Self-adaptive imaging method and system for multi-damage detection of composite material

Abstract

The invention discloses a self-adaptive imaging method and system for multi-damage detection of a composite material, and belongs to the technical field of nondestructive detection. Through the technologies of frequency domain analysis filtering, wave field energy analysis, self-adaptive step length adjustment, self-adaptive weighted root-mean-square imaging algorithm and the like, the problem of multi-damage detection is effectively solved. According to the method, high-quality damage diagnosis can be realized under the condition of low signal-to-noise ratio, and the signal-to-noise ratio of an original signal is enhanced through frequency domain analysis and signal compensation; the damage information of different spatial scales is obtained by adjusting the damage index calculation step length, and the damage state of the composite material is reflected more comprehensively; based on an adaptive weighted root-mean-square damage imaging algorithm, an adaptive weighting coefficient is constructed, the positions, the number and the directions of a plurality of damages in the composite material are accurately identified, the damage detection accuracy is improved, the method has wide application prospects and important practical values, and a new solution is provided for the field of composite material multi-damage detection.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of nondestructive testing, and particularly relates to a self-adaptive imaging method and system for composite material multi-damage detection. BACKGROUND

[0002] Composite materials are widely used in aerospace, transportation and other fields due to their light weight, high strength, corrosion resistance and other advantages. However, composite materials are prone to internal damage such as delamination and cracks during manufacturing and use. These damages are highly concealed and difficult to detect, and if not discovered and repaired in time, they will affect the integrity and safety of the structure.

[0003] It is particularly urgent to develop nondestructive testing techniques that can effectively characterize and evaluate typical defects / damages for delamination damage in composite structures that are not easily detected. As a key means of nondestructive testing of composite materials, the propagation characteristics of ultrasonic waves in carbon fiber reinforced plastics (CFRP) can reveal internal defects and damages. In recent years, advanced sensors based on Doppler laser vibrometer (SLDV) have emerged in the field of ultrasonic testing. This technology acquires wave field data through extremely dense point grid scanning. Compared with the sparse measurement of traditional transducer arrays, dense wave field data contains more valuable damage information. However, the anisotropic properties of composite materials cause differences in wave field intensity and morphology on different propagation paths during wave propagation. Meanwhile, delamination damage in the material can be regarded as a secondary wave field emission source. Different shapes of delamination damage will produce damage wave fields with different morphologies and intensities. In the case of multiple damages in composite materials, the damage information carried in the wave field data becomes extremely complex in spatial scale, and is easily disturbed by strong incident waves and reflected waves. This leads to misjudgments of the number and severity of damages during damage monitoring, making it extremely difficult to identify composite material damages under multi-damage conditions.

[0004] To address this challenge, a wave field analysis method based on Doppler laser vibrometer is proposed, which aims to utilize the rich damage information carried by the wave field, filter out the interference of incident and reflected waves through wave number domain filtering method, separate out the damage information, and realize accurate damage quantification. However, the damage interaction wave is very weak relative to the incident and reflected waves, and it is difficult to identify. In the case of multiple damages in composite materials, the complexity of damage information will further increase, making the existing wave field analysis method unable to capture damage information at different spatial scales; such as CN109884187A discloses an ultrasonic guided wave field damage detection method based on compressed sensing, but the problem of evaluating and improving the coherence of the difference signal and the real wave field still needs to be solved. CN116429895A discloses a composite material damage imaging detection method and device based on local damage resonance, but the sensor array optimization, sound field suppression, comprehensive imaging algorithm improvement, quantitative evaluation and diagnosis of damage, etc. still need to be improved. In addition, the existing technology also has the following main problems: unable to comprehensively evaluate the damage characteristics of multi-modal guided waves in a wide frequency band range; difficult to reveal the damage information of different spatial scales in the wave field data, realize damage imaging under multiple damage conditions of composite materials; poor imaging quality when processing high noise data, unable to realize high-quality damage diagnosis using low signal-to-noise ratio data; lack of mechanism for adaptive evaluation of damage imaging value of wave field data at different times, difficult to accurately identify the position, number and direction of multiple damages in composite materials. SUMMARY

[0005] In view of the problems in the prior art that the existing composite material multi-damage detection method cannot comprehensively evaluate the damage characteristics of multi-modal guided waves in a wide frequency band range, the imaging quality is poor under high noise data, it is difficult to capture complex damage information at different spatial scales at the same time, and there is a lack of mechanism for adaptive evaluation of damage imaging value of wave field data, which makes it difficult to accurately identify the position, number and direction of multiple damages in composite materials, the present application aims to provide an adaptive imaging method and system for composite material multi-damage detection, and improve the accuracy and reliability of damage identification.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is as follows: The present application provides an adaptive imaging method for composite material multi-damage detection, comprising: S1, obtaining the guided wave full wave field original signal of the composite material to be tested, and obtaining the filtered original signal through frequency domain analysis filtering; S2, based on the filtered original signal, introducing a compensation function to obtain an enhanced original signal; analyzing the trend of wave field energy change with time, and selecting a time window containing damage information; S3, constructing a damage index based on the gradient direction of the wave field based on the enhanced original signal and the time window containing damage information, calculating the wave field damage index matrix, adjusting the calculation step of the wave field gradient method, and obtaining damage index information of different spatial scales; S4, based on different spatial scale damage information, constructing an adaptive distribution weighting coefficient based on damage index characteristics, evaluating wave field data at different times or different positions, and obtaining a damage image based on a root mean square imaging method.

[0007] S1 specifically includes: S11, obtaining a guided wave full wave field original signal of the composite material to be tested; S12, performing Fourier transform on the guided wave full wave field original signal to obtain a frequency domain signal, setting a predetermined frequency range, and performing band-pass filtering on the frequency domain signal to filter out environmental noise components outside the predetermined frequency range; S13, performing inverse Fourier transform on the filtered frequency domain signal to obtain a filtered original signal.

[0008] Further, the guided wave full wave field original signal of the composite material to be tested is obtained, a PZT crystal is used as a signal exciter, a measurement grid, a sampling frequency and a time response parameter are set, and full wave field original signal acquisition is performed in combination with the excitation signal; the acquired wave field data is saved in the form of a three-dimensional data set; the three-dimensional data set includes one time dimension and two spatial dimensions, and is represented in discrete form as (u,v,t) where u,v,t are the spatial coordinates and time coordinates of the measurement points, respectively. x, y, t x and y are the number of measurement points in the x and y directions, respectively, t is the number of time frames.

[0009] Further, the measurement grid has 300*300 measurement points; the sampling frequency is 100kHz-500kHz; and the time response is 1ms.

[0010] S2 specifically includes: S21, defining a wave field energy function based on the filtered original signal, introducing a compensation function, and obtaining an enhanced original signal; S22, analyzing the wave field energy curve of the enhanced original signal, determining the starting point and ending point of the time window in the wave field energy curve, and obtaining a time window containing damage information.

[0011] Further, in S21, the wave field energy function is defined as: wherein, E[r] is referred to as a wave field energy function, representing the trend of the change of the wave field energy with time, ​For the original wave field at the grid points ( i, j, r The wave field signal at point ) where and These are the number of measurement points in the x and y directions, respectively; It represents the number of time frames.

[0012] The normalized energy function is: ,in, This represents the normalized wavefield energy function. Let be the wave field energy function. The peak value of the wave field energy function.

[0013] The compensation function is: ,in, The compensation function represents the wave field. This represents the normalized wavefield energy function. This represents the number of frames at the peak point of the wave field energy curve.

[0014] The compensated wave field is: ,in, This represents the compensated wave field. [ i, j, r ] indicates the compensated wavefield signal at the grid points ( i, j, r The wave field signal at () location; Represents the original wave field. [ i, j, r ] indicates the original wave field at grid points ( i, j, r The wave field signal at () location; This is the compensation function.

[0015] Furthermore, in S22, the starting point of the time window is the peak point of the wave field energy curve, and the ending point of the time window is the time frame when the normalized energy drops to 10%.

[0016] S3 specifically includes: S31, based on the enhanced original signal and the time window containing damage information, calculate the gradient direction of the wave field at each point in space; S32, Based on the gradient direction information, construct a damage index based on the wave field gradient direction; S33, apply the constructed damage index to the entire wavefield, calculate the damage index at each location, and obtain the wavefield damage index matrix. S34. Adjust the calculation step size of the wave field gradient method, recalculate the wave field gradient direction and damage index matrix, extract damage information at different spatial scales, and obtain damage information at different spatial scales.

[0017] In S32, the damage index is: , wherein, is the final damage indicator, [ i, j, r ] is used to quantify the damage degree at position (i, j) and time r; gradv represents the wave field gradient, gradv [ i, j, r ] represents the size of the wave field gradient at the grid point ( i, j, r ); represents the compensated wave field signal, [ i, j, r ] represents the wave field signal of the compensated wave field signal at the grid point ( i, j, r ); [ i+k, j, r ] represents the wave field signal of the compensated wave field signal at the grid point ( i+k, j, r ); [ i-k, j, r ] represents the wave field signal of the compensated wave field signal at the grid point ( i-k, j, r ); [ i, j+ k, r ] represents the wave field signal of the compensated wave field signal at the grid point ( i, j+k, r ); h x and h y are the grid spacings in the x and y directions, respectively; k represents the calculation step size of the wave field gradient in the grid.

[0018] S4 specifically comprises: S41, based on different spatial scale damage indicator information, extracting statistical features of damage indicators, constructing adaptive allocation weighting coefficients, the damage information including damage size, shape, position and energy distribution; the statistical features include mean, variance, maximum and minimum; S42, using the adaptive allocation weighting coefficient for wave field data, weighting processing the wave field data to obtain the weighted processed wave field data; S43, based on the root mean square imaging method, image fusion is carried out on the weighted processed wave field data to obtain the final damage image, and the damage image includes the position, size and shape of the damage.

[0019] Further, the adaptive allocation weighting coefficient is:

[0020] wherein, wr To adaptively assign weighting coefficients, Representative array The mean of all elements in the set. This represents the calculation step size of the wavefield gradient in the grid. k The cumulative weighted sum within; Indicates the first i The weighted value of the item, and k This is the total number of items. Representing a time window Inside w r The minimum value, Indicates a time window; k This indicates the calculation step size of the wavefield gradient within the grid; The elements consist of all wavefield damage index values ​​within the time window. constitute.

[0021] This invention provides a system for implementing the above-described adaptive imaging method for multi-damage detection of composite materials, comprising: The signal acquisition and processing module is responsible for acquiring the original waveguide full-wave field signal of the composite material under test, and performing frequency domain analysis and filtering on the acquired original signal to obtain the filtered original signal. The signal enhancement and time window selection module enhances the signal by introducing a compensation function based on the filtered original signal; at the same time, it analyzes the trend of wave field energy change over time and selects a time window that includes damage information. The damage index construction and calculation module constructs a damage index based on the wavefield gradient direction based on the enhanced original signal; calculates the wavefield damage index matrix; and adjusts the calculation step size of the wavefield gradient method as needed to obtain damage information at different spatial scales. The adaptive weighting coefficient construction, image fusion, and damage imaging module constructs adaptive weighting coefficients based on damage information at different spatial scales and damage index features. The constructed adaptive weighting coefficients are used to weight the wavefield data, and the weighted wavefield data is fused using the root mean square imaging method to obtain the final damage image. The control and display module is responsible for the control of the entire system and the display of imaging results, including the coordination and scheduling between various modules such as signal acquisition, signal preprocessing, time window selection, damage index calculation, adaptive weighted root mean square imaging, and result output and display.

[0022] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described adaptive imaging method for multi-damage detection of composite materials.

[0023] The application provides a computer readable storage medium, which stores a computer program, and the computer program realizes the steps of the adaptive imaging method for composite material multi-damage detection when executed by a processor.

[0024] Compared with the prior art, the application has the following beneficial effects: The adaptive imaging method for composite material multi-damage detection provided by the application solves the multi-damage detection problem in the prior art through a series of innovative technologies such as frequency domain analysis filtering, wave field energy analysis, adaptive step adjustment and adaptive weighted mean square root imaging algorithm. The environmental noise in the signal acquisition process is filtered out through the frequency domain analysis method, and the original signal is enhanced in combination with the wave field energy analysis and the signal compensation method. The powerful ability of the wave field gradient square to process high-noise data can realize high-quality damage diagnosis under low signal-to-noise ratio conditions. Based on the distribution characteristics of the wave field energy, the optimal damage imaging time window is selected to improve the imaging quality. Compared with the traditional method, the application has the following significant advantages: the damage characteristics of multi-modal guided waves can be comprehensively evaluated in a wide frequency band range; high-quality damage diagnosis can be realized in a high-noise environment; the evaluation index of the damage imaging result is optimized by adaptively adjusting the damage index calculation step; based on the adaptive weighted mean square root damage imaging algorithm, the adaptive weighting coefficient based on the damage index characteristics is constructed to adaptively evaluate the damage imaging value of the wave field data at different times, and the positions, quantities and directions of multiple damages can be accurately identified under the composite material multi-damage working condition. Therefore, the method of the application has wide application prospects and important practical value in the field of composite material multi-damage detection.

[0025] Further, the signal-to-noise ratio of the original signal is significantly enhanced through frequency domain analysis filtering and signal compensation, thereby improving the clarity and reliability of damage imaging. In combination with the powerful ability of the wave field gradient square to process high-noise data, high-quality damage diagnosis can be realized under low signal-to-noise ratio conditions, and the problem that the imaging quality is poor in a high-noise environment and it is difficult to effectively extract damage information in the traditional method is solved.

[0026] Further, by adjusting the damage index calculation step, damage information of different spatial scales can be obtained, and damage imaging under the composite material multi-damage working condition is realized. The traditional method can usually only detect damage of a single scale, while the application can reveal the damage characteristics of different spatial scales in the wave field data through adaptive step adjustment, thereby more comprehensively reflecting the damage state of the composite material.

[0027] Further, based on the adaptive weighted root mean square damage imaging algorithm, the damage imaging value of the wave field data at different times can be adaptively evaluated by constructing the adaptive weighting coefficient based on the damage index characteristics. When processing multiple damage conditions, the traditional method often has difficulty in accurately distinguishing the position, number and direction of different damages. The adaptive weighting mechanism can more accurately identify the characteristics of multiple damages in the composite material, and significantly improve the accuracy of damage detection.

[0028] The system for implementing the adaptive imaging method for composite material multiple damage detection provided by the present application has significant technical advantages in signal acquisition, preprocessing, enhancement, time window selection, multi-scale damage information extraction, adaptive weighted root mean square imaging and system comprehensive control and management. These advantages make the method have wide application prospects and important practical value in the field of composite material multiple damage detection. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 Flowchart of the adaptive imaging method for composite material multiple damage detection of the present application; Figure 2 Schematic diagram of the sample of the embodiment of the present application; Figure 3 Signal comparison chart before and after filtering of the embodiment of the present application; Figure 4 Schematic diagram of damage index calculation in grid points under different calculation steps k of the embodiment of the present application; Figure 5 Wave field variation of the wave field image at different time points (t = 120us, 230us, 400us, 560us) of the embodiment of the present application, wherein a is t = 120us, b is 230us, c is 400us, and d is 560us; Figure 6 Comparison chart of imaging results of the embodiment of the present application and the control method, wherein a is the control, and b is the embodiment. DETAILED DESCRIPTION

[0030] In order to enable the personnel in the art to better understand the present application scheme, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the scope of protection of the present application.

[0031] Referring to the drawings Figure 1The adaptive imaging method for composite material multi-damage detection provided by the application specifically comprises the following steps: S1, obtaining a guided wave full wave field original signal of a composite material to be measured, and filtering the original signal through frequency domain analysis to obtain a filtered original signal; Referring to the accompanying Figure 2 S11, obtaining a guided wave full wave field original signal of a composite material to be measured.

[0032] (1) Signal excitation and collection A PZT crystal is selected as a signal excitation device, and the excitation signal is a five-period sine signal with a frequency of 65 kHz modulated by a Hanning window; an integrated signal generator is used to output an excitation signal with sufficient intensity; and a signal trigger is responsible for sending a trigger signal to the signal generator and a signal collection card to ensure that the signal measurement and recording process can be kept synchronous.

[0033] (2) Wave field data measurement and storage Measurement grid: a square grid of 300*300 measurement points is preset on the surface of the sample to be measured, and the grid spacing is 1.67 mm.

[0034] Sampling frequency and time response: at each measurement point, a total of 1 ms of time response is recorded at a sampling frequency of 500 kHz, generating 500 time frames.

[0035] Data saving: the time domain wave field signal of each measurement point is sorted and saved in the form of a three-dimensional data set (one time dimension and two space dimensions). The discrete form is represented as , wherein and are the number of measurement points in the x and y directions, respectively; is the number of time frames.

[0036] In the application, sldv does not need to be repeatedly measured and averaged, and a single measurement is used to reduce the measurement time and obtain a raw wave field signal with a high noise level.

[0037] S12, Fourier transforming the original signal, setting a preset frequency range, obtaining a frequency domain signal, and band-pass filtering the frequency domain signal to filter out environmental noise components outside the preset frequency range; S13, inverse Fourier transforming the filtered frequency domain signal to obtain a filtered original signal.

[0038] Specifically: frequency domain filtering: the original wave field signal is first filtered in the frequency domain to filter out noise components outside the main frequency range of the signal. After filtering, the original signal is converted into a usable signal, and damage information can be effectively extracted, meeting the needs of subsequent signal processing, such asFigure 3 As shown.

[0039] S2, based on the filtered original signal, introduce a compensation function to obtain the enhanced original signal; analyze the trend of wave field energy change over time, and select a time window containing damage information; S21. Based on the filtered original signal, define the wave field energy function, introduce a compensation function, and obtain the enhanced original signal; Perform global wavefield energy analysis on the original signal, for each time frame. r The wave field energy at a given location is defined as:

[0040] in, E[r] Known as the wave field energy function, it characterizes the trend of wave field energy changing over time. For the original wave field at the grid points ( i, j, r The wave field signal at point ) where and These are the number of measurement points in the x and y directions, respectively; It represents the number of time frames.

[0041] The normalized energy function is used for further analysis. The normalized energy function is as follows:

[0042] in, This represents the normalized wavefield energy function. Let be the wave field energy function. The peak value of the wave field energy function.

[0043] During wave field propagation within a material, energy dissipation due to material damping and / or energy radiation to the surrounding environment makes it difficult to detect damage at a distance from the actuator. To minimize this effect, we introduce a representation of... C[r] Compensation function:

[0044] in, The compensation function represents the wave field. This represents the normalized wavefield energy function. This represents the number of frames at the peak point of the wave field energy curve.

[0045] The compensation function of the CFRP plate under test is obtained from the above equation, and then the compensation wave field is obtained as follows:

[0046] in, This represents the compensated wave field. [ i, j, rrepresents the wavefield signal of the compensated wavefield signal at the grid point (x, y) ; i, j, r represents the original wavefield, represents the wavefield signal of the original wavefield at the grid point (x, y) ; [ i, j, r i, j, r represents the wavefield signal of the original wavefield at the grid point (x, y) ; is a compensation function.

[0047] S22, analyze the wavefield energy curve of the enhanced original signal, determine the starting point and the ending point of the wavefield energy curve, and obtain a time window containing damage information.

[0048] The wavefield energy curve is analyzed to determine the time window of damage imaging, and the peak point of the wavefield energy curve is selected as the starting point r a , and the guided wave is in an excited state before the starting point r a , at which time the damage information carried in the wavefield data is less. The ending point is selected at the time frame when the normalized energy drops to a given threshold (10% in this example) r b , and the wavefield energy tends to zero after the ending point r b , and the information in the wavefield data is dominated by noise components.

[0049] S3, based on the enhanced original signal and the time window containing damage information, construct a damage index based on the gradient direction of the wavefield, calculate the wavefield damage index matrix, adjust the calculation step size of the wavefield gradient method, and obtain damage index information of different spatial scales.

[0050] S3 specifically includes: S31, based on the enhanced original signal and the time window containing damage information, calculate the gradient direction of the wavefield at each point in space; S32, construct a damage index based on the gradient direction of the wavefield according to the gradient direction information; S33, apply the constructed damage index to the entire wavefield, calculate the damage index of each position, and obtain a wavefield damage index matrix; S34, adjust the calculation step size of the wavefield gradient method, recalculate the wavefield gradient direction and the damage index matrix, extract damage information at different spatial scales, and obtain damage information at different spatial scales.

[0051] ​The vibration signal of the wave field is relatively stable in normal propagation process, and the change rate of the vibration signal of the wave field in space at a moment has strong regularity. When the wave field propagates to the damage, the change rate of the vibration signal will be suddenly changed. The change rate of the wave field propagation can effectively reveal the damage. The gradient as an index for revealing the change rate of the physical field can well characterize the sudden change of the wave field caused by the damage in the wave field propagation process, and provide a more sensitive damage index for damage imaging.

[0052] (1) Wave field gradient The wave field gradient is defined as the change rate of the vibration signal of the wave field at a spatial point:

[0053] wherein, gradv represents the wave field gradient, gradv ( x, y, z ) represents the wave field gradient at the three-dimensional spatial point ( x, y, z ); represents the partial derivative of the compensated wave field in the x direction; represents the partial derivative of the compensated wave field in the y direction.

[0054] In the case of discrete wave field data, the first-order partial derivative in the formula can be approximated by the central difference method, and the discrete form of the wave field gradient is:

[0055] wherein, gradv represents the wave field gradient, gradv ( i, j, r ) represents the size of the wave field gradient at the grid point ( i, j, r ); represents the compensated wave field signal, [ i, j, r ] represents the wave field signal of the compensated wave field signal at the grid point ( i, j, r ); [ i+k, j, r ] represents the wave field signal of the compensated wave field signal at the grid point ( i+k, j, r ); [ i-k, j, r ] represents the wave field signal of the compensated wave field signal at the grid point ( i-k, j, r ); h x and h y are the grid spacings in the x and y directions, respectively; k represents the calculation step of the wave field gradient in the grid.

[0056] (2) Calculation step adjustment Composite materials have anisotropic properties, and the wave field intensity on different propagation paths during wave field propagation is different, and the form of wave field propagation is also different. At the same time, the delamination damage in the material can be regarded as a secondary wave field emission source, and the damage wave field generated by delamination damage of different shapes will also have differences in form and intensity. In the damage detection process, the damage location (representing different wave field propagation paths) and the damage shape are unknown, so for the damage detection of a multi-damage plate, the calculation step k needs to be adjusted to ensure that all damage information carried by the wave field is captured completely. As shown in the accompanying drawings, it can be seen that the damage indicators in the grid points are calculated differently under different calculation steps, and the adjustment of the calculation step k is a key step in composite material damage detection. The method of the present application can ensure that all damage information carried by the wave field is captured completely, thereby improving the accuracy and reliability of the detection. Figure 4

[0057] (3) Wave field gradient square as damage indicator Because the collected is a low signal-to-noise ratio wave field, there are a large number of noise points in the wave field gradient image, which will interfere with the distinguishability of the damage area in the final damage image. It is necessary to further suppress the noise component in the wave field and highlight the difference between the damage indicator anomaly caused by the damage and the noise anomaly, so the wave field gradient square is used as the final damage indicator: wherein, is the final damage indicator, [ i, j, r ] is used to quantify the damage degree at position (i, j) and time r ; gradv represents the wave field gradient, gradv [ i, j, r ] represents the size of the wave field gradient at the grid point ( i, j, r ); represents the compensated wave field signal, [ i, j, r ] represents the wave field signal of the compensated wave field signal at the grid point ( i, j, r ); [ i+k, j, r ] represents the wave field signal of the compensated wave field signal at the grid point ( i+k, j, r ); [ i- k, j, r ] represents the wave field signal of the compensated wave field signal at the grid point ( i-k, j, r ); [ i, j+k, r ​represents the compensated wavefield signal at the grid point ( i, j+k, r ) ; h x and h y are x and y the grid spacing in the x and y directions, respectively; k represents the step size in the grid for the computation of the wavefield gradient.

[0058] S4, based on different spatial scale damage information, an adaptive allocation weighting coefficient based on damage index characteristics is constructed, wavefield data at different times or different positions is evaluated, and image fusion is performed based on a root mean square imaging method to obtain a damage image.

[0059] S41, based on different spatial scale damage index information, statistical characteristics of the damage index are extracted, and an adaptive allocation weighting coefficient is constructed, the damage information includes damage size, shape, position and energy distribution; the statistical characteristics include mean, variance, maximum and minimum; S42, the adaptive allocation weighting coefficient is used for wavefield data, and the wavefield data is weighted processed to obtain the weighted processed wavefield data; S43, based on the root mean square imaging method, the weighted processed wavefield data is image fused to obtain a final damage image, the damage image includes damage position, damage size and damage shape.

[0060] First, the wavefield imaging value in the selected time window period is evaluated, and then image fusion is performed to create a final damage image:

[0061] wherein, AWRMS value is used to represent the damage degree in space, AWRMS [ i, j ] represents the average weighted root mean square damage value at the spatial position ( i, j ) ; r a and r b represent the starting point and the ending point of the selected time window period, respectively; [ i, j, r ] is used to quantify the damage degree at position ( i, j ) and time r ; w r represents an adaptive weighting coefficient, and the weight coefficient w rThe construction of this system enables the value calculation process to adaptively evaluate the value of a certain frame of wavefield data in damage imaging, and to differentiate the contribution of wavefield data in different time frames in image fusion in the form of weight allocation, so as to more effectively highlight the damage information carried in low signal-to-noise ratio data.

[0062] Define array The elements are composed of Composition, record To be Arranged from largest to smallest k There are several values, therefore the adaptive weighting coefficients are... w r Defined as:

[0063] in, Representative array The mean of all elements in the set. Representing a time window Inside w r The minimum value, k This represents the calculation step size of the wavefield gradient within the grid; the ability of the adaptive weighted root mean square imaging algorithm to evaluate the imaging value of wavefield damage depends on the weighting coefficients. w r The construction of the wavefield depends on the imaging value of a particular frame, which in turn depends on the impairment index of that frame, i.e., the difference in the wavefield impairment index. Therefore, the weighting coefficients... w r The key to possessing the characterization capability of a certain frame of wavefield imaging lies in... w r The differences in wavefield damage indices are reflected in the formula construction.

[0064] See appendix Figure 5 By presenting the wavefield variations at different time points (t = 120μs, 230μs, 400μs, 560μs), the propagation and reflection of the wavefield in the composite material are demonstrated, reflecting the damage information inside the material. It is evident that the imaging method of this invention, through frequency domain filtering, signal compensation, time window selection, wavefield gradient calculation, and adaptive weighted root mean square imaging, can effectively extract and analyze damage information from wavefield data, accurately identifying multiple damage locations even under low signal-to-noise ratio conditions. This method provides an efficient and accurate solution for damage detection in composite materials.

[0065] See appendix Figure 6Compared with the signal distribution of damage position in the image of the control method ("Guided wavefield curvature imaging of invisible damage in composite structures", G.G. Sha, H. Xu, M. Radzienski, M.S. Cao, W. Ostachowicz, Z.Q. Su, Mech. Syst. Signal Proc., 150 (2021) 16.), the damage characteristics are not obvious, and it is difficult to accurately identify and locate the damage; compared with the imaging results of the control method, the signal of the damage position is more concentrated, the damage characteristics are more obvious, and the damage can be more clearly identified and located, so the method provided by the present application has higher damage identification accuracy and positioning precision, can more effectively highlight the damage characteristics, and improve the detection effect.

[0066] The embodiment proposes an adaptive weighted root mean square imaging method for composite material multi-damage detection, which can effectively extract and analyze damage information in wave field data through steps such as frequency domain filtering, signal compensation, time window selection, wave field gradient calculation and adaptive weighted root mean square imaging, and can accurately identify the positions of multiple damages even under low signal-to-noise ratio conditions, so that the method provides an efficient and accurate solution for composite material damage detection.

[0067] The above content only illustrates the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made according to the technical idea of the present application on the basis of the technical solution falls within the protection scope of the claims of the present application.

Claims

1. An adaptive imaging method for composite multi-damage detection, characterized in that, The method comprises the following steps: S1, obtaining the guided wave full wave field original signal of the composite material to be tested, filtering the original signal through frequency domain analysis to obtain the filtered original signal; S2, based on the filtered original signal, introducing a compensation function to obtain an enhanced original signal; analyzing the trend of the wave field energy changing with time, and selecting a time window containing damage information; S3, based on the enhanced original signal and the time window containing damage information, constructing a damage index based on the gradient direction of the wave field, calculating the wave field damage index matrix, adjusting the calculation step of the wave field gradient method, and obtaining damage index information of different spatial scales; S4, based on the damage information of different spatial scales, constructing an adaptive allocation weighting coefficient based on the characteristics of the damage index, evaluating the wave field data at different times or different positions, and performing image fusion based on the root mean square imaging method to obtain a damage image.

2. The adaptive weighted root mean square imaging method for composite multi-damage detection according to claim 1, wherein, S1 specifically comprises: S11, obtaining the guided wave full wave field original signal of the composite material to be tested; S12, performing Fourier transform on the original signal, setting a predetermined frequency range, obtaining a frequency domain signal, and performing band-pass filtering on the frequency domain signal to filter out environmental noise components outside the predetermined frequency range; S13, performing inverse Fourier transform on the filtered frequency domain signal to obtain the filtered original signal.

3. The self-adaptive imaging method for composite multi-damage detection according to claim 1, wherein, S2 specifically comprises: S21, based on the filtered original signal, defining a wave field energy function, introducing a compensation function, and obtaining an enhanced original signal; S22, analyzing the wave field energy curve of the enhanced original signal, determining the starting point and ending point of the wave field energy curve, and obtaining a time window containing damage information.

4. The self-adaptive imaging method for composite multi-damage detection according to claim 3, wherein, The starting point of the wave field energy curve is the peak point of the wave field energy curve, and the ending point of the wave field energy curve is the time frame when the normalized energy drops to 10%.

5. The self-adaptive imaging method for composite multi-damage detection according to claim 1, wherein, S3 specifically comprises: S31, based on the enhanced original signal and the time window containing damage information, calculating the gradient direction of the wave field at each point in space; S32, constructing a damage index based on the gradient direction of the wave field according to the gradient direction information; S33, applying the constructed damage index to the entire wave field to calculate the damage index of each position and obtain a wave field damage index matrix; S34, adjusting the calculation step of the wave field gradient method, recalculating the wave field gradient direction and the damage index matrix, extracting damage information at different spatial scales, and obtaining different spatial scale damage information.

6. The self-adaptive imaging method for composite multi-damage detection according to claim 1, wherein, In S32, the damage index is: ； wherein, is the final damage indicator, [ i, j, r ] quantifies the damage degree at position (i, j) and time r; gradv denotes the wavefield gradient, gradv [ i, j, r ] denotes the magnitude of the wavefield gradient at grid point i, j, r ; denotes the compensated wavefield signal, [ i, j, r ] denotes the wavefield signal of the compensated wavefield signal at grid point i, j, r ; [ i+k, j, r ] denotes the wavefield signal of the compensated wavefield signal at grid point i+k, j, r ; [ i-k, j, r ] denotes the wavefield signal of the compensated wavefield signal at grid point i-k, j, r ; [ i, j+ k, r ] denotes the wavefield signal of the compensated wavefield signal at grid point i, j+k, r ; h x and h y are the grid spacings in x and y direction, respectively; k denotes the computation step size of the wavefield gradient in the grid.

7. The self-adaptive imaging method for composite multi-damage detection according to claim 1, wherein, S4 specifically comprises: S41, based on the damage index information of different spatial scales, extracting statistical characteristics of the damage index, constructing an adaptive allocation weighting coefficient, and the damage information includes damage size, shape, position and energy distribution; the statistical characteristics include mean, variance, maximum and minimum; S42, applying the adaptive allocation weighting coefficient to the wave field data to perform weighted processing on the wave field data, and obtaining weighted processed wave field data; S43, based on the root mean square imaging method, image fusion is performed on the wave field data after the weighting processing to obtain a final damage image, and the damage image includes the position, size and shape of the damage.

8. A system for implementing the self-adapting imaging method for multiple damage detection in composite materials according to any one of claims 1 to 7, characterized in that, The method comprises the following steps: A signal acquisition and processing module is responsible for acquiring the guided wave full wave field original signal of the composite material to be tested, and performing frequency domain analysis filtering on the acquired original signal to obtain a filtered original signal. A signal enhancement and time window selection module is based on the filtered original signal, introduces a compensation function to enhance the signal, and analyzes the trend of the wave field energy change with time to select a time window containing damage information. A damage index construction and calculation module is based on the enhanced original signal and the time window containing damage information, constructs a damage index based on the wave field gradient direction, calculates a wave field damage index matrix, and adjusts the wave field gradient damage index calculation step size as needed to obtain damage information of different spatial scales. An adaptive weighting coefficient construction, image fusion and damage imaging module is based on the damage information of different spatial scales, constructs an adaptive distribution weighting coefficient based on the damage index characteristics. The wave field data is weighted using the constructed adaptive weighting coefficient, the wave field data after the weighting processing is image fused based on the root mean square imaging method, and a final damage image is obtained. A control and display module is responsible for the control of the entire system and the display of the imaging results, including the coordination and scheduling between various modules such as signal acquisition, signal preprocessing, time window selection, damage index calculation, adaptive weighting root mean square imaging, and result output and display. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the adaptive imaging method for composite material multi-damage detection in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to realize the steps of the adaptive imaging method for composite material multi-damage detection in any one of claims 1-7.

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