Method for non-reference damage detection of metal thin-walled structure based on synchronous guided wave excitation

Abstract

The present application relates to the technical field of nondestructive testing, and discloses a metal thin-walled structure damage detection method without reference based on synchronous guided wave excitation, which comprises the following steps: S1, a metal thin-walled structure damage detection platform is built to obtain a time-domain receiving signal; S2, the time-domain receiving signal is sparsely sampled; S3, a dictionary matrix is constructed by using a dispersion curve; S4, a compressed sensing equation is constructed; S5, a reconstructed signal is obtained to establish a matching image; and S6, a damage point is obtained according to a grid value. The present application sends an excitation signal in a synchronous excitation mode, and damage positioning is performed according to the compressed sensing principle by using only one measuring point to receive a signal, thereby avoiding the limitation of using a baseline signal as a control and solving the problem that a baseline signal is difficult to obtain in an actual application scenario.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, and in particular to a reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation. Background Technology

[0002] With the rapid development of industries such as aerospace, automotive, and energy, practical engineering applications are constantly placing new demands on the stiffness and strength of materials. However, due to the complex manufacturing process and harsh service environment of thin-walled structures, composite thin-walled structures are prone to damage such as cracking, delamination, and debonding under impact and alternating loads, which will significantly reduce the service life of equipment. Therefore, in order to ensure the safe operation of equipment and prevent serious accidents, it is crucial to locate and assess the damage to thin-walled structures.

[0003] Non-destructive testing (NDT) technology is an effective means to ensure the safe operation of equipment. Technological advancements have spurred new testing solutions, and multi-sensor array imaging technology has gradually attracted the attention of scholars both domestically and internationally. However, using multi-sensor arrays for damage localization has many drawbacks and limitations. For example, dense deployment of multiple sensors may lead to signal superposition or interference, reducing signal quality; multiple sensor arrays generate a large amount of data, making signal processing complex; and the signals received by each sensor are difficult to synchronize in the time domain, hindering signal fusion and processing. These limitations all affect the accuracy of damage localization. To address these limitations, this invention proposes a method for sparse reconstruction of guided wavefields based on compressed sensing, which can accurately reconstruct the complete wavefield with a small number of measurement points. The principle of compressed sensing utilizes the sparsity of signals to reconstruct the complete signal from a small amount of randomly sampled data. It accurately and effectively reconstructs sparse signals from highly undersampled measurements, significantly reducing the number of sampling points and achieving efficient data measurement. Against this backdrop, this invention proposes a reference-free damage detection method for thin-walled structures based on synchronous guided wave excitation. Under synchronous excitation, the damage is located by sparsely reconstructing the guided wave signal. The data acquisition process requires only one signal excitation and single-point signal reception, and there is no need to measure the baseline signal. The guided wave signal is reconstructed using the compressed sensing principle to achieve damage location. Summary of the Invention

[0004] The purpose of this invention is to achieve rapid detection of damage in thin-walled structures using only a single measuring point guided wave signal and compressed sensing algorithm. This invention provides a reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation, which can accurately locate the damage position in the thin-walled metal structure and greatly improve the efficiency of the signal measurement process. Specifically, it includes the following steps:

[0005] S1, Build a damage detection platform for thin-walled metal structures and obtain the time-domain received signal x(t);

[0006] Paste L + 1 PZTs on the thin - walled metal structure, where one PZT is the measurement point and the other L PZTs are excitation points, and L is a positive integer greater than 1; the signal generator generates the original damage detection signal, which is amplified and then input to the excitation points as the excitation signal to generate guided waves in the thin - walled structure. The oscilloscope collects the signal through the measurement point to obtain the time - domain received signal x(t).

[0007] S2. Perform sparse sampling on the time - domain received signal x(t).

[0008] Perform Fourier transform on the time - domain received signal x(t) to solve the corresponding frequency - domain received signal X(ω), and perform random sampling to obtain the sparse measurement signal Y(ω).

[0009] S3. Construct a dictionary matrix using the dispersion curve.

[0010] Construct a dictionary matrix using the dispersion curve. The dictionary matrix A is as follows:

[0011]

[0012] where M and N represent the number of rows and columns respectively, and M < N. The element in the m - th row and n - th column of the dictionary matrix A is a mn , a mn = e jr(n)k(m) , j is the imaginary unit, r(n) represents the n - th element in the guided - wave propagation distance matrix, and k(m) represents the m - th wave number of the A0 mode of the guided - wave signal.

[0013] S4. Construct a compressed sensing equation.

[0014] S5. Obtain the reconstructed signal Establish a matching image.

[0015] According to the compressed sensing equation, use the convex optimization linear programming problem to obtain the reconstructed signal

[0016] Match the elements at the same positions of the reconstructed signal and the guided - wave propagation distance matrix r. Take r as the abscissa,[[]] as the ordinate to obtain the matching image of the guided - wave propagation distance and the reconstructed signal.

[0017] S6. Obtain the damage point according to the grid value.

[0018] In the XY coordinate system established on the plane where the thin - walled metal structure is located, after meshing the thin - walled metal structure, calculate the sum of the distances S i (x,y) from the center point of each grid to each excitation point and measurement point. In the matching image, search for the corresponding i (x,y) with S The vertical axis value represents the S value of each grid center point. i Multiplying (x, y) gives the grid value P(x, y) for each grid cell:

[0019] Based on the grid values, the two grids with the largest grid values ​​are obtained. One grid is the measurement point, and the other is the damage point. Since the location of the measurement point is known, the location of the damage point is obtained.

[0020] Preferably, the thin-walled metal structure in S1 is an aluminum plate.

[0021] Preferably, the number of excitation points in S1 is L = 5.

[0022] Preferably, in step S2, the time-domain received signal x(t) is sparsely sampled; specifically:

[0023] (1) Perform a Fourier transform on the time-domain received signal x(t) to obtain the corresponding frequency-domain received signal X(ω):

[0024]

[0025] Where ω is frequency, t is time, and i is the imaginary unit;

[0026] (2) Randomly sample the frequency domain received signal X(ω) to obtain the sparse measurement signal Y(ω):

[0027] Y(ω)={Y(1), Y(2), …, Y(k), …, Y(M)} (2)

[0028] Y(k)=X(ω k (3)

[0029] Where Y(k) is the frequency point ω of the received signal X(ω) in the frequency domain. k The signal obtained by random sampling at point ω k It is the kth sampling frequency point, k = 1, 2, ..., M; M is the total number of samples.

[0030] Preferably, in S3, the waveguide propagation distance matrix r is a 1*N matrix, the element values ​​in the waveguide propagation distance matrix are uniformly distributed from small to large, the interval between each element value is 1 / N, the value is taken starting from non-zero, and the maximum value must exceed the maximum waveguide distance from the excitation point to the measurement point after passing through the damage point.

[0031] Preferably, the compressed sensing equation in S4 is:

[0032]

[0033] Where Y(ω) is the sparse measurement signal obtained by randomly sampling the frequency domain received signal X(ω), and A is the dictionary matrix. To reconstruct the signal, This is the transpose of the reconstructed signal.

[0034] Preferably, in step S5, a convex optimization linear programming problem is used to obtain the reconstructed signal, specifically:

[0035] The linear optimization problem is described as follows:

[0036]

[0037] Where ||·||1 represents the l1 norm, ||·||2 represents the l2 norm, and λ1 and λ2 are the first and second weighting parameters for adjusting the weights of the l1 and l2 norms, respectively, to reconstruct the signal. It is a one-dimensional matrix of size 1*N.

[0038] Preferably, step S6 further includes: displaying each grid using a different color according to the grid value, and visually displaying the location of the measuring point and the damage point based on the displayed grid image.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] 1. This invention uses synchronous excitation to acquire signals, which makes the signal acquisition process more efficient and more in line with practical engineering applications.

[0041] 2. This invention reconstructs signals based on the principle of compressed sensing, and uses only one measuring point to receive signals for damage localization. It has high computational efficiency and can realize rapid damage localization of thin-walled structures.

[0042] 3. This invention avoids the limitation of using baseline signals as a reference and overcomes the problem of difficulty in obtaining baseline signals in practical application scenarios. Attached Figure Description

[0043] Figure 1 The flowchart shows a reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation.

[0044] Figure 2 A schematic diagram of the constructed metal thin-walled structure damage detection platform;

[0045] Figure 3 This is a schematic diagram showing the layout of the excitation points and measurement points in the embodiment;

[0046] Figure 4 The time-domain waveform of the signal received at the measuring point;

[0047] Figure 5 This is a schematic diagram of a guided wave propagation model;

[0048] Figure 6 This is a schematic diagram of the reconstructed signal in the matching image in the embodiment;

[0049] Figure 7 This is a damage localization effect diagram obtained by multiplication in the embodiment;

[0050] Figure 8 This is a diagram showing the damage localization effect obtained by the addition method in the embodiment. Detailed Implementation

[0051] To better understand the technical solution of the present invention, the specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The same reference numerals in the drawings indicate elements with the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0052] This invention is a reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation, such as... Figure 1 As shown, the specific steps are as follows:

[0053] S1, a thin-walled structure damage detection platform is built to obtain the time-domain received signal x(t).

[0054] like Figure 2 As shown, a damage detection platform for thin-walled metal structures is constructed. Specifically, L+1 lead zirconate titanate (PZT) piezoelectric ceramic sheets are attached to the thin-walled metal structure, with one PZT serving as a measurement point and the other L PZTs serving as excitation points, where L is a positive integer greater than 1. A signal generator generates the original damage detection signal, which is then amplified by a power amplifier. The amplified signal is input to the excitation points as an excitation signal to generate guided waves in the thin-walled structure. An oscilloscope acquires the signal through the measurement points to obtain the time-domain received signal x(t).

[0055] In this embodiment, an aluminum plate with a thickness of 1 mm and a size of 200 mm * 200 mm is used as an example for the thin-walled metal structure. Five excitation points are set on the surface of the aluminum plate being tested. Excitation signals are simultaneously input to these five excitation points. The specific layout of the test points and signal excitation points is as follows. Figure 3 As shown, the XY coordinate system is established on the plane containing the aluminum plate, with the origin typically located at the lower left corner of the plate. An oscilloscope is connected to the lead zirconate titanate piezoelectric ceramic plate at the measurement point on the aluminum plate. The oscilloscope is used to acquire the guided wave generated by the excitation signal to obtain the time-domain received signal x(t). In this embodiment, the waveform of the time-domain received signal is as follows: Figure 4 As shown. In a thin-walled metal structure, the guided waves generated by the excitation signal sometimes pass through the damage point and sometimes do not, such as... Figure 5As shown in the figure, the time-domain received signal collected by the measurement point is the superposition of all guided waves that have not passed through the damaged point and the guided waves that have passed through the damaged point.

[0056] S2, perform sparse sampling on the time-domain received signal x(t).

[0057] One of the prerequisites for using the principle of compressive sensing is that the original signal must be sparse, that is, the signal has fewer non-zero elements. However, in actual situations, the time-domain received signal x(t) is not sparse. Therefore, sparse sampling is first performed on it to meet the prerequisite conditions of compressive sensing. The specific operation is as follows:

[0058] (1) Perform Fourier transform on the time-domain received signal x(t) to solve the corresponding frequency-domain received signal X(ω):

[0059]

[0060] where ω is the frequency, t is the time, and i is the imaginary unit.

[0061] (2) Perform random sampling on the frequency-domain received signal X(ω) to obtain the sparse measurement signal Y(ω):

[0062] Y(ω) = {Y(1), Y(2), …, Y(k), …, Y(M)} (2)

[0063] Y(k) = X(ω k ) (3)

[0064] where Y(k) is the signal obtained by randomly sampling at the frequency point ω k of the frequency-domain received signal X(ω), ω k is the kth sampling frequency point, k = 1, 2, …, M; M is the total number of samplings.

[0065] S3, construct a dictionary matrix using the dispersion curve.

[0066] Construct a dictionary matrix using the dispersion curve. The dictionary matrix A is:

[0067]

[0068] where M and N represent the number of rows and columns respectively, and M < N. The element in the mth row and nth column of the dictionary matrix A is a mn , a mn = e jr(n)k(m) , j is the imaginary unit, r(n) represents the nth element in the guided wave propagation distance matrix, and k(m) represents the mth wave number of the A0 mode of the guided wave signal.

[0069] In this embodiment, M = 400 and N = 1000 are set. Since the size of the aluminum plate is 200mm * 200mm, it can be known that the maximum guided wave distance from the excitation point through the damage point to the measurement point will not exceed 600mm. Therefore, the guided wave distance value is set between 0 and 600mm. In the guided wave propagation distance matrix r which is a 1*N matrix, the interval distance of each element value is 1 / N. Therefore, in this embodiment, the guided wave propagation distance matrix r is a one-dimensional matrix of 1*1000, and the interval distance of each element value is set to 600 / 1000. Therefore, r = [0.6, 1.2, …, 600]. According to the above description, the elements in the guided wave propagation distance matrix r are evenly distributed from small to large, starting from a non-zero value, and the maximum value must exceed the maximum guided wave distance from the excitation point through the damage point to the measurement point. The A0 mode of the guided wave signal is a one-dimensional sequence obtained according to the material and thickness of the metal thin-walled structure. When M = 400, the first 400 values are taken to form a one-dimensional matrix of 1*400.

[0070] S4. Construct a compressive sensing equation.

[0071] The compressive sensing equation is:

[0072]

[0073] where Y(ω) is the sparse measurement signal obtained by randomly sampling the frequency-domain received signal X(ω), A is the dictionary matrix, is the reconstructed signal, is the transpose of the reconstructed signal.

[0074] S5. Obtain the reconstructed signal

[0075] Since M < N, the above equation is an underdetermined equation and cannot be directly solved. Therefore, the problem is usually transformed into a convex optimization linear programming problem to obtain the reconstructed signal. This linear optimization problem is described as:

[0076]

[0077] where ||·||1 represents the l1 norm, ||·||2 represents the l2 norm, λ1 and λ2 are the first weight parameter and the second weight parameter that respectively regulate the weights of the l1 norm and the l2 norm. The two weight parameters will affect the computational complexity and the sparsity of the result, and the parameters can be adjusted according to the actual signal. In the signal processing process of this article, λ1 = 100 and λ2 = 1 are taken for parameter adjustment.

[0078] Solve formula (5) to obtain the reconstructed signal which is a one-dimensional matrix of 1*N.

[0079] Let Match the elements at the same position in r, using r as the x-coordinate. Using the vertical axis as the ordinate, a matching image of the guided wave propagation distance and the reconstructed signal is plotted. In this embodiment, the matching image of the guided wave propagation distance and the reconstructed signal is as follows: Figure 6 As shown, the blue curve represents the reconstructed signal. Reconstructed signal The values ​​of the elements in the middle are called sparsity values. In the matching image, the x-coordinate of the higher value represents the distance (l0) between the excitation point and the measurement point obtained by solving, or the distance (l1+l2) between the excitation point and the measurement point after passing through the damage point.

[0080] To prove the correctness of the above theory, the aluminum plate is used as the test material in this embodiment. Therefore, as... Figure 4 The diagram shows that not only can the distance (l0) between the excitation point and the measurement point be determined, but also the distance (l1+l2) between the excitation point and the measurement point after passing through the damage point. These two distances from the five excitation points are combined... Figure 6 The horizontal coordinates in the matched image are marked with purple circles, which confirms that they do indeed match. Figure 6 Match the x-coordinates of the higher y-coordinates in the image. Figure 6 The fact that there are only 9 purple circles (because two of them overlap in distance) indicates that the theory used in this method is correct.

[0081] S6, the damage point is obtained based on the mesh value.

[0082] In the XY coordinate system established on the plane of the thin-walled metal structure, after meshing the thin-walled metal structure, the sum of distances S from the center point of each mesh to each excitation point and measurement point is calculated. i (x,y), where S i (x,y) represents the sum of the distances from the grid center point (x,y) to the i-th excitation point and the measurement point. In the matching image, S... i (x, y) represents the x-coordinate for searching the corresponding... The vertical axis value. The S-axis value for each grid center point. i Multiplying (x, y) gives the grid value P(x, y) for each grid cell:

[0083]

[0084] Where L is the number of excitation points, S i (x,y) represents the sum of the distances from the grid center point (x,y) to the i-th excitation point and the measurement point.

[0085] Based on the grid values, the two grids with the largest values ​​are directly obtained. One grid is the measurement point, and the other is the damage point. Since the location of the measurement point is known, the location of the damage point is thus determined. To display the damage point location more intuitively, different colors are typically used to visually represent the measurement point and the damage point based on their grid values.

[0086] In this embodiment, the aluminum plate is 200mm*200mm in size. To ensure that the aluminum plate is within the grid, a grid of 250mm*250mm is established in the XY coordinate system, with each grid cell measuring 1mm*1mm. Therefore, a 250*250 grid is established. The size of each grid cell is determined based on the actual situation, which determines the subsequent accuracy. The smaller the grid cell, the higher the accuracy, but the computational workload also increases accordingly. The sum of the distances from the center point of each grid cell to each excitation point and measurement point is calculated. In this embodiment, there are 5 excitation points and 1 measurement point, so each grid cell can obtain 5 values. These 5 values ​​are used as the abscissa to search for the corresponding values ​​in the matching image. The vertical coordinate value is used to obtain the grid value P(x,y) for each grid according to formula (6). Different colors are used to display the grids according to the grid values, resulting in the following: Figure 7 The final damage location shown, based on the XY coordinates, indicates that the highlighted area coordinates are (50mm, 50mm) and (150mm, 150mm). Since the measuring point coordinates are (50mm, 50mm), the damage point coordinates are (150mm, 150mm). It can be seen that... Figure 2 The coordinates of the measuring points and the damage correspond, proving the effectiveness of this method.

[0087] It should be noted that, theoretically, if S is used for each grid... i Adding (x, y) also yields the measurement point and damage point. The resulting mesh will be displayed as multiple ellipses centered on the measurement point and excitation point, such as... Figure 8 As shown, the coordinates of multiple superimposed ellipses represent the damage location, but according to... Figure 8 As can be seen, the measurement point and the damage point are not obvious when the addition method is used. Therefore, the present invention ultimately adopts the multiplication method to amplify the difference between the grid value of the damage location and other locations.

[0088] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation, characterized in that: It includes the following steps: S1, Build a damage detection platform for thin-walled metal structures and obtain the time-domain received signal. ; L+1 PZTs are attached to a thin-walled metal structure, with one PZT serving as a measurement point and the other L PZTs serving as excitation points, where L is a positive integer greater than 1. A signal generator produces the original damage detection signal, which is amplified and input to the excitation points as an excitation signal to generate guided waves in the thin-walled structure. An oscilloscope acquires the signal through the measurement point to obtain the time-domain received signal. ; S2, receiving signals in the time domain Perform sparse sampling; For time-domain received signals Perform Fourier transform to solve for the corresponding frequency domain received signal Sparse measurement signals are obtained by random sampling. ; S3, constructing a dictionary matrix using dispersion curves; Construct a dictionary matrix using the dispersion curve. The dictionary matrix A is: （3）； Where M and N represent the number of rows and columns, respectively, and The element in the m-th row and n-th column of the dictionary matrix A is , , It is the imaginary unit. This represents the nth element in the guided wave propagation distance matrix. This represents the m-th wavenumber of the A0 mode of the guided wave signal; S4, Construct the compressed sensing equation, which is: （4）； in, For frequency domain received signals The sparse measurement signal obtained by random sampling, where A is the dictionary matrix. To reconstruct the signal, For the transpose of the reconstructed signal; S5, obtain the reconstructed signal Create matching images; Based on the compressed sensing equation, a convex optimization linear programming problem is used to obtain the reconstructed signal. ; Reconstruct the signal and guided wave propagation distance matrix Match elements at the same position, using r as the x-coordinate. Using the vertical axis as the coordinate, a matching image of the guided wave propagation distance and the reconstructed signal is obtained; S6, the damage points are obtained based on the mesh values; In the XY coordinate system established on the plane of the thin-walled metal structure, after meshing the thin-walled metal structure, the sum of the distances from the center point of each mesh to each excitation point and measurement point is calculated. In the matching image Search for the corresponding x-axis The vertical axis value represents the center point of each grid. Multiply to get the grid value for each grid cell. : ； in, The number of incentive points, Represents the center point of the grid The sum of the distances to the i-th excitation point and the measurement point; Based on the grid values, the two grids with the largest grid values ​​are obtained. One grid is the measurement point, and the other is the damage point. Since the location of the measurement point is known, the location of the damage point is obtained.

2. The reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation according to claim 1, characterized in that: The thin-walled metal structure in S1 is an aluminum plate.

3. The reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation according to claim 1, characterized in that: The number of excitation points in S1 is L=5.

4. The reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation according to claim 1, characterized in that: The time-domain received signal in S2 Perform sparse sampling; Specifically: (1) For time-domain received signals Perform Fourier transform to solve for the corresponding frequency domain received signal : （1）； Where ω is frequency, t is time, and i is the imaginary unit; (2) For frequency domain received signals Sparse measurement signals are obtained by random sampling. : { } （2）； （3）； in, Receiving signals in the frequency domain frequency The signal obtained by random sampling at that location. It is the first Each sampling frequency point 1,2,…,M; M is the total number of samples.

5. The reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation according to claim 1, characterized in that: In S3, the guided wave propagation distance matrix In a 1*N matrix, the elements of the guided wave propagation distance matrix are uniformly distributed from smallest to largest, with an interval of 1 / N between each element value. The values ​​are taken starting from non-zero, and the maximum value must exceed the maximum guided wave distance from the excitation point to the damage point and then to the measurement point.

6. The reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation according to claim 1, characterized in that: The reconstructed signal is obtained using a convex optimization linear programming problem in S5, specifically: This convex optimization linear programming problem is described as follows: （5）； in, express Norm, express Norm, and They are respectively for Norm and The first and second weighting parameters are used to adjust the norm weights to reconstruct the signal. It is a one-dimensional matrix of size 1*N.

7. The reference-free damage detection method for thin-walled metal structures based on synchronous guided wave excitation according to claim 1, characterized in that: S6 also includes: Each grid is displayed using a different color based on its grid value, and the location of the measuring point and damage point is intuitively displayed based on the grid image.

Images