offshore platforms.

[0004] 2) Low-density sensor layout limits damage identification accuracy: Low-density sensor layout results in insufficient spatial resolution and is unable to detect microcracks. Fiber Bragg grating sensors can achieve high-density layout, but existing technologies mostly use single axial strain monitoring.

[0005] 3) Single-mode analysis is susceptible to environmental noise interference: Traditional methods rely on a single guided wave mode for damage diagnosis, ignoring the multi-modal coupling effect and resulting in a high false alarm rate.

[0006] Therefore, it is necessary to provide a method and device for monitoring the health of offshore wind turbine towers based on passive guided wave imaging, and a storage medium. Summary of the invention

[0007] In order to solve the above technical problems existing in the prior art, the present invention provides a method and device for monitoring the health of an offshore wind power tower, and a storage medium.

[0008] To achieve the above object, the technical solution of the present invention is as follows: In a first aspect, the present invention provides a method for monitoring the health of an offshore wind turbine tower, comprising: S1. Use two or more sensors to synchronously collect environmental noise, record the environmental noise as a time domain signal, and segment the time domain signal to form multiple time domain signals; S2, preprocessing all time domain signals; S3, passively inverse filtering the preprocessed time domain signal, specifically including: S301, perform Fourier transform on each time domain signal after preprocessing to obtain a frequency domain matrix ; S302, performing singular value decomposition on each frequency in the frequency domain matrix to obtain a singular value matrix; S303, performing regularization processing on the singular value matrix; S304, reconstructing the frequency domain impulse response matrix, and obtaining the time domain impulse response matrix by inverse Fourier transform; S4. Analyze the modal coupling characteristics of the tower based on the time domain impulse response matrix.

[0009] Furthermore, the collected environmental noise has a duration of 10 s and a sampling frequency of 2.5 MHz. After being saved as a time domain signal, it is segmented into segments with a duration of 0.1 s each.

[0010] Furthermore, the number of sensors used in step S1 is 90. According to the position of the tower in the seawater and the division of the tower into full immersion zone, tidal range zone and splash zone, 30 sensors are set in each zone.

[0011] Furthermore, in step S2, all time domain signals are preprocessed, specifically including: downsampling, filtering and normalizing the time domain signals in sequence.

[0012] Furthermore, the downsampling is specifically as follows: the sampling frequency is 100KHz, an anti-aliasing FIR filter is used for filtering, and downsampling is performed with a downsampling factor of 10.

[0013] Furthermore, the filtering is specifically: filtering using a Butterworth bandpass filter, with a passband of 20-200kHz and a stopband attenuation of >40dB.

[0014] Furthermore, the normalization is specifically as follows:

[0015] in: ;

[0016] represents the time domain signal corresponding to each sensor after downsampling and filtering, i Indicates the sensor number; represents the normalized time domain signal; is the average value of the time domain signals of all sensors, Represents the standard deviation of the time domain signals of all sensors.

[0017] Furthermore, the frequency domain matrix in step S301 for:

[0018] Get the data matrix :

[0019] in, j is an imaginary unit, Indicates k Frequency; Represents the frequency domain matrix of the first sensor in the first period, Represents the frequency domain matrix of the first sensor at the 100th time segment; Represents the frequency domain matrix of the first time segment of the 90th sensor, Represents the frequency domain matrix of the 90th sensor at the 100th time segment.

[0020] Furthermore, in step S302, singular value decomposition is performed on each frequency in the frequency domain matrix to obtain a singular value matrix; specifically, the step includes: The singular value decomposition is performed using the following formula:

[0021] in, Represents the frequency domain matrix The transposed matrix of represents the kth frequency; represents the left singular vector, represents the transpose of the left singular vector, represents the singular value matrix.

[0022] Furthermore, in step S303, the singular value matrix is ​​regularized, specifically: Setting Thresholds , such as the singular value matrix The singular values ​​in satisfy the following criteria:

[0023] Among them, the singular value matrix is a matrix whose values ​​are all zero except for the diagonal elements. The diagonal elements are singular values, and the singular values ​​are arranged in descending order; is the first singular value, is the M+1th singular value, and from the first singular value onwards, until the M value is obtained when the judgment condition is met for the first time, and then M singular values ​​are obtained; Singular values ​​that do not meet the determination condition are set to zero.

[0024] Furthermore, in step S304, the frequency domain impulse response matrix is ​​reconstructed by:

[0025] In the above formula, represents the frequency domain impulse response matrix, is the M×M identity matrix, is the pseudo-inverse matrix that retains the first M singular values.

[0026] Furthermore, the time domain impulse response matrix is:

[0027] in, represents the time domain impulse response matrix, represents the inverse Fourier transform.

[0028] Furthermore, the offshore wind turbine tower health monitoring method further includes: S5. Guided wave imaging, specifically including: S501, locate and quantify defects: According to the frequency domain impulse response matrix and the time domain impulse response matrix, draw an image, which contains the dispersion curve, identify and extract the signal propagation characteristics; objective function Indicates the phase velocity of the area where defects are detected Phase velocity relative to the defect-free region The square of the relative change in:

[0029] : The wave number of the defect area and the phase velocity Inversely proportional to ( , is the frequency of the received signal after passive inverse filtering); ; v 0 : wave number and phase velocity in defect-free region; i Number the piezoelectric sensor; S502, based on the objective function Draw the guided wave imaging diagram.

[0030] In a second aspect, the present invention further provides an offshore wind power tower health monitoring device, comprising: A sensor group is arranged on the inner wall of the tower to collect environmental noise; A signal acquisition module, connected to the sensor group, for acquiring environmental noise received by the sensor group; A receiving circuit, connected to the signal acquisition module, for receiving environmental noise and transmitting the environmental noise signal to the acquisition circuit; A collection circuit, connected to the receiving circuit, transmits the environmental noise signal to a processing center; The processing center is connected to the acquisition circuit and is used for real-time processing and storage of data. At the same time, based on the received environmental noise signal, the health status of the offshore wind turbine tower is monitored using the above-mentioned offshore wind turbine tower health monitoring method.

[0031] Furthermore, the sensor group includes 90 piezoelectric sensors, the tower is divided into three sections according to the full immersion area, the tidal range area and the splash zone, and 30 piezoelectric sensors are arranged on the inner wall of each section of the tower; and the 30 piezoelectric sensors on the inner wall of each section of the tower are arranged in an array along the circumferential direction of the tower.

[0032] Furthermore, the piezoelectric sensor is glued to the inner wall of the tower using epoxy resin.

[0033] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned offshore wind turbine tower health monitoring method is implemented.

[0034] Compared with the prior art, the present invention has the following beneficial effects: The passive waveguide imaging offshore wind power tower health monitoring method provided by the present invention uses the environmental noise caused by the impact of sea waves as the excitation source. The sea waves impact the tower to emit elastic waves, which will be received by the sensor. After signal processing, valuable high-frequency sound wave features are extracted to obtain relevant information on the occurrence of defects, corrosion and cracks. Without manual intervention, the collected environmental noise is combined with the time domain, frequency domain and space domain features to finally obtain a time domain impulse response matrix, which is conducive to analyzing the overall modal coupling characteristics of the tower and drawing a high-precision three-dimensional wave velocity distribution map, thereby achieving sub-millimeter detection accuracy and reducing the false alarm rate.

[0035] The present invention divides the tower into sections, and multiple sensors are arranged on each section. The outside of the tower covers high-risk corrosion areas (full immersion area, tidal range area, atmospheric area), and a 3×30 sensor array is arranged inside the tower, thereby realizing all-round real-time monitoring of the tower risk areas with high monitoring efficiency.

[0036] The present invention can monitor the corrosion rate, crack initiation and expansion mechanism online in real time, and uses the environmental noise caused by the impact of ocean waves as an excitation source, with low power consumption and low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The present invention is a flow chart of the method.

[0038] Figure 2 FIG. 4 is a diagram of the device architecture of the present invention.

[0039] Figure 3 It is a schematic diagram of the tower section of the present invention.

[0040] Figure 4 Schematic diagram of the installation position of the sensor group of the present invention.

[0041] Figure 5 It is a schematic diagram of the distribution of sensors in each layer of the present invention.

[0042] Reference numerals: 1. Tower, 2. Piezoelectric sensor, 3. Piezoelectric sensor connection, 4. Signal acquisition module. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention, and all other embodiments obtained by ordinary technicians in the field without making creative work are within the protection scope of the present invention.

[0044] It should be noted that, unless otherwise specifically stated, the relative arrangements of components and steps, and numerical expressions set forth in these embodiments should not be construed as limiting the scope of the present invention.

[0045] The following description of the exemplary embodiments is merely illustrative and is not intended to limit the present invention and its application or use in any sense. Techniques, methods and devices known to ordinary technicians in the relevant field may not be discussed in detail here, but where applicable, these techniques, methods and devices should be considered as part of this specification.

[0046] Embodiment 1 When an offshore wind turbine tower is subjected to stress, if it has defects, corrosion or cracks, it will emit "transient elastic waves", which will be received by the acoustic emission sensor. After signal processing, valuable high-frequency acoustic wave features can be extracted to obtain relevant information about defects, corrosion and cracks. Acoustic emission sensors are installed on offshore wind turbine towers. The damaged parts of the tower will emit elastic waves during the impact of waves and wind, which will be captured by the sensor. After data analysis and processing, the tower can be monitored.

[0047] Therefore, this embodiment provides a method for monitoring the health of an offshore wind turbine tower. Figure 1 As shown, including: S1. Use two or more sensors to synchronously collect environmental noise, record the environmental noise as a time domain signal, and segment the time domain signal to form multiple time domain signals; In this embodiment, 90 sensors are used to synchronously collect environmental noise. The 90 sensors are divided into 3 groups, each group includes 30 sensors. The 3 groups of sensors are respectively set in the tidal range area, full immersion area, and atmospheric area of ​​the tower. All sensors sample and record the environmental noise for 10 seconds at a sampling frequency of 2.5MHz, and then post-process to obtain the passive signals coupled by all sensors.

[0048] Save the continuously collected environmental noise (generated by wind and waves) as a time domain signal ,in, =1, 2, ..., 90. Divide the 10-second time domain signal into 100 segments, each 0.1s long, to avoid transient interference. Construct a data matrix , data matrix It is a 90×100-order matrix, and each column is the time domain representation of a single noise fragment:

[0049] Self-constructing matrix , express The transpose of the matrix, t Indicates time; Represents the time domain signal of the first sensor in the first period of time, It represents the time domain signal of the first sensor in the second period. It represents the time domain signal of the first sensor at the 100th time segment. It represents the time domain signal of the first period of the second sensor. It represents the time domain signal of the second sensor in the second period. It represents the time domain signal of the second sensor at the 100th time segment. It represents the time domain signal of the first period of the 90th sensor. It represents the time domain signal of the second period of the 90th sensor. It represents the time domain signal of the 90th sensor in the 100th time segment.

[0050] S2. Preprocess all time domain signals; specifically, preprocess the time domain signals in sequence by downsampling, filtering and normalizing.

[0051] Downsampling: The original sampling rate was 2.5MHz, which was too high and was reduced to =100KHz (retaining the waveguide frequency band 20–200kHz), down-sampled by a factor of 10 after using an anti-aliasing FIR filter.

[0052] Bandpass filtering: Use Butterworth bandpass filter with a passband of 20-200kHz and a stopband attenuation of >40dB to filter out low-frequency vibration and high-frequency electromagnetic noise.

[0053] Normalization: Remove the mean and normalize the signal of each channel:

[0054] in: ;

[0055] represents the time domain signal corresponding to each sensor after downsampling and filtering, i Indicates the sensor number; represents the normalized time domain signal; is the average value of the time domain signals of all sensors, Represents the standard deviation of the time domain signals of all sensors. Through normalization processing, the signal quality is improved and the algorithm stability is enhanced.

[0056] S3, passively inverse filtering the preprocessed time domain signal, specifically including: S301, perform Fourier transform on each time domain signal after preprocessing to obtain a frequency domain matrix :

[0057] Get the data matrix :

[0058] in, j is an imaginary unit, Indicates k Frequency; Represents the frequency domain matrix of the first sensor in the first period, Represents the frequency domain matrix of the first sensor at the 100th time segment; Represents the frequency domain matrix of the first time segment of the 90th sensor, Represents the frequency domain matrix of the 90th sensor at the 100th time segment.

[0059] S302, performing singular value decomposition on each frequency in the frequency domain matrix to obtain a singular value matrix; specifically comprising: The singular value decomposition is performed using the following formula:

[0060] in, Represents the frequency domain matrix The transposed matrix of represents the left singular vector, represents the transpose of the left singular vector, represents the singular value matrix.

[0061] S303, regularizing the singular value matrix; specifically: Setting Thresholds , such as the singular value matrix The singular values ​​in satisfy the following criteria:

[0062] Among them, the singular value matrix is a matrix whose values ​​are all zero except for the diagonal elements. The diagonal elements are singular values, and the singular values ​​are arranged in descending order; is the first singular value, is the M+1th singular value, and from the first singular value onwards, until the M value is obtained when the above judgment condition is met for the first time, and then M singular values ​​are obtained; Singular values ​​that do not meet the judgment criteria are set to zero.

[0063] S304, reconstructing the frequency domain impulse response matrix, and obtaining the time domain impulse response matrix by inverse Fourier transform; the specific method is:

[0064] In the above formula, represents the frequency domain impulse response matrix, is the M×M identity matrix, is the pseudo-inverse matrix that retains the first M singular values.

[0065] The time domain impulse response matrix is ​​obtained by inverse Fourier transform:

[0066] in, represents the time domain impulse response matrix, represents the inverse Fourier transform.

[0067] S4. Analyze the modal coupling characteristics of the tower based on the time domain impulse response matrix.

[0068] First, perform intra-layer correlation, and use the above steps independently for each layer to generate three impulse response matrices , , , used to monitor the corrosion condition of each layer.

[0069] Then perform cross-layer association and combine the three-layer response matrices into:

[0070] Analyze the overall modal coupling characteristics of the tower.

[0071] S5, Guided Wave Imaging S501, locate and quantify defects: Computer by The data are plotted into three graphs (one graph for each layer of signal data), and each graph plots the dispersion curves of 30 waveguides of the corresponding layer, identifies and extracts the signal propagation characteristics (phase velocity, etc.).

[0072] Objective Function Indicates the phase velocity of the area where defects are detected Phase velocity relative to the defect-free region Relative change squared:

[0073] : The wave number of the defect area and the phase velocity Inversely proportional to ( , w is the frequency of the received signal after passive inverse filtering); ; : wave number and phase velocity in defect-free region; i Number the piezoelectric sensor.

[0074] Objective Function The size of the tower is used to measure the defects: The larger it is, the more serious the defects in the tower are, thus locating and quantifying the defects.

[0075] S502, Image Generation and Visualization: First, the data is normalized to the range of [0, 1]:

[0076] Maximum , for Normalized value.

[0077] Then do the color mapping: RGB color mapping coordinates are (R, G, B); Flawless Color: : RGB color: dark blue (0, 0, 255).

[0078] Color where the defect occurs , and the red channel intensity is positively correlated with the normalized value: Red component: R =255× , RGB color: (255× , 0, 0).

[0079] In summary, the objective function of receiving the signal through the corresponding piezoelectric sensor is Changes in corrosion defects can be located and quantified.

[0080] Embodiment 2 This embodiment provides an offshore wind power tower health monitoring device, including: The sensor group is arranged on the inner wall of the tower and is used to collect environmental noise.

[0081] A signal acquisition module, connected to the sensor group, for acquiring environmental noise received by the sensor group; A receiving circuit, connected to the signal acquisition module, for receiving environmental noise and transmitting the environmental noise signal to the acquisition circuit; A collection circuit, connected to the receiving circuit, transmits the environmental noise signal to the processing center; The processing center is connected to the acquisition circuit and is used for real-time processing and storage of data. At the same time, based on the received environmental noise signal, the health status of the offshore wind turbine tower is monitored using the above-mentioned offshore wind turbine tower health monitoring method.

[0082] like Figures 2 to 5 As shown, the sensor group includes 90 piezoelectric sensors 2. The tower 1 is divided into three sections according to the full immersion area, the tidal range area and the splash zone. 30 piezoelectric sensors 2 are arranged on the inner wall of each section of the tower 1; and the 30 piezoelectric sensors 2 on the inner wall of each section of the tower 1 are arranged in an array along the circumferential direction of the tower 1. The piezoelectric sensor 2 is a circular sensor with a radius of 18 mm, which is glued to the inner wall of the tower 1 with epoxy resin. All piezoelectric sensors 2 are statistically connected to the piezoelectric sensor connection line 3, and then connected to the signal acquisition module 4.

[0083] The processing center includes a computer and FPGA. The computer establishes communication with the FPGA through a wireless transmission protocol. The FPGA, as the core processor, is responsible for real-time processing and storage management of data.

[0084] Signal acquisition module 4 is a 32-bit ADC with a sampling rate of 2.5MHz, supporting the synchronous acquisition of 90-channel signals; the FPGA chip realizes real-time signal preprocessing, including calculation acceleration: using FPGA to parallelize the passive inverse filtering algorithm and accelerate matrix operations; FPGA preprocessing (filtering, downsampling).

[0085] Embodiment 3 This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned offshore wind turbine tower health monitoring method is implemented.

[0086] The above specific implementation methods are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to examples, a person skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring the health of an offshore wind turbine tower, characterized in that: include: S1. Use two or more sensors to synchronously collect environmental noise, record the environmental noise as a time domain signal, and segment the time domain signal to form multiple time domain signals; S2, preprocessing all time domain signals; S3, passively inverse filtering the preprocessed time domain signal, specifically including: S301, perform Fourier transform on each time domain signal after preprocessing to obtain a frequency domain matrix ; S302, performing singular value decomposition on each frequency in the frequency domain matrix to obtain a singular value matrix; S303, performing regularization processing on the singular value matrix; S304, reconstructing the frequency domain impulse response matrix, and obtaining the time domain impulse response matrix by inverse Fourier transform; S4. Analyze the modal coupling characteristics of the tower based on the time domain impulse response matrix.

2. The offshore wind turbine tower health monitoring method according to claim 1, characterized in that: The collected environmental noise lasts for 10 seconds and has a sampling frequency of 2.5 MHz. After being saved as a time domain signal, it is segmented into segments with a duration of 0.1 seconds.

3. The offshore wind turbine tower health monitoring method according to claim 2, characterized in that: The number of sensors used in step S1 is 90. According to the position of the tower in the seawater and the division of the tower into full immersion zone, tidal range zone and splash zone, 30 sensors are set in each zone.

4. The offshore wind turbine tower health monitoring method according to claim 3, characterized in that: In step S2, all time domain signals are preprocessed, specifically including: downsampling, filtering and normalizing the time domain signals in sequence.

5. The offshore wind turbine tower health monitoring method according to claim 4, characterized in that: The downsampling is specifically as follows: the sampling frequency is 100 KHz, an anti-aliasing FIR filter is used for filtering, and downsampling is performed with a downsampling factor of 10.

6. The offshore wind turbine tower health monitoring method according to claim 4, characterized in that: The filtering is specifically: filtering using a Butterworth bandpass filter, with a passband of 20-200kHz and a stopband attenuation of >40dB.

7. The offshore wind turbine tower health monitoring method according to claim 4, characterized in that: The normalization is specifically as follows: in: ； represents the time domain signal corresponding to each sensor after downsampling and filtering, i Indicates the sensor number; represents the normalized time domain signal; is the average value of the time domain signals of all sensors, Represents the standard deviation of the time domain signals of all sensors.

8. The offshore wind turbine tower health monitoring method according to claim 3, characterized in that: The frequency domain matrix in step S301 for: Get the data matrix : in, j is an imaginary unit, Indicates k Frequency; Represents the frequency domain matrix of the first sensor in the first period, Represents the frequency domain matrix of the first sensor at the 100th time segment; Represents the frequency domain matrix of the first time segment of the 90th sensor, Represents the frequency domain matrix of the 90th sensor at the 100th time segment.

9. The offshore wind turbine tower health monitoring method according to claim 3, characterized in that: In step S302, singular value decomposition is performed on each frequency in the frequency domain matrix to obtain a singular value matrix; specifically, the step includes: The singular value decomposition is performed using the following formula: in, Represents the frequency domain matrix The transposed matrix of represents the kth frequency; represents the left singular vector, represents the transpose of the left singular vector, represents the singular value matrix.

10. The offshore wind turbine tower health monitoring method according to claim 9, characterized in that: In step S303, the singular value matrix is ​​regularized, specifically: Setting Thresholds , such as the singular value matrix The singular values ​​in satisfy the following criteria: Among them, the singular value matrix is a matrix whose values ​​are all zero except for the diagonal elements. The diagonal elements are singular values, and the singular values ​​are arranged in descending order; is the first singular value, is the M+1th singular value, and from the first singular value onwards, until the M value is obtained when the judgment condition is met for the first time, and then M singular values ​​are obtained; Singular values ​​that do not meet the determination condition are set to zero.

11. The offshore wind turbine tower health monitoring method according to claim 10, characterized in that: In step S304, the frequency domain impulse response matrix is ​​reconstructed, and the specific method is as follows: In the above formula, represents the frequency domain impulse response matrix, is the M×M identity matrix, is the pseudo-inverse matrix that retains the first M singular values.

12. The offshore wind turbine tower health monitoring method according to claim 11, characterized in that: The time domain impulse response matrix is: in, represents the time domain impulse response matrix, represents the inverse Fourier transform.

13. The offshore wind turbine tower health monitoring method according to claim 12, characterized in that: The offshore wind power tower health monitoring method further includes: S5. Guided wave imaging, specifically including: S501, locate and quantify defects: According to the frequency domain impulse response matrix and the time domain impulse response matrix, draw an image, which contains the dispersion curve, identify and extract the signal propagation characteristics; objective function Indicates the phase velocity of the area where defects are detected Phase velocity relative to the defect-free region The square of the relative change in : The wave number of the defect area and the phase velocity Inversely proportional; , The frequency of the received signal after passive inverse filtering; ; : wave number and phase velocity in defect-free region; i Number the piezoelectric sensor; S502, based on the objective function Draw the guided wave imaging diagram.

14. An offshore wind power tower health monitoring device, characterized in that: include: A sensor group is arranged on the inner wall of the tower to collect environmental noise; A signal acquisition module, connected to the sensor group, for acquiring environmental noise received by the sensor group; A receiving circuit, connected to the signal acquisition module, for receiving environmental noise and transmitting the environmental noise signal to the acquisition circuit; A collection circuit, connected to the receiving circuit, transmits the environmental noise signal to a processing center; A processing center is connected to the acquisition circuit and is used for real-time processing and storage of data. At the same time, based on the received environmental noise signal, the offshore wind turbine tower health monitoring method according to any one of claims 1 to 13 is used to monitor the health status of the offshore wind turbine tower.

15. The offshore wind power tower health monitoring device according to claim 14, characterized in that: The sensor group includes 90 piezoelectric sensors. The tower is divided into three sections according to the full immersion area, the tidal range area and the splash zone. 30 piezoelectric sensors are arranged on the inner wall of each section of the tower; and the 30 piezoelectric sensors on the inner wall of each section of the tower are arranged in an array along the circumferential direction of the tower.

16. The offshore wind power tower health monitoring device according to claim 15, characterized in that: The piezoelectric sensor is glued to the inner wall of the tower using epoxy resin.

17. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the offshore wind turbine tower health monitoring method according to any one of claims 1 to 13 is implemented.

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