Reliability assessment method for damage mechanism of single-lap adhesively bonded composite joints based on acoustic emission

Abstract

The present invention provides a damage clustering reliability assessment method for single-lap adhesive joints of composite materials based on acoustic emission, comprising the following steps: 1. performing a tensile test to obtain acoustic emission signals; 2. proposing a noise separation method combining variational mode decomposition (VMD) and fast independent component analysis (FastICA) to remove environmental noise and invalid components; 3. proposing a statistically based credibility assessment method based on the characteristic parameters of the acoustic emission signal in the time and frequency domains, i.e., using the Pearson correlation coefficient to quantify neighborhood changes before and after signal dimensionality reduction, and evaluating the credibility of the event based on a credibility threshold; 4. identifying three types of damage, namely matrix cracking, adhesive debonding, and fiber breakage, based on a clustering algorithm, and analyzing their development mechanisms. The present invention innovatively combines VMD and FastICA for noise separation and proposes a cluster credibility assessment index, effectively improving the accuracy and reliability of acoustic emission data analysis, and providing technical support for damage assessment and health monitoring of single-lap adhesive joints of composite materials and other complex structures.

Description

Technical Field

[0001] The present invention relates to the technical field of composite material health monitoring, and in particular to a reliability assessment method for damage mechanism of composite material single-lap adhesive joints based on acoustic emission. Background Art

[0002] Lightweight design is of great significance to improving energy efficiency and achieving energy conservation and emission reduction. High-performance composite materials (such as carbon fiber reinforced polymers) are ideal choices for achieving this goal because of their advantages such as high strength, high stiffness, corrosion resistance, long fatigue life and light weight. They are currently widely used in aerospace, shipbuilding and automotive fields. Bonded structures are used in many fields because of their light weight and the absence of destructive processes such as drilling. However, bonded joints are prone to defects, which may lead to catastrophic failures. Therefore, it is crucial to use structural health monitoring technology to understand the damage mechanism and ensure structural reliability.

[0003] Acoustic emission monitoring technology is a highly sensitive, real-time nondestructive testing method that offers significant advantages in characterizing the damage behavior of composite materials and the progression of damage in bonded structures. However, despite its many advantages, its practical application still faces challenges in signal analysis reliability, specifically:

[0004] (1) Noise interference increases the uncertainty of acoustic emission signal analysis. Acoustic emission signals are often interfered with by environmental sources such as testing machines, ground vibrations, and other external noises. Techniques such as fast Fourier transform, wavelet decomposition, wavelet packet decomposition, modal decomposition, and local mean decomposition have achieved some success in mitigating random or non-periodic noise, but their effectiveness decreases significantly when the noise frequency overlaps or is close to the target signal frequency.

[0005] (2) In the unsupervised classification process, there is still a lack of methods to evaluate the credibility of acoustic emission signals. Existing algorithms usually evaluate the overall effect of clustering by "high similarity within clusters and low similarity between clusters". Dimensionality reduction methods such as t-SNE can retain the similarity between data points, which not only helps to explain cluster analysis, but also can intuitively present the clustering results of other algorithms. However, many studies rely on exhaustive search and empirical selection of hyperparameters (such as perplexity) when using t-SNE, and lack quantitative indicators to evaluate the neighborhood relationship of the data, which in turn affects the reliability of evaluating the clustering results.

[0006] Based on the above research gaps, it is necessary to propose a technology to separate effective signals from environmental noise and introduce a confidence index to quantify the credibility of damage mechanism classification. Summary of the Invention

[0007] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a reliability assessment method for the damage mechanism of single-lap adhesive joints of composite materials based on acoustic emission. The useful signal is extracted from the mixed signal by using variational mode decomposition (VMD) and fast independent component analysis (FastICA). The credibility of the acoustic emission signal is evaluated by an evaluation method based on t-SNE. Finally, the key features are extracted and the Gaussian mixture model (GMM) is used to cluster the acoustic emission signals. This method can reliably and accurately study the damage mechanism of single-lap joints of composite materials.

[0008] To achieve the above objectives, the present invention adopts the following technical solutions:

[0009] A reliability assessment method for damage mechanism of composite single-lap adhesive joints based on acoustic emission comprises the following steps:

[0010] Step 1: Conduct tensile testing and collect acoustic emission signals

[0011] A mechanical loading experiment was conducted on the single-lap adhesive joint of composite materials to collect the acoustic emission signals generated during the mechanical loading process.

[0012] Step 2: Noise separation of acoustic emission signals

[0013] The acoustic emission signal is separated using variational mode decomposition (VMD) to obtain multiple intrinsic mode functions (IMFs). To address the invalid or pseudo modes that may be generated during the VMD separation process, the IMFs are screened using Pearson correlation analysis. A threshold is set to select IMFs with high correlation with the original signal. The original signal and the screened IMFs are combined into a multidimensional signal, which serves as the input for fast independent component analysis (FastICA). Since the order of the separation results is uncertain, the duration of the acoustic emission is used as the basis for distinguishing between ambient noise and valid signals. All signals are subjected to noise separation processing.

[0014] Step 3: Credibility assessment of acoustic emission signals

[0015] Short-time Fourier transform (STFT) is used to extract the time-frequency domain characteristic indicators of the acoustic emission signal: peak frequency and peak amplitude. In addition, count, energy, duration, and amplitude are selected as input features of the t-SNE dimensionality reduction algorithm. The distance between each acoustic emission event and its neighboring acoustic emission events in the pre-embedded space before t-SNE dimensionality reduction and the embedded space after t-SNE dimensionality reduction is calculated, and these events are sorted from near to far. The credibility of a single acoustic emission event is defined as the Pearson correlation coefficient between its distance vectors in the two spaces. Among them, the neighborhood of the acoustic emission event is defined Multiply the total number of acoustic emission events by the "similarity percentage"; calculate the Pearson correlation coefficient between the acoustic emission event and the randomly sorted acoustic emission event neighborhood to obtain the zero distribution of the credibility score; based on the zero distribution, define two thresholds of cluster credibility: (1) credible threshold: the 95th percentile of the zero distribution; (2) suspicious threshold: the 5th percentile of the zero distribution; if the credibility score of the acoustic emission event is higher than the credible threshold, it is marked as a credible event; if the credibility score of the acoustic emission event is lower than the suspicious threshold, it is marked as a suspicious event; events with scores between the credible threshold and the suspicious threshold are not marked;

[0016] Step 4: Reliable clustering and mechanism analysis of acoustic emission signals

[0017] Based on the peak frequency and peak amplitude of the time-frequency domain characteristic indicators of the acoustic emission signal, the Gaussian mixture model (GMM) was used to cluster the acoustic emission signals. The damage label of each acoustic emission event was obtained, and the average clustering probability of each acoustic emission event being judged as a specific damage was calculated. Based on the above clustering results, the change curve of the normalized cumulative count of different bonding lengths of the specific damage with the normalized displacement was calculated to explore the relationship between typical acoustic emission signals and damage evolution.

[0018] In the step 1, a tensile test based on displacement control is performed on the single-lap adhesive joint of the composite material, and the acoustic emission signal is recorded by an acoustic emission data acquisition system.

[0019] In step 2, noise separation of acoustic emission signals is performed by variational mode decomposition (VMD). By establishing a constrained variational model, the complex signal is decomposed into multiple intrinsic mode functions (IMFs) and the optimal frequency center and bandwidth of each component are iteratively determined. The specific steps are as follows:

[0020] 1) Perform a Hilbert transform on each intrinsic mode function (IMF) to obtain an analytical signal, which is then down-converted to baseband based on the estimated center frequency. The modulation signal bandwidth is then calculated using the derivative method. The problem of minimizing the sum of the modal bandwidths and reconstructing the input signal is formulated as a constrained variational problem:

[0021]

[0022] Where: ωk is the center frequency of the kth mode; u k (t) is the kth mode function; δ(t) is the unit impulse function; * represents the convolution operator; Indicates u k (t) The analytical signal is modulated to baseband; Represents the partial derivative operation, which is used to estimate the smoothness of the bandwidth metric; j represents the imaginary unit f(t) is the target signal;

[0023] 2) Introducing the Lagrangian function L(*) and the penalty factor α, the constrained variational problem in formula (1) is transformed into an unconstrained variational problem, resulting in the following expression:

[0024]

[0025] Where: t (t) is the Lagrange operator term with respect to time t;

[0026] 3) Using alternating multiplication ADMM and λ n+1 The iterative update is performed to find the saddle point of the Lagrangian function, which corresponds to the optimal solution. During the iteration, the update of the modal components follows formula (3), and the update of the center frequency follows formula (4). Based on the updated modal components and the corresponding center frequencies, the update of the Lagrangian operator is performed according to formula (5):

[0027]

[0028] Where: Indicates u k (t) nth iteration value in Fourier domain; represents the Fourier transform of the target signal f(t), Represents ω k The nth iteration value; τ is the Lagrange operator term step size;

[0029] 4) The above iterative process terminates when the convergence condition is met, that is,

[0030]

[0031] Where: ε is the convergence parameter; when the solution reaches the discrimination accuracy ε, the iteration stops and K modal components are output; the decomposition quality of variational mode decomposition (VMD) depends particularly on the choice of mode number;

[0032] Fast Independent Component Analysis (FastICA) is one of the improved ICA algorithms. This algorithm uses the Newton-Raphson method and takes the maximization of negative entropy as the objective function. Its main idea is to separate multiple mixed signals into independent signal sources. The observed signal is defined as The source signal is defined as The mixing relationship is defined as x(t) = A·s(t), where A is the mixing coefficient matrix. The purpose of FastICA is to estimate the separation matrix W, which is the inverse matrix of the mixing matrix A. This matrix is ​​used to calculate the independent signals: y (t) = W·x(t) = W·A·s(t); is an estimate of the source signal s(t), and all signals in y are independent of each other;

[0033] By combining the above-mentioned variational mode decomposition VMD and fast independent component analysis FastICA, the effective signal can be efficiently separated from the mixed signal.

[0034] In step three, the optimal dimensionality reduction parameters of the acoustic emission data are determined by iteratively searching for perplexity values ​​ranging from 5 to 60, and the proportion of suspicious events in the signal space is calculated based on this, thereby ensuring consistency and fairness in evaluating event credibility using the dimensionality reduction method on specimens with different bonding lengths.

[0035] In step 4, the peak frequency and peak amplitude characteristic parameters are selected, and the Gaussian mixture model (GMM) is used to cluster the acoustic emission signals of different damages to obtain three types of damage: matrix cracking, adhesive debonding, and fiber breakage. The average clustering probability of the acoustic emission event being judged as a specific damage type is then calculated.

[0036] In step 4, a grid search method is used to optimize the number of clusters and the covariance type in the Gaussian mixture model (GMM) using the Bayesian Information Criterion (BIC) to find the optimal clustering parameters. Clustering effectiveness is also evaluated using the Silhouette Coefficient (Silhouette Coefficient) and the Davies-Bouldin Index (DBI). The Silhouette Coefficient measures the internal consistency of clusters, while the Davies-Bouldin Index quantifies the degree of separation and closeness between clusters by comparing the intra-cluster distance to the inter-cluster distance. Combining these three metrics ensures the accuracy of clustering results in a more comprehensive and objective manner.

[0037] In step 4, by analyzing the acoustic emission signals collected from specimens with different bonding lengths, the regularity and consistency of the change of the normalized cumulative counts of different damages with the normalized displacement are determined; based on the above reliability analysis, the development process of different damages in a single specimen is further studied to reveal the occurrence mechanism of adhesive layer damage.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. High accuracy: Through noise separation technology, useful signals are effectively extracted from mixed signals, so that the analysis is based on pure signals without noise, thereby reducing the uncertainty of cluster analysis to a certain extent and improving the reliability of damage mechanism research;

[0040] 2. High reliability: Based on the credibility assessment method, it can provide a confidence reference for the clustering results;

[0041] 3. Real-time monitoring: Acoustic emission monitoring technology is used to obtain the health status of composite structures in real time, providing a reliable reference for exploring the mechanism of damage processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Flow chart of the method of the present invention;

[0043] Figure 2 This is a flow chart of the noise separation method in the embodiment;

[0044] Figure 3 The noise separation result in the embodiment;

[0045] Figure 4 The method for evaluating the credibility of acoustic emission events and data sets in the embodiment;

[0046] Figure 5 (a) is the t-SNE visualization result of the acoustic emission signal collected at the optimal perplexity for the 30 mm bonding length in the embodiment;

[0047] Figure 5 (b) is the t-SNE visualization result of the acoustic emission signal collected at the optimal perplexity for the 40 mm bonding length in the embodiment;

[0048] Figure 5 (c) is the t-SNE visualization result of the acoustic emission signal collected at the optimal perplexity for the 50 mm bonding length in the embodiment;

[0049] Figure 6 (a) is a heat map of the credibility score of the original acoustic emission signal collected at a bonding length of 50 mm in the embodiment;

[0050] Figure 6(b) is a heat map of the credibility score of the effective acoustic emission signal collected after noise separation at a bonding length of 50 mm in the embodiment;

[0051] Figure 7 (a) is the clustering result of the acoustic emission signal collected at a bonding length of 30 mm in the embodiment based on the noise reduction data.

[0052] Figure 7 (b) is the clustering result of the acoustic emission signal collected at a bonding length of 40 mm in the embodiment based on the noise reduction data.

[0053] Figure 7 (c) is the clustering result of the acoustic emission signal collected at a bonding length of 50 mm in the embodiment based on the noise reduction data.

[0054] Figure 8 (a) is the consistency curve of normalized displacement and normalized cumulative count of acoustic emission signal of substrate cracking at different bonding lengths in the embodiment.

[0055] Figure 8 (b) is the consistency curve of the normalized displacement and normalized cumulative count of the acoustic emission signal of the adhesive layer debonding at different bonding lengths in the embodiment.

[0056] Figure 8 (c) is the consistency curve of the normalized displacement and normalized cumulative count of the acoustic emission signal of fiber breakage at different bonding lengths in the embodiment. DETAILED DESCRIPTION

[0057] To better understand the technical solutions of the present invention, the specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0058] The entire testing system consists of a tensile test apparatus, an acoustic emission sensor, a data acquisition system, a computer, and a composite single-lap bonded joint. The acoustic emission sensor is attached to the surface of the composite single-lap bonded joint by adhesion or clamping. During mechanical loading, the data acquisition system acquires the acoustic emission signal.

[0059] like Figure 1 As shown, a reliability assessment method for damage mechanism of composite single-lap adhesive joints based on acoustic emission includes the following steps:

[0060] Step 1: Conduct tensile testing and collect acoustic emission signals

[0061] A mechanical loading experiment was conducted on composite adhesive joints to obtain force-load displacement curves and acoustic emission signals during the loading process. The acquisition frequency of the acoustic emission signals was set to 2 MHz, the pre-amplifier gain was set to 40 dB, and the threshold was set to 50 dB.

[0062] Step 2: Noise separation of acoustic emission signals

[0063] The acoustic emission signal is separated using variational mode decomposition (VMD) to obtain multiple intrinsic mode functions (IMFs). In order to solve the invalid or pseudo modes that may be generated during the separation process of variational mode decomposition (VMD), Pearson correlation analysis is used to screen the intrinsic mode functions (IMFs), set a specific threshold, and select IMFs with a high correlation with the original signal. The original signal and the screened IMFs are combined into a multidimensional signal as the input of the fast independent component analysis (FastICA). Since the arrangement order of the FastICA separation results is uncertain, acoustic emission indicators (such as duration) are used to distinguish between environmental noise and damage signals, and all signals are subjected to noise separation processing. The process is as follows: Figure 2 As shown;

[0064] The parameters of variational mode decomposition (VMD) are set to alpha = 2000, init = 1, tol = 1e-7, and K = 4. The parameters of fast independent component analysis (FastICA) are set to n_component = 2, default = unit-variance, and neg-entropy: logcosh. Figure 3 As shown, from Figure 3 It can be seen that after using the VMD-FastICA separation method, Figure 3 The original signal of (a) is decomposed into two components, Figure 3 The signal with a peak frequency of 316.41kHz in (b) has a short duration and a significant amplitude, and is identified as an effective damage signal. Figure 3 The signal in (c) has a longer duration, indicating that it belongs to the ambient noise. This shows that the proposed method can effectively separate the useful acoustic emission signal from the ambient noise.

[0065] Step 3: Credibility assessment of acoustic emission signals

[0066] Credibility assessment process Figure 4As shown. First, the short-time Fourier transform (STFT) is used to extract the time-frequency domain feature indicators of the acoustic emission signal, and the amplitude, count, energy and duration are used as the input features of the t-SNE dimensionality reduction. Among them, the parameters of the short-time Fourier transform (STFT) are set to: nperseg=256, noverlap=128, the length of the Kaiser window is 256, and the shape parameter is 5. In the pre-embedding space before t-SNE dimensionality reduction and the embedded space after dimensionality reduction, the distance between each acoustic emission event and other events in its neighborhood is calculated, and these events are sorted from near to far according to distance. The confidence of a single acoustic emission event is defined as the Pearson correlation coefficient between its two distance vectors. Among them, the neighborhood of an acoustic emission event is defined as the total number of acoustic emission events multiplied by the "similarity percentage". By calculating the Pearson correlation coefficient between the acoustic emission event and the randomly sorted acoustic emission event neighborhood, the zero distribution of the credibility score can be obtained. Based on the null distribution, two confidence thresholds are defined: (1) credible threshold: the 95th percentile of the null distribution; (2) suspicious threshold: the 5th percentile of the null distribution. If the confidence score is higher than the credible threshold, it is marked as a credible event; if the confidence score is lower than the suspicious threshold, it is marked as a suspicious event; events with scores between the credible threshold and the suspicious threshold are recorded as intermediate events. The optimal perplexity for dimensionality reduction is determined by iteratively searching within the perplexity range of 5 to 60 and combining it with the proportion of suspicious events in the acoustic emission signal space. Table 1 lists the dimensionality reduction of acoustic emission data of three specimens with different bonding lengths at different perplexity values, where PDE represents the proportion of suspicious events and PTE represents the proportion of credible events. For specimens with bonding lengths of 30 mm and 40 mm, the dimensionality reduction effect is best when the perplexity is 60; while for specimens with bonding lengths of 50 mm, the dimensionality reduction effect is best when the perplexity is 5.

[0067] Table 1 Basis for selecting hyperparameters for acoustic emission data of specimens with different bonding lengths

[0068]

[0069] The credibility evaluation results and thermal images of acoustic emission signals collected on specimens with three different bonding lengths are shown in the figure. Figure 5 (a), (b), (c) and Figure 6 As shown in (a) and (b). Figure 5 The data set clustering characteristics under the optimal hyperparameter dimensionality reduction condition are shown. Different damage types have clear boundaries and are usually located at the boundary between isolated data clusters or two clusters (such as black points). In contrast, credible events show a high degree of consistency with the data feature patterns in the neighborhood and are usually distributed in the center of the cluster area (such as medium gray points), indicating that their clustering is more credible. Figure 6As shown in the figure, after using VMD-FastICA noise separation, the number of light gray points increases, indicating a general improvement in the data confidence. Specifically, after the VMD-FastICA noise separation algorithm, the confidence of events with lower peak amplitudes increases. At the same time, in areas with higher peak amplitudes, the number of low-confidence events decreases due to the removal of noise signals.

[0070] Step 4: Reliable clustering and mechanism analysis of acoustic emission signals

[0071] Based on the characteristic index of acoustic emission signal characteristics, Gaussian mixture model GMM is used to cluster acoustic emission signals. The results based on Bayesian information criterion BIC, silhouette coefficient and Davies-Bouldin index show that when the number of clusters is 4, GMM can achieve the best clustering. The distribution of different damages in peak frequency, peak amplitude and time is shown in Figure 2. Figure 7 As shown in Figures (a), (b), and (c), the acoustic emission signals are divided into four clusters based on peak frequency. AE events with frequencies below 150 kHz correspond to matrix cracking, while those around 300 kHz are associated with fiber breakage. Signals between these two frequencies are considered adhesive debonding. Signals with larger peak amplitudes are classified as "other" signals. These signals often occur when the specimen is about to break and may represent a variety of damage types. Due to the relatively small proportion of these signals, they are not further analyzed in this paper.

[0072] The average probability of acoustic emission events of different damages being clustered into specific damages is shown in Table 2. The average probability of acoustic emission signals being predicted as different damages after noise separation using VMD-FastICA is significantly improved. The curve of normalized cumulative counts of different bonding lengths under specific damages versus normalized displacement is shown in Figure 8 As shown in (a), (b), and (c), it can be seen from the figure that the development process of different damages of specimens with different bonding lengths has a certain consistency.

[0073] Table 2 Average clustering probability of different damages

[0074]

[0075] This invention innovatively proposes a noise separation method and credibility assessment method, which utilizes mature signal processing algorithms and dimensionality reduction algorithms. It is simple and convenient to operate, can adapt to complex and harsh external field conditions, ensures the accuracy of the acoustic emission clustering process, and realizes reliable research on the structural damage mechanism of single-lap adhesive joints of composite materials.

[0076] Final Notes

[0077] The embodiments described above are intended only to illustrate the technical solutions of the present invention and are not intended to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may be modified, or some or all of the technical features therein may be replaced with equivalents; such modifications or replacements do not deviate from the essence of the corresponding technical solutions within the scope of the various embodiments of the present invention.

Claims

1. A reliability assessment method for damage mechanism of composite single-lap adhesive joints based on acoustic emission, characterized by: The following steps are involved: Step 1: Conduct tensile testing and collect acoustic emission signals A mechanical loading experiment was conducted on the single-lap adhesive joint of composite materials to collect the acoustic emission signals generated during the mechanical loading process. Step 2: Noise separation of acoustic emission signals The acoustic emission signal is separated using variational mode decomposition (VMD) to obtain multiple intrinsic mode functions (IMFs). To address the invalid or pseudo modes that may be generated during the VMD separation process, the IMFs are screened using Pearson correlation analysis. A threshold is set to select IMFs with high correlation with the original signal. The original signal and the screened IMFs are combined into a multidimensional signal, which serves as the input for fast independent component analysis (FastICA). Since the order of the separation results is uncertain, the duration of the acoustic emission is used as the basis for distinguishing between ambient noise and valid signals. All signals are subjected to noise separation processing. Step 3: Credibility assessment of acoustic emission signals Short-time Fourier transform (STFT) is used to extract the time-frequency domain characteristic indicators of the acoustic emission signal: peak frequency and peak amplitude. In addition, count, energy, duration, and amplitude are selected as input features of the t-SNE dimensionality reduction algorithm. The distance between each acoustic emission event and its neighboring acoustic emission events in the pre-embedded space before t-SNE dimensionality reduction and the embedded space after t-SNE dimensionality reduction is calculated, and these events are sorted from near to far. The credibility of a single acoustic emission event is defined as the Pearson correlation coefficient between its distance vectors in the two spaces. Among them, the neighborhood of the acoustic emission event is defined The total number of acoustic emission events is multiplied by the "similarity percentage"; the Pearson correlation coefficient between the acoustic emission event and the randomly sorted acoustic emission event neighborhood is calculated to obtain the zero distribution of the credibility score; based on the zero distribution, two thresholds of cluster credibility are defined: (1) credible threshold: the 95th percentile of the zero distribution; (2) suspicious threshold: the 5th percentile of the zero distribution; if the credibility score of the acoustic emission event is higher than the credible threshold, it is marked as a credible event; if the credibility score of the acoustic emission event is lower than the suspicious threshold, it is marked as a suspicious event; events with scores between the credible threshold and the suspicious threshold are not marked; Step 4: Reliable clustering and mechanism analysis of acoustic emission signals Based on the peak frequency and peak amplitude of the time-frequency domain characteristic indicators of the acoustic emission signal, the Gaussian mixture model (GMM) was used to cluster the acoustic emission signals. The damage label of each acoustic emission event was obtained, and the average clustering probability of each acoustic emission event being judged as a specific damage was calculated. Based on the above clustering results, the change curve of the normalized cumulative count of different bonding lengths of the specific damage with the normalized displacement was calculated to explore the relationship between typical acoustic emission signals and damage evolution.

2. The reliability assessment method for damage mechanism of composite single-lap adhesive joints based on acoustic emission according to claim 1 is characterized in that: In the step 1, a tensile test based on displacement control is performed on the single-lap adhesive joint of the composite material, and the acoustic emission signal is recorded by an acoustic emission data acquisition system.

3. The reliability assessment method for damage mechanism of composite single-lap adhesive joints based on acoustic emission according to claim 1 is characterized by: In step 2, noise separation of acoustic emission signals is performed by variational mode decomposition (VMD). By establishing a constrained variational model, the complex signal is decomposed into multiple intrinsic mode functions (IMFs) and the optimal frequency center and bandwidth of each component are iteratively determined. The specific steps are as follows: 1) Perform a Hilbert transform on each intrinsic mode function (IMF) to obtain an analytical signal, which is then down-converted to baseband based on the estimated center frequency. The modulation signal bandwidth is then calculated using the derivative method. The problem of minimizing the sum of the modal bandwidths and reconstructing the input signal is formulated as a constrained variational problem: Where: ω k is the center frequency of the kth mode; u k (t) is the kth mode function; δ(t) is the unit impulse function; * represents the convolution operator; Indicates u k (t) The analytical signal is modulated to baseband; Represents the partial derivative operation, which is used to estimate the smoothness of the bandwidth metric; j represents the imaginary unit f(t) is the target signal; 2) Introducing the Lagrangian function L(*) and the penalty factor α, the constrained variational problem in formula (1) is transformed into an unconstrained variational problem, resulting in the following expression: Where: t (t) is the Lagrange operator term with respect to time t; 3) Using alternating multiplication ADMM and λ n+1 The iterative update is performed to find the saddle point of the Lagrangian function, which corresponds to the optimal solution. During the iteration, the update of the modal components follows formula (3), and the update of the center frequency follows formula (4). Based on the updated modal components and the corresponding center frequencies, the update of the Lagrangian operator is performed according to formula (5): Where: Indicates u k (t) nth iteration value in Fourier domain; represents the Fourier transform of the target signal f(t), Represents ω k The nth iteration value; τ is the Lagrange operator term step size; 4) The above iterative process terminates when the convergence condition is met, that is, Where: ε is the convergence parameter; when the solution reaches the discrimination accuracy ε, the iteration stops and K modal components are output; the decomposition quality of variational mode decomposition (VMD) is related to the choice of mode number; Fast Independent Component Analysis (FastICA) is one of the improved ICA algorithms. This algorithm uses the Newton-Raphson method and takes the maximization of negative entropy as the objective function to separate multiple mixed signals into independent signal sources. The observed signal is defined as The source signal is defined as The mixing relationship is defined as x(t) = A·s(t), where A is the mixing coefficient matrix. The purpose of FastICA is to estimate the separation matrix W, which is the inverse matrix of the mixing matrix A. This matrix is ​​used to calculate the independent signals: y(t) = W·x(t) = W·A·s(t). is an estimate of the source signal s(t), and all signals in y are independent of each other; By combining the above-mentioned variational mode decomposition VMD and fast independent component analysis FastICA, the effective signal can be efficiently separated from the mixed signal.

4. The reliability assessment method for damage mechanism of composite single-lap adhesive joints based on acoustic emission according to claim 1 is characterized by: In step 3, the proportion of suspicious events in the acoustic emission signal space is calculated by iteratively searching different perplexity values ​​ranging from 5 to 60 to find the optimal hyperparameters for dimensionality reduction of acoustic emission data.

5. The reliability assessment method for damage mechanism of composite single-lap adhesive joints based on acoustic emission according to claim 1 is characterized by: In step 4, the peak frequency and peak amplitude are selected as characteristic parameters, and the Gaussian mixture model (GMM) is used to cluster the acoustic emission signals to obtain three types of damage: matrix cracking, adhesive debonding, and fiber breakage. The average clustering probability of the acoustic emission event being judged as a specific damage type is then calculated.

6. The reliability assessment method for damage mechanism of composite single-lap adhesive joints based on acoustic emission according to claim 1 is characterized by: In step 4, based on the Bayesian Information Criterion (BIC), the grid search method is used to optimize the number of clusters and the covariance type in the Gaussian mixture model (GMM) to find the optimal clustering parameters. At the same time, the clustering effect is evaluated by the Silhouette Coefficient and the Davies-Bouldin Index to determine the optimal number of clusters.

7. The reliability assessment method for damage mechanism of composite single-lap adhesive joints based on acoustic emission according to claim 1 is characterized by: In step 4, by analyzing the acoustic emission signals collected from specimens with different bonding lengths, the regularity and consistency of the change of the normalized cumulative counts of different damages with the normalized displacement are determined; based on the above reliability analysis, the development process of different damages in a single specimen is further studied to reveal the occurrence mechanism of adhesive layer damage.

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