Wind turbine blade crack detection system based on acoustic signature analysis

Abstract

The present invention relates to the field of wind turbine detection technology, and specifically to a wind turbine blade crack detection system based on acoustic feature analysis, comprising an acoustic signal acquisition module, a material fatigue state monitoring module, a signal separation and identification module, and a crack location module, wherein: the acoustic signal acquisition module acquires acoustic signals generated during blade operation; the material fatigue state monitoring module compares changes in material properties with a material damage database to determine the material fatigue state of the current blade; the signal separation and identification module distinguishes between normal operating acoustic signals and abnormal acoustic signals; and distinguishes crack signals from material fatigue signals; and the crack location module determines the crack location using a triangulation positioning algorithm. The present invention significantly improves the accuracy of abnormal signal separation through the coordinated analysis of material behavior and acoustic signals, thereby more accurately detecting crack signals.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine detection, and in particular to a wind turbine blade crack detection system based on acoustic feature analysis. Background Art

[0002] With the rapid development of wind power technology, wind turbine blades, as the core components of wind power systems, are crucial to the reliable operation of the entire wind power system in terms of operational safety and stability. However, during long-term operation, wind turbine blades are prone to cracks or other forms of damage due to the complex wind loads, gravity, and material factors. If these cracks are not detected and treated in a timely manner, they may cause serious structural failure, resulting in reduced power generation efficiency or even equipment scrapping. Therefore, how to efficiently and accurately detect cracks in wind turbine blades has become a technical problem that urgently needs to be solved in the industry.

[0003] Currently, blade crack detection technologies are primarily divided into two categories: visual inspection and acoustic inspection. Visual inspection typically uses cameras or laser scanners to monitor the blade surface to detect surface cracks. However, visual inspection is often limited by ambient light, viewing angle, and blade surface conditions, making it difficult to effectively detect internal cracks. Furthermore, wind turbine blades are typically large, making visual inspection expensive and requiring limited real-time performance. In contrast, acoustic signature-based crack detection has become a mainstream approach due to its ability to nondestructively detect internal cracks, its significant advantages in environmental adaptability, and its real-time performance. Existing acoustic crack detection technologies primarily use acoustic sensors to collect acoustic signals from blades during operation and analyze abnormal signals using spectrum analysis or filtering to detect cracks. However, existing technologies still face numerous technical bottlenecks in terms of signal separation accuracy and the ability to distinguish crack signals from material fatigue signals. Specifically, existing systems often rely on traditional linear filtering algorithms, which struggle to effectively separate complex abnormal acoustic signals. Detection accuracy is particularly limited when dealing with tiny cracks and complex material fatigue states. Summary of the Invention

[0004] The present invention provides a wind turbine blade crack detection system based on acoustic feature analysis.

[0005] The wind turbine blade crack detection system based on acoustic feature analysis includes an acoustic signal acquisition module, a material fatigue state monitoring module, a signal separation and identification module, and a crack location module, among which:

[0006] The acoustic signal acquisition module collects acoustic signals generated during the operation of the wind turbine blades in real time by installing acoustic sensors at multiple locations on the blades of the wind turbine;

[0007] The material fatigue state monitoring module uses a multimodal sensor to monitor the material property changes of the blade material in real time. The material property changes include stress changes and temperature gradients. The material fatigue state monitoring module has a built-in material damage database. The module compares the material property changes with the material damage database and combines the historical operation data of the wind turbine blade to determine the material fatigue state of the current blade.

[0008] The signal separation and identification module uses a nonlinear signal decomposition algorithm and a self-supervised learning model to perform multi-level analysis on the collected acoustic signals, decomposing them using the Hilbert-Huang Transform (HHT) to distinguish between normal operating acoustic signals and abnormal acoustic signals. Abnormal acoustic signals also include crack signals and material fatigue signals. Based on the material fatigue status data output by the fatigue status monitoring module, a dual matching method of acoustic feature time-frequency diagrams and strain change diagrams is used to distinguish crack signals from material fatigue signals.

[0009] After distinguishing the crack signal, the crack location module combines the data of the multi-point acoustic sensor and determines the crack position through a triangulation positioning algorithm.

[0010] Optionally, the acoustic signal acquisition module installs array acoustic sensors at multiple predetermined positions on the wind turbine blades, including the root, middle and tip of the blades, to ensure full coverage of the key structural areas of the blades. The acoustic sensors are interconnected through a wireless communication network to form a distributed acoustic signal acquisition unit, which collects in real time the acoustic signals generated by the blades under different wind speeds, wind directions and load conditions.

[0011] Optionally, the material fatigue state monitoring module collects data on stress changes and temperature gradient changes of the blade in real time through a multimodal sensor array installed on the surface and inside of the blade. The multimodal sensor array includes strain sensors and thermal sensors, which capture the fatigue state of the blade material in multiple dimensions. The material damage database built into the material fatigue state monitoring module stores damage thresholds based on various blade material types and different operating environment conditions. By matching the real-time collected material property changes with the damage thresholds in the material damage database, and combining the operating history data of the wind turbine blade, the multivariate regression analysis method is used to evaluate the current material fatigue state of the blade in real time.

[0012] Optionally, the multivariate regression analysis method is expressed as:

[0013] Y=β0+β1σ+β2T g +∈, where Y is the fatigue state value, indicating the degree to which the material is close to fatigue damage, σ is the stress value calculated by the stress-strain relationship, T gis the temperature gradient, which represents the local temperature change of the blade, collected by the thermal sensor, β0 is the constant term, β1 and β2 are regression coefficients, obtained by training with historical data, and ∈ is the error term.

[0014] Optionally, the stress-strain relationship is calculated as: σ = E·ε, where E is the Young's modulus of the material, ε is the strain value measured by the strain sensor, and the fatigue damage state is evaluated by calculating the fatigue state value Y through a multivariate regression model, which is then compared with the material damage threshold T damage For comparison: D fatigue is the evaluation value of the material fatigue damage state, when D fatigue ≥1, it means that the material has reached the critical point of fatigue. fatigue When <1, the material is in a safe state.

[0015] Optionally, the signal separation and identification module uses empirical mode decomposition and Hilbert spectrum analysis in the Hilbert-Huang transform to decompose the acoustic signal in the time domain and frequency domain, extract each intrinsic mode function (IMF), identify the tiny frequency and energy change characteristics in the acoustic signal, analyze the frequency components and energy distribution of the decomposed signal, and then use a classifier to automatically distinguish between normally operating acoustic signals and abnormal acoustic signals.

[0016] Optionally, the classifier is trained in combination with a self-supervised learning model, and the signal features (frequency and energy changes) obtained by Hilbert-Huang transform decomposition are input into the self-supervised learning model. The self-supervised learning model is trained through comparative learning tasks. The model automatically learns how to distinguish between normal and abnormal signals without manually labeled data. Through training, a feature representation of the signal is generated, which is then used to automatically identify new signals and determine whether they are abnormal signals.

[0017] Optionally, the use of a dual matching method of acoustic feature time-frequency graph and strain change graph to distinguish crack signals from material fatigue signals specifically includes:

[0018] Acoustic feature time-frequency diagram generation: In the signal separation and identification module, after the Hilbert-Huang transform, the acoustic signal is decomposed into multiple intrinsic mode functions. The instantaneous frequency and amplitude of the acoustic signal are extracted through Hilbert spectrum analysis. Based on the extracted instantaneous frequency and amplitude of the acoustic signal, an acoustic feature time-frequency diagram is generated, showing the changes in frequency and energy at different time points. Crack signals show sudden high-frequency characteristics accompanied by significant changes in energy; material fatigue signals show gradually developing low-frequency characteristics with relatively gentle energy changes.

[0019] Strain change graph generation: Based on real-time data from strain sensors, a strain change graph is generated to describe the strain distribution and change trend of the blade during operation. Material fatigue signals are manifested as gradual accumulation of strain, while crack signals are accompanied by sudden and drastic changes in strain.

[0020] Double matching method: The double matching method achieves comprehensive differentiation between crack signals and material fatigue signals by comparing and analyzing the acoustic feature time-frequency diagram and the strain change diagram.

[0021] Optionally, the double matching method specifically includes:

[0022] Frequency matching submodule: Analyzes frequency changes in the acoustic signal and matches them with the strain change pattern in the material fatigue state. If the frequency change is relatively gentle and conforms to the trend of gradual strain accumulation, the signal is judged to be a material fatigue signal; if there is a sudden high-frequency change and an obvious strain mutation in the strain graph, the signal is a crack signal;

[0023] Energy matching submodule: Analyzes the energy changes of acoustic signals. Material fatigue is usually manifested as a slow accumulation of energy, while crack signals are accompanied by a sudden release of energy. By comparing the synchronization of energy mutations and strain changes in acoustic signals, the type of abnormal acoustic signal is reconfirmed.

[0024] Optionally, the crack location module determines the crack location by combining data from multi-point acoustic sensors installed at different positions on the wind turbine blade. The acoustic sensor collects the relationship between the time difference of the crack signal and the distance the signal reaches each sensor. The geometric relationship between the time difference and spatial coordinates of the three points is used to calculate the distance between the crack location and each acoustic sensor. The coordinate position of the crack on the surface or inside the blade is calculated based on the trigonometric geometric positioning formula, thereby achieving precise positioning of the crack.

[0025] Beneficial effects of the present invention:

[0026] The present invention realizes real-time detection and positioning of cracks in wind turbine blades through the organic combination of an acoustic signal acquisition module, a material fatigue state monitoring module, and a dynamic signal separation and identification module. In particular, the Hilbert-Huang transform algorithm is used to perform multi-level decomposition of acoustic signals, effectively identifying tiny frequency and energy changes, and distinguishing normal acoustic signals from abnormal signals. Combined with data from multi-point sensors and a triangulation positioning algorithm, the crack positioning module can accurately determine the crack position, solving the problems of insufficient crack detection accuracy and difficulty in positioning in the prior art, and significantly improving the accuracy and timeliness of crack detection.

[0027] The material fatigue state monitoring module uses multimodal sensors to monitor material strain, temperature, and other changes in real time, compares these data with a built-in material damage database, and outputs material fatigue state data. Based on this, the dynamic signal separation and identification module uses a dual matching algorithm between fatigue state data and acoustic feature time-frequency maps and strain change maps to effectively distinguish crack signals from material fatigue signals. This overcomes the limitations of traditional filtering algorithms and significantly improves the accuracy of abnormal signal separation through the coordinated analysis of material behavior and acoustic signals, leading to more accurate detection of crack signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 A schematic diagram of the functional modules of the system according to an embodiment of the present invention;

[0030] Figure 2 Schematic diagram of the double matching method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0032] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0033] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0034] like Figure 1-Figure 2 As shown in FIG, a wind turbine blade crack detection system based on acoustic feature analysis includes an acoustic signal acquisition module, a material fatigue state monitoring module, a signal separation and identification module, and a crack location module, wherein:

[0035] The acoustic signal acquisition module installs acoustic sensors at multiple locations on the wind turbine blades to collect the acoustic signals generated during the operation of the blades in real time;

[0036] The material fatigue status monitoring module uses multimodal sensors to monitor changes in the material properties of the blade material in real time. Material property changes include stress changes and temperature gradients. The module has a built-in material damage database. It compares the material property changes with the material damage database and combines it with the historical operation data of the wind turbine blade to determine the material fatigue status of the current blade.

[0037] The signal separation and recognition module uses a nonlinear signal decomposition algorithm and a self-supervised learning model to perform multi-level analysis on the collected acoustic signals, and uses the Hilbert-Huang transform (HHT) to decompose the acoustic signals to distinguish between normal operating acoustic signals and abnormal acoustic signals. During the operation of wind turbine blades, normal acoustic signals have relatively stable frequency and energy distribution, while cracks or damage usually cause abnormal sound wave characteristics, such as irregular frequency changes or sudden energy changes. After HHT decomposition, these abnormal signals can be effectively identified, laying the foundation for subsequent crack signal identification and positioning. Abnormal acoustic signals also include crack signals and material fatigue signals. Based on the material fatigue status data output by the fatigue status monitoring module, the dual matching method of acoustic feature time-frequency diagram and strain change diagram is used to distinguish crack signals from material fatigue signals. This module does not rely on traditional filtering algorithms, but instead performs intelligent separation based on the differences in signal morphology and material behavior.

[0038] After distinguishing the crack signal, the crack location module combines the data from multiple acoustic sensors and determines the crack location through a triangulation algorithm.

[0039] The acoustic signal acquisition module installs array acoustic sensors at multiple predetermined positions on the wind turbine blades, including the root, middle section and tip of the blades, to ensure full coverage of the key structural areas of the blades. The acoustic sensors are interconnected through a wireless communication network to form a distributed acoustic signal acquisition unit, which collects the acoustic signals generated by the blades under different wind speeds, wind directions and load conditions in real time. The collected acoustic signals include background acoustic signals during normal operation and abnormal acoustic signals that may be caused by cracks, fatigue damage or material deformation. The signal sampling frequency is adaptively adjusted according to the dynamic changes in the blade operating speed to ensure high-precision real-time data acquisition.

[0040] The material fatigue state monitoring module collects real-time data on stress changes and temperature gradient changes in the blades through a multimodal sensor array installed on the surface and inside of the blades. The multimodal sensor array includes strain sensors and thermal sensors, which capture the fatigue state of the blade material in multiple dimensions. The built-in material damage database of the material fatigue state monitoring module stores damage thresholds based on various blade material types and different operating environment conditions. By matching the real-time collected material property changes with the damage thresholds in the material damage database, and combining the historical operating data of the wind turbine blades, the multivariate regression analysis method is used to evaluate the current material fatigue state of the blades in real time.

[0041] The strain ε is used to calculate the stress σ through the stress-strain relationship formula. The stress σ is used as a key parameter in the regression model together with other factors affecting the fatigue state to analyze the fatigue state of the material. The multivariate regression analysis method is expressed as:

[0042] Y=β0+β1σ+β2T g +∈, where Y is the fatigue state value, indicating the degree to which the material is close to fatigue damage, σ is the stress value calculated by the stress-strain relationship, T g is the temperature gradient, which represents the local temperature change of the blade, collected by the thermal sensor, β0 is the constant term, β1 and β2 are regression coefficients, obtained by training with historical data, and ∈ is the error term.

[0043] In order to train the regression model with historical data and determine the regression coefficients β0, β1, β2, an optimization algorithm is used to fit the model. The following are the specific training steps and calculation process:

[0044] Consider a historical dataset containing the following variables:

[0045] σ: material stress under different conditions;

[0046] T g : Temperature gradient of leaves under different conditions;

[0047] Y: known material fatigue state (measured experimentally);

[0048] Each row of the dataset represents an observation from one experiment or run, and has the following format:

[0049] Where n is the number of samples of historical data.

[0050] 2. Regression equation: According to the previous regression model formula:

[0051] Y=β0+β1σ+β2T g +∈, we hope to determine the regression coefficients β0, β1, β2 through historical data;

[0052] 3. The optimization algorithm uses the least squares method, and the goal is to find a set of regression coefficients so that the actual observed fatigue state value Y i Fatigue state value predicted by the model The sum of squared errors between the two is minimized. The model predicts the value It can be expressed as:

[0053]

[0054] The goal is to minimize the following sum of squared errors:

[0055] The optimal regression coefficient value is calculated by taking the derivative of β0, β1, and β2 and setting the derivative to 0.

[0056] 4. Convert the regression problem into matrix form to solve it:

[0057] Let X be a matrix containing the independent variables:

[0058]

[0059] Y is the vector of target variables:

[0060]

[0061] β is the regression coefficient vector:

[0062]

[0063] The regression problem can be written as a matrix equation: Y = Xβ. To solve for β, the standard formula of the least squares method is used: β = (X T X) -1 X T Y, where X T is the transposed matrix of X, (X T X) -1 It's X TThe inverse matrix of X, X T Y is the product of X and Y. Through this formula, the optimal values ​​of β0, β1 and β2 can be calculated.

[0064] The stress-strain relationship is calculated as: σ = E·ε, where E is the Young's modulus of the material and ε is the strain value measured by the strain sensor. The fatigue damage state is evaluated by calculating the fatigue state value Y through a multivariate regression model, which is then compared with the material damage threshold T damage For comparison: D fatigue is the evaluation value of the material fatigue damage state, when D fatigue ≥1, it means that the material has reached the critical point of fatigue. fatigue When <1, the material is in a safe state.

[0065] The signal separation and identification module uses empirical mode decomposition and Hilbert spectrum analysis in the Hilbert-Huang transform to decompose the acoustic signal in the time domain and frequency domain, extract each intrinsic mode function (IMF), identify the subtle frequency and energy change characteristics in the acoustic signal, analyze the frequency components and energy distribution of the decomposed signal, and then use a classifier to automatically distinguish between normal operating acoustic signals and abnormal acoustic signals.

[0066] (1) The role of empirical mode decomposition (EMD) is to decompose complex acoustic signals into a series of intrinsic mode functions (IMFs). These functions reflect the frequency changes of the signal at different time scales. Suppose the acoustic signal we collected is x(t). Through EMD decomposition, we can obtain multiple IMFs: Among them, the IMF i (t) is the i-th intrinsic mode function, representing the essential oscillatory component of the signal, r(t) is the residual component, representing the low-frequency or long-term trend component, and n is the number of IMFs, which is automatically determined by the EMD decomposition process. Each IMF i (t) reflects the signal components at different frequencies and time scales. By analyzing these IMFs i (t), identifying small frequency changes in the acoustic signal.

[0067] (2) Hilbert spectrum analysis: After obtaining each IMF i (t) After that, the instantaneous frequency and energy distribution are calculated using the Hilbert transform, which is defined as follows:

[0068] Through Hilbert transform, we get the complex signal z i (t):

[0069] in;

[0070] is the instantaneous amplitude;

[0071] is the instantaneous phase.

[0072] The instantaneous frequency f can be obtained by taking the derivative of the instantaneous phase i (t):

[0073]

[0074] By calculating each IMF i (t) the instantaneous frequency f i (t) and instantaneous amplitude a i (t), construct the time-frequency spectrum of the acoustic signal, capture the tiny frequency and energy changes in the signal, and then distinguish normal from abnormal acoustic signals.

[0075] The classifier is trained in combination with a self-supervised learning model. The signal features (frequency and energy changes) obtained by Hilbert-Huang transform decomposition are input into the self-supervised learning model. The self-supervised learning model is trained through contrastive learning tasks. The model automatically learns how to distinguish between normal and abnormal signals without manually labeled data. Through training, it generates a feature representation of the signal, which is then used to automatically identify new signals and determine whether they are abnormal signals.

[0076] The self-supervised learning model is used to automatically distinguish normal acoustic signals from abnormal acoustic signals (such as crack signals and fatigue damage signals) from the decomposed signals. The signal features obtained through HHT decomposition are used as input data X to enter the self-supervised learning model;

[0077] Construct a self-supervised learning model f(X;θ), where X is the input signal feature and θ is the model parameter. The training process is as follows:

[0078] (1) Construct input data: The input data X includes the instantaneous frequency f of each IMF i (t), instantaneous amplitude a i (t) and other statistical features within the time period, let X be a matrix containing all signal features:

[0079] X=(f1(t) a1(t) … f n (t) a n (t));

[0080] (2) The self-supervised learning model performs unsupervised training by defining a contrastive learning task, automatically learning the feature representation of the signal. The loss function of the model can be defined as the contrastive learning loss: Among them, f(X i ; θ) is the model's response to the input signal Xi The encoding, It is X i The comparison sample after disturbance, τ is the temperature coefficient;

[0081] By minimizing the loss function L(θ), the feature representation of the signal is learned, which can effectively distinguish normal acoustic signals from abnormal acoustic signals.

[0082] (3) After the training is completed, the newly input acoustic signal is analyzed to identify whether it is an abnormal signal. The output result is a binary classification label, indicating whether the input signal is an abnormal acoustic signal: y = f(X; θ), where y represents the classification result, 1 represents an abnormal signal, and 0 represents a normal signal. Based on this classification result, the abnormal signal is further processed as a crack signal or a fatigue damage signal.

[0083] Using the dual matching method of acoustic feature time-frequency diagram and strain change diagram, the crack signal and material fatigue signal are distinguished. The specific steps include:

[0084] Input of material fatigue state data: The fatigue state monitoring module outputs material fatigue state data. This data is based on historical strain distribution, temperature gradient, and matching results with the material damage database. This data reflects whether the material is in the fatigue stage, the severity of fatigue progression, and the health status of the material at a certain moment. This data is used for subsequent signal classification.

[0085] Acoustic feature time-frequency diagram generation: In the signal separation and identification module, after the Hilbert-Huang transform, the acoustic signal is decomposed into multiple intrinsic mode functions. The instantaneous frequency and amplitude of the acoustic signal are extracted through Hilbert spectrum analysis. Based on the extracted instantaneous frequency and amplitude of the acoustic signal, an acoustic feature time-frequency diagram is generated, showing the changes in frequency and energy at different time points. Crack signals show sudden high-frequency characteristics accompanied by significant changes in energy; material fatigue signals show gradually developing low-frequency characteristics with relatively gentle energy changes.

[0086] Strain change graph generation: Based on real-time data from strain sensors, a strain change graph is generated to describe the strain distribution and change trend of the blade during operation. Material fatigue signals are manifested as gradual accumulation of strain, while crack signals are accompanied by sudden and drastic changes in strain.

[0087] Double matching method: The double matching method achieves comprehensive differentiation between crack signals and material fatigue signals by comparing and analyzing the acoustic feature time-frequency diagram and the strain change diagram.

[0088] The double matching method specifically includes:

[0089] Frequency matching submodule: Analyzes frequency changes in the acoustic signal and matches them with the strain change pattern in the material fatigue state. If the frequency change is relatively gentle and conforms to the trend of gradual strain accumulation, the signal is judged to be a material fatigue signal; if there is a sudden high-frequency change and an obvious strain mutation in the strain graph, the signal is a crack signal;

[0090] Energy matching submodule: Analyzes energy changes in acoustic signals. Material fatigue is usually manifested as a slow accumulation of energy, while crack signals are accompanied by a sudden release of energy. By comparing the synchronization of energy mutations and strain changes in acoustic signals, the type of abnormal acoustic signal can be re-confirmed.

[0091] It also includes comprehensive decision-making: after completing the dual matching of frequency and energy, a comprehensive decision is made based on the output information of the fatigue status monitoring module:

[0092] If the fatigue state data indicates that the material is approaching the critical point of fatigue damage, and the acoustic signal exhibits slow changes at a low frequency and steady energy accumulation, the system will determine that the abnormal acoustic signal is a material fatigue signal;

[0093] If the acoustic signal shows sudden high-frequency and high-energy changes, accompanied by drastic changes in strain, and the fatigue state data does not show that it is close to the critical point of damage, the system will determine that the abnormal acoustic signal is a crack signal;

[0094] Through the above-mentioned double matching method, the abnormal acoustic signal can be further divided into a crack signal or a material fatigue signal, and the result is output to the subsequent module to determine the crack location.

[0095] The crack location module determines the crack location by combining data from multiple acoustic sensors installed at different locations on the wind turbine blade. The module calculates the distance between the crack location and each acoustic sensor through the geometric relationship between the time difference between the three points and the spatial coordinates, and calculates the coordinate position of the crack on the blade surface or inside based on the trigonometric positioning formula, thereby achieving precise crack positioning.

[0096] 1. Time difference measurement of multi-point acoustic sensors: Select three acoustic sensors at different positions of the blade, the sensor positions are P1 (x1, y1, z1), P2 (x2, y2, z2) and P3 (x3, y3, z3), the crack position is P c (x c ,y c ,z c ), the sound wave from the crack position P c The time of propagation to each sensor is t1, t2 and t3 respectively;

[0097] Known conditions: the spatial coordinates of the sensor are P1, P2, P3, and the propagation speed of the sound wave in the blade material is v;

[0098] 2. The distance formula of sound wave propagation: The distance of crack signal propagation is related to the time and speed of sound wave propagation as follows: d1 = v·t1; d2 = v·t2; d3 = v·t3; where d1, d2, and d3 are the distances of sound wave propagation from the crack position P, respectively. c Distance to sensors P1, P2, P3;

[0099] 3. Time difference formula: Assume that the time differences collected by the sensor are Δt 12 =t2-t1 and Δt 13 =t3-t1, the distance difference can be calculated according to the time difference formula:

[0100] Δd 12 =v·Δt 12 =d2-d1;

[0101] Δd 13 =v·Δt 13 =d3-d1;

[0102] 4. According to the triangular geometry, the crack position P c (x c ,y c ,z c ) to the sensor is:

[0103]

[0104] Combined with the time difference formula Δd 12 and Δd 13 Calculations yield the following two sets of equations:

[0105]

[0106] 5. Through the above two sets of equations, combined with the known sensor positions P1 (x1, y1, z1), P2 (x2, y2, z2) and P3 (x3, y3, z3), the position coordinates of the crack can be solved by simultaneous equations P c (x c ,y c ,z c ), the equation is solved by an iterative method to finally determine the specific spatial position of the crack on the blade.

[0107] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0108] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A wind turbine blade crack detection system based on acoustic feature analysis is characterized by: It includes acoustic signal acquisition module, material fatigue state monitoring module, signal separation and identification module, and crack location module, among which: The acoustic signal acquisition module collects acoustic signals generated during the operation of the wind turbine blades in real time by installing acoustic sensors at multiple locations on the blades of the wind turbine; The material fatigue state monitoring module uses a multimodal sensor to monitor the material property changes of the blade material in real time. The material property changes include stress changes and temperature gradients. The material fatigue state monitoring module has a built-in material damage database. The module compares the material property changes with the material damage database and combines the historical operation data of the wind turbine blade to determine the material fatigue state of the current blade. The signal separation and recognition module uses a nonlinear signal decomposition algorithm and a self-supervised learning model to perform multi-level analysis on the collected acoustic signals. It uses the Hilbert-Huang transform to decompose the acoustic signals to distinguish between normal operating acoustic signals and abnormal acoustic signals. Abnormal acoustic signals also include crack signals and material fatigue signals. Based on the material fatigue status data output by the fatigue status monitoring module, a dual matching method of acoustic feature time-frequency diagrams and strain change diagrams is used to distinguish crack signals from material fatigue signals. After distinguishing the crack signal, the crack location module combines the data of the multi-point acoustic sensor and determines the crack position through a triangulation positioning algorithm.

2. The wind turbine blade crack detection system based on acoustic feature analysis according to claim 1 is characterized in that: The acoustic signal acquisition module installs array acoustic sensors at multiple predetermined positions on the wind turbine blades, including the root, middle section and tip of the blades. The acoustic sensors are interconnected through a wireless communication network to form a distributed acoustic signal acquisition unit, which collects the acoustic signals generated by the blades under different wind speeds, wind directions and load conditions in real time.

3. The wind turbine blade crack detection system based on acoustic feature analysis according to claim 1, characterized in that: The material fatigue state monitoring module collects data on stress changes and temperature gradient changes in the blade in real time through a multimodal sensor array installed on the surface and inside of the blade. The multimodal sensor array includes strain sensors and thermal sensors, which capture the fatigue state of the blade material in multiple dimensions. The built-in material damage database of the material fatigue state monitoring module stores damage thresholds based on various blade material types and different operating environment conditions. By matching the real-time collected material property changes with the damage thresholds in the material damage database, and combining the historical operating data of the wind turbine blade, the multivariate regression analysis method is used to evaluate the current material fatigue state of the blade in real time.

4. The wind turbine blade crack detection system based on acoustic feature analysis according to claim 3 is characterized in that: The multivariate regression analysis method is expressed as: Y=β0+β1σ+β2T g +∈, where Y is the fatigue state value, indicating the degree to which the material is close to fatigue damage, σ is the stress value calculated by the stress-strain relationship, T g is the temperature gradient, which represents the local temperature change of the blade, collected by the thermal sensor, β0 is the constant term, β1 and β2 are regression coefficients, obtained by training with historical data, and ∈ is the error term.

5. The wind turbine blade crack detection system based on acoustic feature analysis according to claim 4 is characterized in that: The stress-strain relationship is calculated as: σ = E·ε, where E is the Young's modulus of the material and ε is the strain value measured by the strain sensor. The fatigue damage state is evaluated by calculating the fatigue state value Y through a multivariate regression model, which is then compared with the material damage threshold T damage For comparison: D fatigue is the evaluation value of the material fatigue damage state, when D fatigue ≥1, it means that the material has reached the critical point of fatigue. fatigue When <1, the material is in a safe state.

6. The wind turbine blade crack detection system based on acoustic feature analysis according to claim 5, characterized in that: The signal separation and identification module uses empirical mode decomposition and Hilbert spectrum analysis in the Hilbert-Huang transform to decompose the acoustic signal in the time domain and frequency domain, extract each intrinsic mode function, identify the subtle frequency and energy change characteristics in the acoustic signal, analyze the frequency components and energy distribution of the decomposed signal, and then use a classifier to automatically distinguish between normally operating acoustic signals and abnormal acoustic signals.

7. The wind turbine blade crack detection system based on acoustic feature analysis according to claim 6, characterized in that: The classifier is trained in conjunction with a self-supervised learning model, where the signal features obtained through Hilbert-Huang transform decomposition are input into the self-supervised learning model. The self-supervised learning model is trained through contrastive learning tasks. The model automatically learns how to distinguish between normal and abnormal signals without manually labeled data. Through training, a feature representation of the signal is generated, which is then used to automatically identify new signals and determine whether they are abnormal signals.

8. The wind turbine blade crack detection system based on acoustic feature analysis according to claim 6, characterized in that: The dual matching method of acoustic feature time-frequency diagram and strain change diagram is used to distinguish crack signals from material fatigue signals, specifically including: Acoustic feature time-frequency diagram generation: In the signal separation and identification module, after the Hilbert-Huang transform, the acoustic signal is decomposed into multiple intrinsic mode functions. The instantaneous frequency and amplitude of the acoustic signal are extracted through Hilbert spectrum analysis. Based on the extracted instantaneous frequency and amplitude of the acoustic signal, an acoustic feature time-frequency diagram is generated, showing the changes in frequency and energy at different time points. Crack signals exhibit high-frequency characteristics accompanied by energy changes; material fatigue signals exhibit gradually developing low-frequency characteristics with gentle energy changes. Strain change graph generation: Based on real-time data from strain sensors, a strain change graph is generated to describe the strain distribution and change trend of the blade during operation. Material fatigue signals are manifested as gradual accumulation of strain, while crack signals are accompanied by drastic changes in strain. Double matching method: The double matching method comprehensively distinguishes crack signals and material fatigue signals by comparing and analyzing the acoustic feature time-frequency diagram and the strain change diagram.

9. The wind turbine blade crack detection system based on acoustic feature analysis according to claim 8, characterized in that: The double matching method specifically includes: Frequency matching submodule: Analyzes frequency changes in the acoustic signal and matches it with the strain change pattern in the material fatigue state. If the frequency change is gentle and consistent with the trend of gradual strain accumulation, the signal is judged to be a material fatigue signal; if high-frequency changes occur and the strain graph is accompanied by a sudden change in strain, the signal is a crack signal; Energy matching submodule: Analyzes the energy changes of acoustic signals. Material fatigue is usually manifested as a slow accumulation of energy, while crack signals are accompanied by a sudden release of energy. By comparing the synchronization of energy mutations and strain changes in acoustic signals, the type of abnormal acoustic signal is reconfirmed.

10. The wind turbine blade crack detection system based on acoustic feature analysis according to claim 1, characterized in that: The crack location module determines the crack location by combining data from multiple acoustic sensors installed at different locations on the wind turbine blade. The acoustic sensors collect the time difference between the crack signal and the distance the signal reaches each sensor. The module calculates the distance between the crack location and each acoustic sensor through the geometric relationship between the time difference and spatial coordinates of the three points, and calculates the coordinate position of the crack on or inside the blade based on the trigonometric geometric positioning formula.

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