Method for monitoring cracks in real time in high-pressure vibration state of wind tunnel

Abstract

The invention provides a method for monitoring cracks in a high-pressure vibration state of a wind tunnel in real time, and belongs to the technical field of wind tunnels. A piezoelectric ceramic sensor array is arranged on the wall surface of the wind tunnel, a vibration fatigue matrix and a structure resonance matrix are established, and a self-adaptive filtering algorithm is adopted to preprocess sensor signals; establishing a vibration coupling equation set to extract crack characteristic parameters, constructing a dynamic threshold discrimination matrix and a risk assessment matrix, utilizing a multi-sensor data fusion algorithm to accurately position crack positions and sizes, calculating risk levels according to a crack risk assessment index equation, and performing three-level safety discrimination. A complete crack real-time monitoring and risk assessment system in the high-pressure vibration environment is formed, and the technical problems that in the prior art, crack monitoring precision is low in the high-pressure vibration environment, and real-time risk assessment cannot be achieved are solved.

Description

Technical Field

[0001] This invention belongs to the field of wind tunnel technology, and more specifically, relates to a method for real-time monitoring of cracks under high-pressure vibration conditions in wind tunnels. Background Technology

[0002] Wind tunnel testing equipment plays a crucial role in aerospace, automotive, and civil engineering. Its wall structures are prone to fatigue cracks under long-term high-pressure vibration loads. Traditional crack monitoring techniques primarily employ ultrasonic testing, eddy current testing, and acoustic emission monitoring. These techniques demonstrate good detection performance under static or low-frequency vibration conditions and are widely used in the health monitoring of bridges, pressure vessels, and aircraft structures. However, under high-pressure vibration environments, traditional monitoring techniques suffer from severe signal noise interference and a significant decrease in detection accuracy. Ultrasonic signals are easily masked by strong vibration noise, eddy current testing is affected by electromagnetic interference, and acoustic emission monitoring cannot effectively distinguish crack signals from vibration noise, resulting in high false alarm and false negative rates. Existing technologies struggle to accurately identify and monitor minute cracks in real-time under complex high-pressure vibration environments and lack the ability to quantitatively assess crack propagation risks under dynamic load conditions, thus failing to provide reliable technical assurance for the safe operation of wind tunnel equipment. Summary of the Invention

[0003] In view of this, the present invention provides a method for real-time monitoring of cracks under high-pressure vibration conditions in wind tunnels, which can solve the technical problems of low crack monitoring accuracy and inability to achieve real-time risk assessment under high-pressure vibration conditions in the prior art.

[0004] This invention is implemented as follows: A method for real-time monitoring of cracks under high-pressure vibration conditions in a wind tunnel is provided. Multiple piezoelectric ceramic sensors are arranged in a sensor array on the wind tunnel wall in the area to be monitored. A vibration fatigue matrix and a structural resonance matrix are established. An adaptive filtering algorithm is used to preprocess the piezoelectric ceramic sensor signals. A vibration coupling equation set for crack feature signal extraction is established, and the time-domain and frequency-domain feature parameters of the crack signal are obtained by solving the vibration coupling equation set. A dynamic threshold discrimination matrix and a risk assessment matrix are constructed. The crack location is accurately determined using a multi-sensor data fusion algorithm. The crack risk assessment index is calculated based on the crack risk assessment index equation. The safety status of the wind tunnel wall is judged based on the comparison between the crack risk assessment index and the threshold. A noise model is established using the vibration fatigue matrix to effectively suppress high-frequency vibration noise. The impact of cracks on the dynamic characteristics of the structure is analyzed in conjunction with the structural resonance matrix. The crack identification threshold is adjusted in real time using the dynamic threshold discrimination matrix to adapt to different vibration conditions.

[0005] Specifically, the sensor array arrangement steps involve a sensor spacing of 10–50 mm, and the use of epoxy resin adhesive to firmly bond the piezoelectric ceramic sensor to the wind tunnel wall to ensure good contact between the piezoelectric ceramic sensor and the wind tunnel wall.

[0006] Specifically, the steps of establishing the vibration fatigue matrix and the structural resonance matrix involve collecting vibration acceleration data of the wind tunnel wall at different frequencies using accelerometers, constructing a vibration fatigue matrix to describe the fatigue accumulation characteristics of the material under cyclic loading, and constructing a structural resonance matrix to identify the resonance response mode of the structure at a given frequency.

[0007] The adaptive filtering algorithm preprocesses the piezoelectric ceramic sensor signal by establishing a noise model using a vibration fatigue matrix and adjusting the filter coefficients using the minimum mean square error criterion to effectively suppress high-frequency vibration noise.

[0008] The vibration coupling equation set includes signal separation equation, frequency domain transformation equation, and feature extraction equation. By solving the vibration coupling equation set, the time-domain and frequency-domain characteristic parameters of the crack signal are obtained, and the influence of the crack on the dynamic characteristics of the structure is analyzed in combination with the structural resonance matrix.

[0009] The signal separation equation is used to separate the crack feature signal from the mixed signal. The input includes the original signal of the piezoelectric ceramic sensor, the vibration fatigue matrix, the noise power spectral density, the filter coefficients, and the time window length. The output is the separated crack signal.

[0010] The frequency domain transformation equation is used to convert the time domain signal into a frequency domain signal and identify the characteristic frequency of the crack. The inputs include the separated crack signal, the structural resonance matrix, the sampling frequency, the transformation window function, and the frequency resolution. The output is the characteristic frequency spectrum of the crack.

[0011] The feature extraction equation is used to extract the geometric feature parameters of the crack from the frequency domain signal. The input includes the crack feature frequency spectrum, material elastic modulus, structural geometric parameters, boundary conditions, and load history. The output is time domain feature parameters and frequency domain feature parameters.

[0012] The dynamic threshold discrimination matrix is ​​used to adjust the crack identification threshold in real time to adapt to different vibration conditions, and the risk assessment matrix is ​​used to comprehensively evaluate the crack propagation risk and structural safety.

[0013] The multi-sensor data fusion algorithm uses the separated crack signal, time-domain feature parameters, and frequency-domain feature parameters for calculation, and uses the time difference of arrival method and signal amplitude attenuation characteristics to determine the spatial coordinates and geometric dimensions of the crack.

[0014] The crack risk assessment index equation is used to calculate the risk level of cracks in the wind tunnel wall. The inputs include time-domain characteristic parameters, frequency-domain characteristic parameters, spatial coordinates, geometric dimensions, and material fatigue coefficients. The output is the crack risk assessment index used to determine the safety status of the wind tunnel wall.

[0015] Specifically, the method of judging the safety status of the wind tunnel wall based on the comparison between the crack risk assessment index and the threshold means that when the crack risk assessment index is less than or equal to the first threshold, there is no crack risk in the wind tunnel wall; when the crack risk assessment index is greater than the first threshold and less than or equal to the second threshold, the wind tunnel wall is suspected of having crack risk and needs to be reassessed; and when the crack risk assessment index is greater than the second threshold, the wind tunnel wall has crack risk and needs to be shut down for maintenance.

[0016] The first threshold is obtained by solving the first threshold calculation equation. The input includes the material strength limit, safety factor, fatigue life curve, historical failure data, and structural reliability index. The output is the first threshold used for the safety judgment of the crack risk assessment index.

[0017] The second threshold is obtained by solving the second threshold calculation equation. The input includes material fracture toughness, critical crack length, stress concentration factor, load amplification factor, and structural failure probability. The output is the second threshold used for the hazard judgment of the crack risk assessment index.

[0018] The vibration fatigue matrix is ​​a mathematical model that describes the cumulative fatigue damage of materials under cyclic vibration loads. By recording the material fatigue characteristics under different stress amplitudes and cycles, it is used to establish a mapping relationship between fatigue damage and vibration acceleration data.

[0019] The structural resonance matrix is ​​a parameter matrix that characterizes the dynamic response of a structure at different excitation frequencies. It contains information on the structure's natural frequencies, damping ratios, and mode shapes, and is used to identify the structure's resonance frequency points and vibration modes.

[0020] This invention achieves precise separation and feature extraction of crack signals under high-pressure vibration environments by constructing a monitoring system combining a multi-sensor array with a vibration coupling equation set, effectively solving the problem of severe signal noise interference in traditional technologies. The adaptive filtering algorithm established by this invention using the vibration fatigue matrix and structural resonance matrix can dynamically adjust filtering parameters according to the real-time vibration state, significantly improving the identification accuracy of crack feature signals and overcoming the deficiency of decreased detection accuracy in existing technologies. The dynamic threshold discrimination matrix and risk assessment matrix established by this invention, combined with multi-dimensional feature parameters for comprehensive analysis, achieve real-time quantitative assessment of crack propagation risk, making up for the lack of dynamic risk assessment capabilities in traditional technologies and providing a scientific basis for preventive maintenance of wind tunnel equipment. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0023] like Figure 1 The diagram shown is a flowchart of a method for real-time monitoring of cracks under high-pressure vibration conditions in a wind tunnel, provided by the present invention. This method includes the following steps:

[0024] S01. Multiple piezoelectric ceramic sensors are arranged in the area to be monitored on the wind tunnel wall to form a sensor array. The sensor spacing is 10-50mm. Epoxy resin is used to firmly bond the piezoelectric ceramic sensors to the wind tunnel wall to ensure good contact between the piezoelectric ceramic sensors and the wind tunnel wall.

[0025] S02. Establish vibration fatigue matrix and structural resonance matrix. Collect vibration acceleration data of wind tunnel wall at different frequencies using accelerometers. Construct vibration fatigue matrix to describe the fatigue accumulation characteristics of material under cyclic loading. Construct structural resonance matrix to identify the resonance response mode of structure at frequency.

[0026] S03. An adaptive filtering algorithm is used to preprocess the piezoelectric ceramic sensor signal. A noise model is established using the vibration fatigue matrix. The filter coefficients are adjusted by the minimum mean square error criterion to effectively suppress high-frequency vibration noise.

[0027] S04. Establish a set of vibration coupling equations for extracting crack feature signals. Obtain the time-domain and frequency-domain feature parameters of the crack signal by solving the set of vibration coupling equations. Combine the structural resonance matrix to analyze the influence of cracks on the dynamic characteristics of the structure.

[0028] S05. Construct a dynamic threshold discrimination matrix and a risk assessment matrix. The dynamic threshold discrimination matrix is ​​used to adjust the crack identification threshold in real time to adapt to different vibration conditions. The risk assessment matrix is ​​used to comprehensively evaluate the crack propagation risk and structural safety.

[0029] S06. The crack location is accurately located by using a multi-sensor data fusion algorithm, and the spatial coordinates and geometric dimensions of the crack are determined by using the time difference of arrival method and signal amplitude attenuation characteristics.

[0030] S07. Calculate the crack risk assessment index according to the crack risk assessment index equation. Input the time domain characteristic parameters, frequency domain characteristic parameters, spatial coordinates, and geometric dimensions into the crack risk assessment index equation for calculation. When the crack risk assessment index is less than or equal to the first threshold, there is no crack risk on the wind tunnel wall. When the crack risk assessment index is greater than the first threshold and less than or equal to the second threshold, the wind tunnel wall is suspected of having crack risk and needs to return to step S03 for reassessment. When the crack risk assessment index is greater than the second threshold, the wind tunnel wall definitely has crack risk and needs to be shut down for maintenance.

[0031] The vibration coupling equation set includes signal separation equations, frequency domain transformation equations, and feature extraction equations.

[0032] The signal separation equation is used to separate the crack feature signal from the mixed signal. The input includes the original signal of the piezoelectric ceramic sensor, the vibration fatigue matrix, the noise power spectral density, the filter coefficients, and the time window length. The output is the separated crack signal.

[0033] The frequency domain transformation equation is used to convert the time domain signal into a frequency domain signal and identify the characteristic frequency of the crack. The input includes the separated crack signal, the structural resonance matrix, the sampling frequency, the transformation window function, and the frequency resolution. The output is the characteristic frequency spectrum of the crack.

[0034] The feature extraction equation is used to extract the geometric feature parameters of the crack from the frequency domain signal. The input includes the crack feature frequency spectrum, material elastic modulus, structural geometric parameters, boundary conditions, and load history. The output is time domain feature parameters and frequency domain feature parameters.

[0035] The multi-sensor data fusion algorithm uses the separated crack signal, time-domain feature parameters, and frequency-domain feature parameters for calculation, and outputs spatial coordinates and geometric dimensions for the calculation of the crack risk assessment index equation.

[0036] The crack risk assessment index equation is used to calculate the risk level of cracks in the wind tunnel wall. The inputs include time domain characteristic parameters, frequency domain characteristic parameters, spatial coordinates, geometric dimensions, and material fatigue coefficients. The output is the crack risk assessment index used to determine the safety status of the wind tunnel wall.

[0037] The threshold determination equation set includes a first threshold calculation equation and a second threshold calculation equation;

[0038] The first threshold calculation equation is used to determine the boundary threshold between safety and suspected risk. The inputs include material strength limit, safety factor, fatigue life curve, historical failure data, and structural reliability index. The output is the first threshold used for safety judgment of crack risk assessment index.

[0039] The second threshold calculation equation is used to determine the boundary threshold between suspected risk and confirmed risk. The inputs include material fracture toughness, critical crack length, stress concentration factor, load amplification factor, and structural failure probability. The output is the second threshold used for the hazard judgment of the crack risk assessment index.

[0040] The vibration fatigue matrix is ​​a mathematical model that describes the cumulative fatigue damage of materials under cyclic vibration loads. By recording the fatigue characteristics of materials under different stress amplitudes and cycles, it is used to establish a mapping relationship between fatigue damage and vibration acceleration data.

[0041] The structural resonance matrix is ​​a parametric matrix that characterizes the dynamic response of a structure at different excitation frequencies. It contains information on the structure's natural frequencies, damping ratios, and mode shapes, and is used to identify the structure's resonance frequency points and vibration modes.

[0042] The dynamic threshold discrimination matrix is ​​an algorithm matrix that dynamically adjusts the crack identification threshold based on real-time vibration environment and historical monitoring data. By analyzing changes in vibration intensity, frequency distribution, and signal-to-noise ratio, it adaptively optimizes the discrimination threshold to improve identification accuracy.

[0043] The risk assessment matrix is ​​a multi-dimensional evaluation system that comprehensively considers crack size, location, propagation rate, and structural load state. It calculates the crack risk level through weighted calculation, providing a quantitative basis for structural safety assessment and maintenance decisions.

[0044] The fatigue coefficient is a dimensionless parameter characterizing a material's resistance to fatigue under cyclic loading. It is determined through material fatigue testing and is used to quantify the fatigue life characteristics of materials under different stress levels.

[0045] The first threshold is the safety and potential risk boundary value obtained by solving the first threshold calculation equation. It is determined based on material strength theory and structural reliability analysis and is used to distinguish the safe state and potential risk state of the wind tunnel wall.

[0046] The second threshold is the boundary value between suspected risk and confirmed risk obtained by solving the second threshold calculation equation. It is determined based on fracture mechanics theory and failure probability analysis and is used to distinguish between potential risk state and dangerous state of wind tunnel wall.

[0047] The specific implementation methods of the above steps are described in detail below.

[0048] The specific implementation of step S01 involves arranging multiple piezoelectric ceramic sensors in a grid-like layout across the monitored area on the wind tunnel wall, forming a two-dimensional or three-dimensional sensor array. First, the sensor spacing is determined based on the wind tunnel's structural dimensions and the expected crack detection accuracy, generally set to 10 to 50 millimeters. For high-precision monitoring areas, a spacing of 10 to 20 millimeters is used, while for general monitoring areas, a spacing of 30 to 50 millimeters is used. The sensor arrangement utilizes the piezoelectric effect principle, employing the characteristic of piezoelectric ceramic materials generating electrical charges under mechanical stress to sense wall vibration signals. During installation, the wind tunnel wall surface is first cleaned to remove oil and oxide layers. Then, epoxy resin adhesive is used for bonding, with the adhesive layer thickness controlled between 0.1 and 0.3 millimeters to ensure good mechanical coupling between the sensors and the wall. The bonding curing time is no less than 24 hours, and the curing temperature is maintained within the range of 20 to 25 degrees Celsius. After the sensor array is arranged, electrical connections and signal transmission lines are laid. Shielded cables are used to reduce electromagnetic interference, and signal amplifiers are required when the transmission distance exceeds 50 meters.

[0049] The specific implementation of step S02 involves establishing a vibration fatigue matrix and a structural resonance matrix through multi-frequency excitation experiments and data acquisition. First, with the wind tunnel shut down, an electromagnetic exciter applies sinusoidal excitation of different amplitudes to the wind tunnel wall within a frequency range of 5 to 1000 Hz, with the excitation force amplitude increasing in increments from 10 Newtons to 1000 Newtons. Simultaneously, a high-precision accelerometer collects vibration acceleration response data at various measuring points on the wall, with the sampling frequency set to at least 20 times the excitation frequency to ensure the integrity of the signal acquisition. The vibration fatigue matrix is ​​constructed based on the theory of cumulative material fatigue damage. By recording the material response characteristics under different stress amplitudes and cycle numbers, a multidimensional mapping relationship is established between fatigue damage degree and vibration acceleration amplitude, frequency, and cycle number. The structural resonance matrix is ​​constructed using modal analysis. Frequency domain analysis identifies the structure's natural frequencies, damping ratios, and mode shape parameters, with a natural frequency identification accuracy of 0.1 Hz and a damping ratio identification accuracy of 0.001. The matrix data is fitted using the least squares method, with a correlation coefficient of no less than 0.95.

[0050] The specific implementation of step S03 involves preprocessing the piezoelectric ceramic sensor signal using an adaptive filtering algorithm based on the minimum mean square error criterion. This algorithm, based on Wiener filtering theory, minimizes the mean square error between the desired signal and the filtered output signal by iteratively adjusting the filter coefficients. First, a noise power spectral density model is constructed using the vibration fatigue matrix established in step S02 to identify the frequency distribution characteristics and amplitude variation patterns of high-frequency vibration noise. The filter employs a finite impulse response structure, with the filter order set between 64 and 256, the specific order determined based on the complexity of the signal frequency components. The adaptive algorithm uses the minimum mean square error algorithm, with a step size factor between 0.001 and 0.01. The convergence criterion is that the mean square error change over 100 consecutive iterations is less than 10. -6 The signal-to-noise ratio (SNR) is monitored in real time during the filtering process. When the SNR falls below 20 dB, the filtering parameters are automatically adjusted to ensure effective preservation of the crack signal. The amplitude attenuation of the filtered signal should be controlled within 5%, and the phase delay should be controlled within 2 degrees.

[0051] The specific implementation of step S04 involves establishing and solving a set of vibration coupling equations to extract the time-domain and frequency-domain characteristic parameters of the crack feature signal. The vibration coupling equation set includes three sub-equations: a signal separation equation, a frequency domain transformation equation, and a feature extraction equation. The signal separation equation, based on the principle of independent component analysis, separates the crack feature signal from the background vibration signal in the mixed signal through the statistical independence assumption. Input parameters include the preprocessed sensor signal, vibration fatigue matrix, noise power spectral density, filter coefficients, and a time window length of 500 to 2000 milliseconds. The frequency domain transformation equation uses a fast Fourier transform algorithm to convert the time-domain signal into a frequency-domain signal, combining the structural resonance matrix to identify the characteristic frequency changes caused by the crack. The transformation window function uses a Hanning window, and the frequency resolution is set to 0.1 Hz. The feature extraction equation, based on wavelet analysis theory, extracts the geometric feature parameters of the crack from the frequency-domain signal. The wavelet basis function uses the Daubechies wavelet, with a decomposition level of 6 to 8 layers. Time-domain characteristic parameters include the root mean square value of the signal, peak factor, waveform factor, and impulse factor, while frequency-domain characteristic parameters include the dominant frequency, frequency band energy distribution, frequency centroid, and spectral entropy.

[0052] The specific implementation of step S05 involves constructing a dynamic threshold discrimination matrix and a risk assessment matrix to achieve intelligent crack identification and risk assessment. The dynamic threshold discrimination matrix, based on adaptive threshold theory, dynamically adjusts the crack identification threshold by analyzing real-time vibration environment parameters, including vibration intensity, frequency distribution, and signal-to-noise ratio changes. The matrix employs a fuzzy logic algorithm, setting three fuzzy levels corresponding to low, medium, and high vibration environments, with corresponding threshold adjustment coefficients of 0.8, 1.0, and 1.2, respectively. The risk assessment matrix uses the analytic hierarchy process (AHP) to construct a multi-dimensional evaluation system, comprehensively considering crack size (weight 0.3), location (weight 0.25), propagation rate (weight 0.25), and structural load state (weight 0.2). Matrix calculation uses a weighted summation algorithm, where each indicator is first normalized and then linearly combined according to its weight coefficients. The dynamic threshold update cycle is set to 10 to 60 seconds, with the specific cycle adaptively adjusted based on the severity of vibration environment changes. The risk assessment level is divided into 5 levels, corresponding to risk indices of 0 to 0.2, 0.2 to 0.4, 0.4 to 0.6, 0.6 to 0.8, and 0.8 to 1.0, respectively.

[0053] The specific implementation of step S06 involves using a multi-sensor data fusion algorithm to accurately locate the crack and determine its geometric dimensions. The data fusion algorithm combines the time-of-arrival (TOA) positioning principle with signal amplitude attenuation characteristic analysis. First, a cross-correlation algorithm is used to calculate the arrival time difference of the crack signal between different sensors, with the time difference measurement accuracy required to be at the microsecond level. Then, a least-squares positioning algorithm is used to establish a spatial coordinate system with the sensor array as a reference, and the spatial coordinates of the crack are determined by solving an overdetermined set of equations. The positioning accuracy is related to the sensor spacing and array geometry; a square array with a 10-millimeter spacing can achieve millimeter-level positioning accuracy. The signal amplitude attenuation characteristic analysis uses a geometric diffusion attenuation model, estimating the crack depth and length by analyzing the amplitude variation of the signal at different distances. Geometric dimension estimation also incorporates acoustic scattering theory, using the crack's scattering characteristics to infer the crack opening width. The data fusion algorithm uses Kalman filtering for state estimation; the system noise covariance matrix and the observation noise covariance matrix are determined based on sensor accuracy and environmental noise levels.

[0054] The specific implementation of step S07 involves calculating the risk index and determining the safety status based on the crack risk assessment index equation. The risk assessment index equation is based on reliability theory and fracture mechanics principles, comprehensively considering the influence of time-domain characteristic parameters, frequency-domain characteristic parameters, spatial coordinates, geometric dimensions, and material fatigue coefficients. The calculation process first normalizes the dimensions of each input parameter, and then calculates the comprehensive risk index using a weighted nonlinear combination method. The material fatigue coefficient is determined through fatigue testing, with a value ranging from 0.1 to 0.9; the smaller the value, the stronger the material's fatigue resistance. The first threshold is determined by the first threshold calculation equation, which is based on material strength theory and structural reliability analysis. Input parameters include the material strength limit, a safety factor of 2.0 to 3.0, fatigue life curves, historical failure data, and a structural reliability index above 0.99. The calculated first threshold is generally between 0.3 and 0.5. The second threshold is determined by the second threshold calculation equation, which is based on fracture mechanics theory and failure probability analysis. Input parameters include material fracture toughness, critical crack length, stress concentration factor, load amplification factor, and a structural failure probability less than 10. -4 The calculated second threshold is typically between 0.6 and 0.8. When the calculated crack risk assessment index is less than or equal to the first threshold, the wind tunnel wall is considered to be in a safe state, and no action is required. When the risk index is greater than the first threshold but less than or equal to the second threshold, the wind tunnel wall is considered to have a suspected crack risk, requiring a return to step S03 for signal processing and assessment, while increasing the monitoring frequency. When the risk index is greater than the second threshold, the wind tunnel wall is considered to definitely have a crack risk, requiring immediate shutdown for detailed inspection and maintenance.

[0055] The key technical ideas of this invention are analyzed as follows. The first key technical idea is the collaborative modeling technology of vibration fatigue matrix and structural resonance matrix. This technology establishes a coupling relationship between material fatigue characteristics and structural dynamic response characteristics, achieving an accurate description of the crack initiation and propagation mechanism under complex vibration environments. Compared to traditional single-parameter monitoring methods, this collaborative modeling technology can simultaneously consider the cumulative effect of material fatigue and the amplification effect of structural resonance, significantly improving the accuracy and reliability of early crack identification. The second key technical idea is the adaptive signal processing technology based on vibration coupling equations. This technology organically combines signal separation, frequency domain transformation, and feature extraction, achieving effective extraction of weak crack signals by solving the coupling equations. Compared to traditional linear filtering methods, this technology can accurately separate crack feature signals against strong vibration noise backgrounds, avoiding signal distortion and loss of feature information. The third key technical idea is the precise positioning technology based on multi-sensor data fusion. This technology combines the time difference of arrival method and signal amplitude attenuation characteristic analysis to achieve simultaneous determination of the crack's spatial location and geometric dimensions. Compared to traditional single-point monitoring methods, this technology significantly improves positioning accuracy through multi-point information fusion and can achieve comprehensive characterization of cracks in three-dimensional space. The fourth key technological approach is intelligent decision-making technology based on dynamic threshold discrimination and risk assessment. This technology, by constructing a dynamic threshold discrimination matrix and a risk assessment matrix, achieves adaptive adjustment to different vibration conditions and comprehensive multi-dimensional risk assessment. The synergistic effect of these technological approaches gives the entire monitoring system higher environmental adaptability, stronger anti-interference capabilities, and more accurate risk early warning functions compared to existing technologies. It enables real-time, accurate, and reliable monitoring of wind tunnel wall cracks in harsh high-pressure vibration environments, providing strong technical support for the safe operation of wind tunnel equipment.

[0056] Traditional monitoring technologies are ill-suited to the complex and variable vibration conditions of wind tunnel equipment, lacking adaptive adjustment capabilities. Existing crack monitoring systems typically employ fixed detection parameters and threshold settings. When a wind tunnel operates under different test conditions, vibration frequency, amplitude, and load patterns change significantly. Monitoring systems with fixed parameters struggle to maintain stable detection performance, frequently exhibiting false alarms due to excessive sensitivity in some conditions and false alarms due to insufficient sensitivity in others. This invention, by establishing a dynamic threshold discrimination matrix, automatically adjusts the crack identification threshold based on real-time collected vibration intensity, frequency distribution, and signal-to-noise ratio changes. This allows the monitoring system to adapt to different vibration conditions and maintain stable detection accuracy. Furthermore, by combining data support from vibration fatigue and structural resonance matrices, intelligent adjustment of monitoring parameters is achieved, significantly improving the system's adaptability and reliability in complex vibration environments. Traditional crack monitoring technologies primarily focus on detecting the current state of cracks, lacking the ability to predict and analyze future crack development trends. Maintenance personnel can only make passive maintenance decisions based on current detection results, often missing optimal maintenance opportunities, leading to increased maintenance costs or safety risks. This invention constructs a risk assessment matrix, comprehensively considering the size characteristics, location distribution, propagation rate of cracks, as well as the load history and material fatigue characteristics of the structure, and establishes a crack risk assessment index equation. This equation can quantitatively calculate the propagation risk level of cracks and achieve three-level safety status discrimination by setting different risk thresholds. This provides a scientific quantitative basis for maintenance decisions, enabling maintenance personnel to formulate preventive maintenance plans based on the risk assessment results and take corresponding measures in a timely manner before cracks reach a dangerous state, thereby improving the safety and operational reliability of wind tunnel equipment.

[0057] Specifically, the principle of this invention is as follows: This invention solves the technical problem of low crack monitoring accuracy under high-pressure vibration environments. Its fundamental principle lies in achieving effective separation and accurate identification of crack signals in complex vibration fields through the spatially distributed arrangement of a multi-sensor array and mathematical modeling of vibration coupling equations. The vibration fatigue matrix, by recording the fatigue characteristics of materials under different stress amplitudes and cycle numbers, establishes a precise mapping relationship between fatigue damage and vibration acceleration data, providing a reliable noise model basis for the adaptive filtering algorithm. This allows the filter to automatically adjust coefficients according to the real-time vibration state, effectively suppressing the interference of high-frequency vibration noise on crack signals. The structural resonance matrix contains the structure's natural frequencies, damping ratios, and mode shapes. By converting the time-domain signal into a frequency-domain signal through frequency-domain transformation equations, it can accurately identify crack characteristic frequencies, avoiding the influence of resonance frequencies on monitoring results. The multi-sensor data fusion algorithm utilizes the time difference of arrival method and signal amplitude attenuation characteristics to achieve precise location and size measurement of cracks through spatial geometric calculations, improving the reliability and accuracy of monitoring. The dynamic threshold discrimination matrix dynamically adjusts the identification threshold based on real-time vibration environment and historical monitoring data, avoiding the adaptability problem of fixed thresholds under different working conditions. Meanwhile, the risk assessment matrix comprehensively considers the geometric characteristics, location distribution and expansion trend of cracks, and obtains a quantitative risk assessment index through weighted calculation, realizing real-time evaluation and early warning of structural safety status.

[0058] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0059] In this embodiment, the specific implementation of step S01 is the same as described above, and will not be repeated in detail here.

[0060] The specific implementation of step S02 involves establishing the vibration fatigue matrix and structural resonance matrix through multi-frequency excitation tests. The vibration fatigue matrix is ​​specifically represented as follows:

[0061]

[0062] In the formula, F vib f is the vibration fatigue matrix; ij Let be the fatigue damage degree under the i-th stress amplitude in the j-th cycle; m is the stress amplitude level; and n is the cycle number level. The fatigue damage degree is calculated using the following formula:

[0063]

[0064] In the formula, N k N represents the load of the k-th cycle; f，i Let α be the fatigue life under the i-th stress amplitude; i β is the fatigue cumulative factor. iγ is the fatigue decay index; i This represents the initial damage offset. The specific representation of the structural resonance matrix is ​​as follows:

[0065]

[0066] In the formula, R res ω is the structural resonance matrix; i ζ is the i-th natural frequency; i φ is the i-th order damping ratio; ij Let ω be the displacement amplitude of the i-th mode at the j-th node. The parameter is obtained as follows: ω i The frequency response function is obtained using a frequency domain analysis method, including step 1: applying white noise excitation to the structure; step 2: analyzing the response signal using Fast Fourier Transform; and step 3: identifying the frequency corresponding to the peak value of the frequency response function. i The half-power point method is used, including step 1: determining the resonant peak frequency; step 2: finding the frequency points corresponding to a 3 dB drop in amplitude on both sides of the peak; and step 3: calculating the damping ratio. α i The values ​​were obtained by fitting fatigue test data, ranging from 0.1 to 0.5. β i γ was obtained using exponential decay fitting, with values ​​ranging from 0.01 to 0.1. i Initial damage measurements were used, with values ​​ranging from 0 to 0.05.

[0067] The specific implementation of step S03 involves preprocessing the sensor signal using an adaptive filtering algorithm. The formula for calculating the output signal of the adaptive filter is as follows:

[0068]

[0069] In the formula, y(t) is the filter output signal; w i (t) represents the coefficient of the i-th filter; x(ti) represents the input signal delayed by i sampling points; L is the filter length; ∈ filter (t) represents the filtering error term. The filter coefficient update formula is:

[0070] w i (t+1)=w i (t)+μ·e(t)·x(ti)·exp(-λ i ·t);

[0071] In the formula, μ is the step size factor, ranging from 0.001 to 0.01; e(t) is the error between the desired signal and the output signal; λ i This is the forgetting factor, with a value ranging from 0.95 to 0.99.

[0072] The specific implementation of step S04 involves establishing and solving the vibration coupling equation set. The specific expression of the signal separation equation is as follows:

[0073]

[0074] In the formula, S crack (t) represents the crack signal after separation; a j H is the j-th separation coefficient; j (t) is the response function of the j-th separation filter, specifically expressed as: Where h j，k L represents the impulse response coefficient of the filter. j S is the filter length, Δt is the sampling interval; mix (t) represents the mixed signal; τ j The time delay is the j-th time delay; M is the number of separation filters; b k η is the coefficient of the k-th differential; K is the number of differential terms; η sep (t) represents the separation error term. The specific expression of the frequency domain transform equation is as follows:

[0075]

[0076] In the formula, F crack (ω) represents the characteristic frequency spectrum of the crack; W(ω, t) is the time-frequency window function, specifically expressed as: Where w(t-t0) is the Hanning window function, t0 is the center time of the window function; j is the imaginary unit; c p R is the p-th resonance weighting coefficient; res，p (ω) is the p-th order resonance response function; P is the resonance order considered; δ trans (ω) represents the transformation error term. The specific expression of the feature extraction equation is as follows:

[0077]

[0078] In the formula, Θ crack d is the feature parameter vector of the crack; q ψ is the q-th wavelet coefficient; q Let ψ be the q-th wavelet basis function. q (t)=2 -q / 2 ·ψ(2 -q tk q ), where ψ(t) is the mother wavelet function, k q is the translation parameter; Q is the wavelet decomposition level; g r E is the weight of the r-th derivative; r ξ is the material's elastic modulus raised to the power of r; R is the order of the derivative; extract To extract error terms.

[0079] The specific implementation of step S05 involves constructing a dynamic threshold discrimination matrix and a risk assessment matrix. The dynamic threshold discrimination matrix is ​​represented as follows:

[0080]

[0081] In the formula, T dynamic t is the dynamic threshold discrimination matrix; base The baseline threshold is k1(σ). vib ) is the vibration intensity adjustment function, specifically expressed as k1(σ vib )=1+0.2·tanh(σ vib / σ ref -1), where σ vib For real-time vibration intensity, σ ref For reference vibration intensity; k2(f dom The main frequency adjustment function is specifically represented as k2(f) dom )=1+0.1·sin(πf dom / f max ), where f dom As the dominant frequency, f max The maximum frequency is given by k; k3(SNR) is the signal-to-noise ratio adjustment function, specifically expressed as k3(SNR) = 1 - 0.3 * e -SNR / 20 Where SNR is the signal-to-noise ratio; k4(ρ env ) is the environmental density adjustment function, specifically expressed as k4(ρ env )=1+0.15·(ρ env / ρ0-1), where ρ env Here, ρ0 represents the standard environmental density; Δt1 and Δt2 represent the threshold offsets. The risk assessment matrix is ​​specifically represented as follows:

[0082]

[0083] In the formula, V risk For risk assessment matrix; v ij L represents the risk assessment weighting coefficient. crack P represents the crack size. crack This is the crack location coefficient; σ is the crack propagation rate; load This refers to the structural load state; υ i This is a risk assessment offset.

[0084] The specific implementation of step S06 involves using a multi-sensor data fusion algorithm for precise positioning. The formula for calculating the time difference of arrival is:

[0085]

[0086] In the formula, Δt ij S is the time difference of arrival between sensors i and j; i (t) and S j (t) represent the signals from sensors i and j, respectively; τ is the time delay variable; ∈ corr，ij For cross-correlation error term; SNR i and SNR j Here, represents the signal-to-noise ratio of sensors i and j, respectively. The formula for calculating the crack location coordinates is:

[0087]

[0088] In the formula, x crack y crack , z crack Let A be the spatial coordinates of the crack; let A be the sensor position matrix, specifically represented as follows: Where x i y i , z i Let r be the coordinates of the i-th sensor. i κ is the estimated distance from the i-th sensor to the crack; I is the regularization parameter; d is the identity matrix; i φ is the distance from the i-th sensor to the crack; N is the number of sensors; x , φ y , φ z This is the positioning error term.

[0089] The specific implementation of step S07 is to calculate the crack risk assessment index. The specific expression of the crack risk assessment index equation is as follows:

[0090]

[0091] In the formula, R index For crack risk assessment index; w h The weight of the h-th time-domain feature parameter; Θ h Let α be the h-th time-domain feature parameter; h The index of the h-th time-domain feature parameter; H is the number of time-domain feature parameters; u l The weight of the l-th frequency domain feature parameter; F l Let β be the l-th frequency domain characteristic parameter; l L is the exponent of the l-th frequency domain characteristic parameter; freq p represents the number of frequency domain feature parameters. n The weight of the nth spatial coordinate; x n y n , z n For the nth spatial coordinate component; γ n N is the nth spatial coordinate index; coordq represents the number of spatial coordinates; q represents the geometric dimension weight; D represents the number of coordinates in space. crack δ represents the geometric dimensions of the crack; C represents the geometric dimension exponent. fatigue ε is the material fatigue coefficient; fatigue The fatigue coefficient index; ζ risk This represents the risk assessment error term. The specific expression of the equation for calculating the first threshold is as follows:

[0092]

[0093] In the formula, Γ1 is the first threshold; σ ultimate S represents the material's ultimate strength. factor For safety factor; N cycle N is the current loop count; fatigue θ1 is the fatigue life; Φ is the fatigue life index; reliability For structural reliability indicators; h m The weight of the m-th historical fault data; D history，m This represents the m-th historical fault data; M history ι1 represents the number of historical fault data; ι1 represents the first threshold error term. The specific expression of the second threshold calculation equation is as follows:

[0094]

[0095] In the formula, Γ2 is the second threshold; K IC For the fracture toughness of the material; L critical K is the critical crack length. stress A is the stress concentration factor; load P is the load amplification factor. failure θ1 is the structural failure probability; θ2 is the failure probability exponent; s o The weight of the o-th additional factor; T o χ is the o-th additional factor; o ι2 is the index of the o-th additional factor; O is the number of additional factors; ι2 is the second threshold error term.

[0096] The vibration fatigue matrix is ​​established based on Miner's linear cumulative damage theory. By representing fatigue damage under different stress amplitudes in a matrix form, it achieves an accurate description of complex load histories. Compared with the traditional single fatigue curve method, this matrix can simultaneously consider the cumulative effect of multi-level loads and the influence of load sequences, significantly improving the accuracy of fatigue life prediction.

[0097] Based on the theory of modal superposition, the structural resonance matrix represents the dynamic characteristics of a structure as a combination of natural frequencies, damping ratios, and mode shapes. Compared with the traditional simplified single-degree-of-freedom model, this matrix can comprehensively describe the multi-order vibration characteristics of the structure and provides a sensitive basis for identifying structural parameter changes caused by cracks.

[0098] The coefficient update formula in the adaptive filtering algorithm is based on the minimum mean square error criterion and the exponential forgetting mechanism. By introducing a forgetting factor, it achieves adaptive adjustment to the time-varying environment. Compared with the traditional fixed coefficient filter, this algorithm can optimize the filtering performance in real time under strong noise environment and effectively improve the extraction accuracy of weak crack signals.

[0099] The signal separation equations in the vibration coupling equation set are based on independent component analysis and higher-order statistical theory. Through polynomial expansion and the introduction of differential terms, they achieve effective separation of nonlinear mixed signals. Compared to traditional linear separation methods, these equations can handle complex signal coupling relationships and significantly improve the separation effect of crack signals. The separation filter response function... Accurate modeling of time-varying characteristics was achieved through a finite impulse response structure.

[0100] The frequency domain transform equation, based on the short-time Fourier transform and structural response theory, achieves accurate analysis of non-stationary signals through the combination of a time-frequency window function and a resonance response term. Compared to the traditional fast Fourier transform, this equation can simultaneously preserve the time and frequency domain information of the signal, providing higher resolution for the identification of crack characteristic frequencies. The time-frequency window function... Time-frequency localization analysis was achieved by multiplying the Hanning window and the complex exponential function.

[0101] The feature extraction equation, based on wavelet transform and materials mechanics theory, achieves accurate extraction of crack geometric features through multi-scale analysis and coupling of material parameters. Compared with traditional time-domain analysis methods, this equation can effectively identify crack features at different scales, significantly improving the accuracy of feature parameter extraction. The wavelet basis functions are included. Multi-resolution decomposition was achieved through scaling and translation operations.

[0102] The dynamic threshold discrimination matrix, based on fuzzy logic and environmental adaptation theory, achieves dynamic adjustment of the recognition threshold through a nonlinear combination of multi-parameter functions. Compared with the traditional fixed threshold method, this matrix can automatically optimize the discrimination criteria according to real-time environmental conditions, effectively reducing the false alarm rate and false negative rate. The adjustment functions employ nonlinear functions such as hyperbolic tangent, sine, and exponential functions, accurately reflecting the influence of different environmental parameters on the threshold.

[0103] The risk assessment matrix is ​​based on multi-criteria decision-making and weight allocation theory. It achieves a comprehensive assessment of crack risk through weighted combination. Compared with the traditional single-indicator assessment method, this matrix can comprehensively consider the multi-dimensional characteristics of cracks and provide a scientific basis for the accurate determination of risk level.

[0104] The localization formula in the multi-sensor data fusion algorithm is based on least squares estimation and regularization theory. By introducing a regularization parameter, the influence of ill-conditioned matrices is effectively suppressed. Compared with traditional direct solution methods, this algorithm can achieve stable localization calculations even when sensor data contains noise and errors, significantly improving the reliability of position estimation. The sensor position matrix A is constructed through geometric relationships and distance normalization, ensuring the numerical stability of the localization equation.

[0105] The crack risk assessment index equation is based on multiple regression and power function fitting theory. By introducing nonlinear terms, it achieves accurate modeling of complex risk factors. Compared with traditional linear assessment methods, this equation can better reflect the interaction between various risk factors and provides a mathematical basis for the accurate quantification of risk levels.

[0106] The threshold calculation equation is based on reliability theory and fracture mechanics principles. It achieves accurate determination of the safety boundary through the product and exponential form of multiple factors. Compared with the traditional empirical threshold setting method, this equation can scientifically determine the discrimination criteria based on material properties and structural parameters, providing theoretical guarantee for the safe operation of the system.

[0107] To better understand and implement this invention, the following is an embodiment 2 of a specific application scenario: A technical team deployed a real-time crack monitoring system on a wind tunnel facility. The wind tunnel test section measures 2.4m × 2.4m × 6.0m, with a maximum operating Mach number of 2.5 and a maximum saturation pressure of 0.8MPa. The wind tunnel walls are made of high-strength alloy steel, which is at risk of fatigue cracking under long-term high-pressure vibration loads. The technical team selected the critical area at the connection between the wind tunnel contraction section and the test section as the monitoring target, as this area had historically exhibited numerous micro-cracks.

[0108] The technical team deployed 64 piezoelectric ceramic sensors in an 8×8 square array in the area to be monitored, following the method of this invention. The sensor spacing was set to 25mm, covering a monitoring area of ​​175mm×175mm. The sensors were lead-zirconate titanate piezoelectric ceramics with dimensions of φ10mm×2mm, a resonant frequency of 2.8MHz, and a sensitivity of 50pC / N. Epoxy resin adhesive was used for bonding, with the adhesive layer thickness controlled at 0.2mm, the curing temperature maintained at 23℃, and the curing time at 48 hours.

[0109] The technical team established the vibration fatigue matrix and structural resonance matrix through multi-frequency excitation tests. An electromagnetic exciter applied sinusoidal excitation within a frequency range of 10Hz to 800Hz, with the excitation force amplitude increasing in 10 increments from 50N to 800N. Simultaneously, an accelerometer was used to collect the wall vibration response, with the sampling frequency set to 16kHz. Through 100 hours of continuous testing, complete vibration fatigue data and structural dynamic characteristic parameters were obtained, as shown in Table 1.

[0110] Table 1 Dynamic Characteristic Parameters of Wind Tunnel Wall Structure

[0111] Modal order Natural frequency (Hz) Damping ratio Main vibration mode characteristics 1 85.2 0.008 First-order bending 2 158.7 0.012 First-order torsion 3 242.3 0.015 Second-order bending 4 336.8 0.018 Second-order torsion 5 445.1 0.021 Third-order bending 6 578.9 0.025 Local vibration

[0112] The technical team designed an adaptive filtering algorithm to preprocess the sensor signals. The filter employs a 128th-order finite impulse response structure, with a step size factor set to 0.005 and a forgetting factor set to 0.98. During wind tunnel operation, the system monitors signal quality in real time, automatically adjusting the filtering parameters when the signal-to-noise ratio falls below 18 dB. After filtering, the signal amplitude attenuation is controlled within 3.2%, and the phase delay is controlled within 1.5 degrees.

[0113] The technical team established a set of vibration coupling equations to extract crack feature signals. The signal separation equations employed six separation filters, each with a length of 64 points, and time delays of 0.125ms, 0.25ms, 0.375ms, 0.5ms, 0.625ms, and 0.75ms, respectively. The frequency domain transformation equations used a 2048-point Fast Fourier Transform with a Hanning window length of 1024 points, an overlap rate of 50%, and a frequency resolution of 7.8Hz. The feature extraction equations used Daubechies 4 wavelets for 8-level decomposition, with the material elastic modulus set to 210 GPa.

[0114] Under normal wind tunnel operation, the technical team continuously monitored for 720 hours, obtaining a large amount of vibration data and crack characteristic information. The system successfully identified three suspected crack areas and one confirmed crack area. The confirmed crack is located at coordinates (87.5 mm, 112.5 mm), with a length of approximately 2.3 mm, a depth of approximately 0.8 mm, and a propagation rate of 0.02 mm / day. The risk assessment results during the monitoring period are shown in Table 2.

[0115] Table 2 Statistical Table of Crack Risk Assessment Results

[0116] Time period (hours) First threshold Second threshold Risk assessment index Risk level 0-120 0.35 0.72 0.28 Safety 120-240 0.38 0.75 0.42 Suspected risks 240-360 0.36 0.73 0.58 Suspected risks 360-480 0.39 0.76 0.68 Suspected risks 480-600 0.37 0.74 0.77 Identify risks 600-720 0.40 0.78 0.82 Identify risks

[0117] The technical team constructed a dynamic threshold discrimination matrix and a risk assessment matrix. The baseline threshold was set to 0.35, and the reference vibration intensity for the vibration intensity adjustment function was 5 m / s². 2The maximum frequency of the main frequency adjustment function is 800Hz, and the standard environmental density of the environmental density adjustment function is 1.225kg / m³. 3 The risk assessment matrix adopts a 4×4 structure, with weighting coefficients of 0.30 for crack size, 0.25 for crack location, 0.25 for propagation rate, and 0.20 for load state.

[0118] The multi-sensor data fusion algorithm employs a combination of least squares estimation and regularization. The regularization parameter is set to 0.001, and the cross-correlation calculation window length is 512 points. Through joint positioning by 64 sensors, the crack location accuracy reaches 1.2 mm, and the geometric dimension measurement accuracy reaches 0.3 mm. The numerical stability of the positioning calculation is good, with the condition number controlled within 15.

[0119] The technical team set the calculation parameters for the crack risk assessment index. Time-domain characteristic parameters include root mean square value, peak factor, waveform factor, and impulse factor, with weighting coefficients of 0.3, 0.25, 0.25, and 0.2, respectively. Frequency-domain characteristic parameters include dominant frequency, band energy, frequency centroid, and spectral entropy, with weighting coefficients of 0.4, 0.3, 0.2, and 0.1, respectively. The material fatigue coefficient was set to 0.45 based on the fatigue characteristics of alloy steel. In the first threshold calculation, the safety factor was set to 2.5, and the structural reliability index was set to 0.995. In the second threshold calculation, the material fracture toughness was set to 45 MPa·m. 1 / 2 The critical crack length is 5 mm, the stress concentration factor is 2.8, the load amplification factor is 1.6, and the structural failure probability is set to 10. -5 .

[0120] During continuous monitoring, the system demonstrated stable and reliable performance. The sensor signal acquisition success rate reached 99.7%, data processing showed good real-time performance, and a single complete analysis cycle was 15 seconds. The system exhibited strong adaptability under different operating conditions, automatically adjusting monitoring parameters when wind tunnel operating parameters changed. The false alarm rate was controlled below 2.1%, and the missed alarm rate was controlled below 0.8%. The performance indicators of the monitoring system are shown in Table 3.

[0121] Table 3 Statistical Table of Monitoring System Performance Indicators

[0122] Performance indicators numerical values Traditional numerical methods Improvement range Crack detection accuracy (mm) 1.2 1.5 20％ Positioning error (mm) 0.8 1.0 20％ Response time (seconds) 15 18 17％ False alarm rate (%) 2.1 2.6 19％ Missed report rate (%) 0.8 1.0 20％ System reliability (%) 99.7 98.5 1％

[0123] During 720 hours of continuous operation, the system accurately warned of a serious crack risk once, issuing a shutdown maintenance recommendation 48 hours in advance. After shutdown and inspection, a fatigue crack with a length of 2.1 mm was indeed found at the warned location, with an error of only 9.5% compared to the system's predicted length of 2.3 mm. The technical team promptly carried out repairs, averting a potential safety incident.

[0124] This invention represents a significant technological advancement over traditional methods. First, it shifts from periodic detection to real-time continuous monitoring, greatly improving the timeliness and accuracy of crack detection. Second, through the collaborative modeling of vibration fatigue and structural resonance matrices, it accurately describes the crack initiation mechanism under complex vibration environments, providing a scientific basis for early identification. Third, the adaptive signal processing technology based on vibration coupling equations effectively solves the challenge of extracting weak signals in high-noise environments, significantly improving signal quality. Finally, multi-sensor data fusion and dynamic threshold discrimination technologies enable precise crack location and intelligent risk assessment, providing reliable technical support for maintenance decisions. These technological advancements comprehensively enhance the safety monitoring level of wind tunnel facilities, providing an effective solution for structural health monitoring under high-pressure vibration environments.

[0125] It should be noted that the variables involved in this invention are explained in detail in Tables 4 and 5.

[0126] Table 4. Variable Explanation Table (Part 1)

[0127]

[0128]

[0129] Table 5. Variable Explanation Table (Part Two)

[0130]

[0131] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for real-time monitoring of cracks in a wind tunnel under high pressure vibration conditions, characterized by, A plurality of piezoelectric ceramic sensors are arranged in the monitoring area of the wind tunnel wall to form a sensor array; a vibration fatigue matrix and a structure resonance matrix are established; an adaptive filtering algorithm is used to preprocess the piezoelectric ceramic sensor signals; a vibration coupling equation group for extracting crack characteristic signals is established, and time-domain characteristic parameters and frequency-domain characteristic parameters of the crack signals are obtained by solving the vibration coupling equation group; A dynamic threshold discrimination matrix and a risk assessment matrix are constructed; the crack position is accurately positioned through a multi-sensor data fusion algorithm; a crack risk assessment index is calculated according to a crack risk assessment index equation, and the safety state of the wind tunnel wall is discriminated based on the comparison between the crack risk assessment index and the threshold value; the vibration fatigue matrix is used to establish a noise model to effectively suppress high-frequency vibration noise; the influence of the crack on the dynamic characteristics of the structure is analyzed by using the structure resonance matrix; and the crack identification threshold is adjusted in real time by using the dynamic threshold discrimination matrix to adapt to different vibration conditions.

2. The method of real-time monitoring of cracks in a wind tunnel under high pressure vibration conditions according to claim 1, characterized in that, In the arrangement step of the sensor array, the sensor spacing is 10-50 mm, the piezoelectric ceramic sensors are firmly bonded to the wind tunnel wall with epoxy resin glue, and the contact between the piezoelectric ceramic sensors and the wind tunnel wall is ensured to be good.

3. The method of real-time monitoring of cracks in a wind tunnel under high pressure vibration conditions according to claim 2, characterized in that, In the step of establishing the vibration fatigue matrix and the structure resonance matrix, the vibration acceleration data of the wind tunnel wall at different frequencies are collected by an accelerometer, the vibration fatigue matrix is constructed to describe the fatigue accumulation characteristics of the material under cyclic loading, and the structure resonance matrix is constructed to identify the resonance response mode of the structure at the frequency.

4. The method of real-time monitoring of cracks in a wind tunnel under high pressure vibration conditions according to claim 3, wherein, In the step of preprocessing the piezoelectric ceramic sensor signals by using the adaptive filtering algorithm, a noise model is established by using the vibration fatigue matrix, the filter coefficients are adjusted by using the least mean square error criterion, and high-frequency vibration noise is effectively suppressed.

5. The method of real-time monitoring of cracks in a wind tunnel under high pressure vibration conditions according to claim 4, characterized in that, The vibration coupling equation group includes a signal separation equation, a frequency domain transformation equation, and a feature extraction equation, time-domain characteristic parameters and frequency-domain characteristic parameters of the crack signals are obtained by solving the vibration coupling equation group, and the influence of the crack on the dynamic characteristics of the structure is analyzed by using the structure resonance matrix.

6. The method of real-time monitoring of cracks in a wind tunnel under high pressure vibration conditions according to claim 5, wherein, The signal separation equation is used to separate the crack characteristic signals from the mixed signals, and the input includes the original piezoelectric ceramic sensor signals, the vibration fatigue matrix, the noise power spectral density, the filter coefficients, and the time window length, and the output is the separated crack signals.

7. The method of real-time monitoring of cracks in a wind tunnel under high pressure vibration conditions according to claim 6, characterized in that, The frequency domain transformation equation is used to convert the time-domain signals into frequency-domain signals and identify the crack characteristic frequencies, and the input includes the separated crack signals, the structure resonance matrix, the sampling frequency, the transformation window function, and the frequency resolution, and the output is the crack characteristic frequency spectrum.

8. The method of real-time monitoring of cracks in a wind tunnel under high pressure vibration conditions according to claim 7, characterized in that, The feature extraction equation is used to extract the geometric characteristic parameters of the crack from the frequency-domain signals, and the input includes the crack characteristic frequency spectrum, the material elastic modulus, the structure geometric parameters, the boundary conditions, and the load history, and the output is the time-domain characteristic parameters and the frequency-domain characteristic parameters.

9. The method of real-time monitoring of cracks in a wind tunnel under high pressure vibration conditions according to claim 8, wherein, The dynamic threshold discrimination matrix is used to adjust the crack identification threshold in real time to adapt to different vibration conditions, and the risk assessment matrix is used to comprehensively evaluate the crack propagation risk and the structural safety.

10. The method of real-time monitoring of cracks in a wind tunnel under high pressure vibration conditions according to claim 9, wherein, The multi-sensor data fusion algorithm uses the separated crack signals, time domain characteristic parameters and frequency domain characteristic parameters to calculate, and uses the time difference of arrival method and signal amplitude attenuation characteristics to determine the spatial coordinates and geometric dimensions of the cracks.

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