Acoustic emission sensor and acoustic emission energy signal separation method

Abstract

The present invention belongs to the technical field of acoustic emission sensors, and specifically relates to an acoustic emission sensor and an acoustic emission energy signal separation method. A time domain signal dataset is obtained by emitting multiple single-frequency excitation signals and receiving echoes. Time-frequency analysis, feature extraction, principal component analysis, cluster analysis, and input into a dispersion model for verification are then performed to separate the signal into a reflection signal, a transmission signal, and a noise signal. Finally, the transmission signal is reconstructed and the separated signal is evaluated to obtain a high-quality separated transmission signal. By analyzing the diffusion characteristics of sound waves of different frequencies in a material, the present invention effectively distinguishes between reflection signals, transmission signals, and ambient noise, thereby accurately identifying the energy signal that actually passes through an object. The high-quality separated transmission signal is used for air-coupled ultrasonic detection, effectively reducing the interference of reflection and noise signals and improving detection accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of acoustic emission sensors, and in particular relates to an acoustic emission sensor and an acoustic emission energy signal separation method. Background Art

[0002] Acoustic emission sensors are widely used in the field of nondestructive testing of materials. This technology primarily utilizes the propagation characteristics of sound waves to analyze the acoustic signal characteristics within a material to assess its structural integrity, defect distribution, and mechanical properties. Acoustic emission sensors assess internal damage by monitoring transient elastic waves generated by a material under stress, temperature, or other external stimuli. Typical acoustic emission signals are closely correlated with internal damage behaviors such as microcracks, fiber debonding, and pore expansion.

[0003] Acoustic emission sensors based on air-coupled ultrasound technology offer the advantages of high sensitivity and high resolution, enabling effective, contactless, and non-destructive testing. However, in practical applications, the echo signal from the acoustic emission sensor contains a mixture of reflected and transmitted signals. Furthermore, complex structures or multi-layered materials can cause signal interference, as can environmental noise. This reduces the accuracy and reliability of test results, limiting the application of non-destructive testing technology. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the defect in the prior art that the air-coupled ultrasonic technology is affected by the reflected signal and noise interference and affects the signal quality, thereby providing an acoustic emission sensor and an acoustic emission energy signal separation method.

[0005] A method for separating acoustic emission energy signals comprises the following steps:

[0006] Step S1: generating a plurality of single-frequency excitation signals, wherein the frequencies of the plurality of single-frequency excitation signals are logarithmically spaced;

[0007] Step S2: transmitting a plurality of the single-frequency excitation signals through a transducer, and receiving echoes to obtain a time domain signal data set;

[0008] Step S3: performing time-frequency analysis on the time domain signal data set to obtain a time-frequency spectrum set;

[0009] Step S4: extracting features from the time-frequency spectrum set to obtain a feature matrix;

[0010] Step S5: performing principal component analysis on the feature matrix to obtain a dimension-reduced feature matrix;

[0011] Step S6: performing cluster analysis on the dimension-reduced feature matrix to obtain a clustering result, wherein the clustering result includes a category label for each data point, and the category label includes reflection, transmission, and noise;

[0012] Step S7: constructing a dispersion model based on the clustering results and material physical parameters, incorporating the dispersion model into the dispersion model, and obtaining a verified clustering result;

[0013] Step S8: inputting the verification clustering result into the dispersion model, and based on the cluster center feature identification signal of the verification clustering result, marking the signal in the verification clustering result as a reflection signal, a transmission signal and a noise signal;

[0014] Step S9: superimposing and processing the transmission signals to obtain a reconstructed transmission signal;

[0015] Step S10: Calculating a separation effect index based on the single-frequency excitation signal and the reconstructed transmission signal.

[0016] Furthermore, in step S1, the single-frequency excitation signal is a sinusoidal wave signal.

[0017] Furthermore, in step S3, the time-frequency analysis is continuous wavelet transform:

[0018] W(a,b)=(1 / sqrt(|a|))∫x(t)ψ*((tb) / a)dt;

[0019] Among them, ψ(t) represents the mother wavelet, a represents the scale parameter, and b represents the translation parameter.

[0020] Furthermore, in step S3, the time-frequency analysis is discrete wavelet transform:

[0021] W[m,n]=(1 / sqrt(a0 m ))∑x[k]ψ*((k-nb0 a0^m) / a0 m );

[0022] Among them, m and n are integers, a0>1 represents a fixed expansion step size, and b0>0 represents a position parameter.

[0023] Furthermore, in step S4, each row of the feature matrix represents a signal of a frequency in the time-frequency spectrum set, and each column represents a feature; the features include peak amplitude, root mean square value, form factor, kurtosis, spectral entropy, energy density ratio, center frequency, frequency diffusion width, diffusion coefficient, and group velocity dispersion.

[0024] Furthermore, step S5 includes the following steps:

[0025] Step S5.1: Obtain the feature matrix X, the size of which is m×n, where m is the number of samples and n is the number of features;

[0026] Step S5.2: Calculate the central characteristic matrix of the characteristic matrix:

[0027] X centered =X–μ;

[0028] Wherein, μ is the mean vector of the feature matrix;

[0029] Step S5.3: Calculate the covariance matrix C of the centered feature matrix:

[0030] C=(1 / m)*X centered T *X centered ;

[0031] Step S5.4: Perform eigenvalue decomposition on the covariance matrix:

[0032] C=VΛV^T;

[0033] Where V represents the eigenvector matrix, and Λ represents the diagonal matrix of eigenvalues;

[0034] Step S5.5: Arrange the eigenvectors of the eigenvector matrix in descending order, and select the first k eigenvectors to form the projection matrix W; the k is such that the cumulative explained variance ratio reaches a preset threshold:

[0035] explained_variance_ratio = λ i / ∑λ i ;

[0036] where λ i represents the eigenvalue;

[0037] Step S5.6: Project the centered feature matrix to the new subspace to obtain the reduced-dimensional feature matrix:

[0038] Y=X centered *W.

[0039] Furthermore, in step S6, the cluster analysis includes the following steps:

[0040] Step S6.1: define the neighborhood radius and the minimum number of points in the dense area;

[0041] Step S6.2: For each point in the reduced dimension feature matrix, if the number of points in its neighborhood is greater than or equal to the minimum number of points in the dense area, it is defined as a core point;

[0042] Step S6.3: For a core point, add all points in its neighborhood to a cluster, and add all points in the neighborhood of the cluster to the cluster, and repeat until no new points are added to the cluster;

[0043] Step S6.4: Define the points that do not belong to any cluster as noise points, and output a clustering result, which includes all the clusters and the noise points.

[0044] Furthermore, step S7 includes the following steps:

[0045] Step S7.1: Based on the clustering results, the transmission signal dispersion analysis calculates the frequency-diffusion relationship, the reflection signal dispersion analysis calculates the frequency dispersion, and calculates the comprehensive dispersion function;

[0046] Step S7.1.1: Extract frequency diffusion features:

[0047] σf=sqrt(Σ(f-fc) 2 |X(f)| 2 / Σ|X(f)| 2 );

[0048] Determine the frequency dependence of α(ω) and v(ω)

[0049] Establishing the frequency-diffusion relationship:

[0050] D trans (ω)=f(σf,α(ω),v(ω));

[0051] Step S7.1.2: Extract frequency dispersion characteristics and calculate v(ω) and R(ω)

[0052] Analyze dispersion patterns and study frequency dependence

[0053] Establish the frequency-dispersion relationship:

[0054] D ref (ω)=g(v(ω),R(ω));

[0055] Step S7.1.3: Calculate the integrated dispersion function:

[0056] D(ω)=Σ(wi*Di(ω)) / Σwi;

[0057] Where D(ω) represents the comprehensive dispersion function, Di(ω) represents the dispersion function of each group, and wi represents the weight based on signal quality and physical rationality;

[0058] Step S7.2: Calculate the diffusion function of each cluster group, check consistency and update the dispersion classification, and calculate the diffusion difference of each cluster group:

[0059] Step S7.2.1: Extract frequency diffusion features for each cluster group and calculate σf and Dc;

[0060] Analyze diffusion laws and study changes in α(ω) and v(ω);

[0061] Compute the diffusion function:

[0062] D(ω)=(f max -f min ) / f center ;

[0063] Step S7.2.2: Calculate the dispersion characteristic variance:

[0064] Var(D)=Σ(Di-D mean ) 2 / n;

[0065] Identify abnormal diffusion points:

[0066] |Di-D mean |>3σ;

[0067] Eliminate outliers, reclassify, and update dispersion classification;

[0068] Step S7.2.3: Analyze the dispersion differences between different categories: Calculate the dispersion feature differences;

[0069] Verify the rationality of dispersion physics: check whether it meets the physical laws of the material;

[0070] Calculate the diffusion difference:

[0071] ΔD=|D1(ω)-D2(ω)| / D ref ;

[0072] Step S7.3: According to the updated clustering model, verify based on the theoretical dispersion model and update the dispersion classification:

[0073] Step S7.3.1: Calculate the frequency spread width:

[0074] σf=sqrt(Σ(f-fc) 2 |X(f)| 2 / Σ|X(f)| 2 );

[0075] Calculate the diffusion characteristic deviation:

[0076] δD=|Dm-Dc| / Dc;

[0077] Where Dm represents the measured diffusion function and Dc represents the theoretical diffusion function;

[0078] Calculate frequency dispersion:

[0079] ΔV=|V(f2)-V(f1)| / V(f1);

[0080] Step S7.3.2: For each cluster group, if (σf < 0.2) && (δD < 0.15) && (ΔV < 0.1) is satisfied, the cluster group passes; if not, the model parameters are adjusted;

[0081] Step S7.3.3: Remove points that do not conform to the dispersion law:

[0082] |DD mean |>2σ;

[0083] Re-divide the boundary based on ΔD, calculate the dispersion feature center and update D mea n and σ.

[0084] Furthermore, step S9 includes the following steps:

[0085] Step S9.1: Constructing a signal model based on the transmission signal:

[0086] y=Φx+n;

[0087] Where y represents the measurement signal, Φ represents the measurement matrix, x represents the original signal, and n represents the noise;

[0088] Step S9.2: Compressed sensing problem:

[0089] min||x||1subject to||y-Φx||2≤ε;

[0090] Among them, ||·||1 represents the L1 norm, ||·||2 represents the L2 norm, and ε represents the noise level;

[0091] Step S9.3: Implement the basis pursuit denoising algorithm:

[0092] min(1 / 2)||y-Φx||2 2 +λ||x||1;

[0093] Where λ represents the regularization parameter;

[0094] Step S9.4: Implement the orthogonal matching pursuit algorithm:

[0095] r0=y, x0=0;

[0096] For k = 1 to K:

[0097]

[0098] ii.Λ k =Λ_{k-1}∪{λ k};

[0099] iii.x k=arg min||y-Φ_Λ k x||2iv.r k =y-Φx k Return x k ;

[0100] Among them, φ j Represents the column of Φ, Φ_Λ k Denotes a submatrix of Φ, whose columns are represented by Λ k index.

[0101] Furthermore, the separation effect indicators in step S10 include: signal-to-noise ratio, mean square error, peak signal-to-noise ratio, structural similarity index, correlation coefficient, spectrum consistency, and energy recovery ratio.

[0102] An acoustic emission sensor realizes signal separation through the above-mentioned acoustic emission energy signal separation method.

[0103] Beneficial effects: The present invention discloses a method for separating acoustic emission energy signals. The method obtains a time domain signal data set by emitting multiple single-frequency excitation signals and receiving echoes. The method then performs time-frequency analysis, feature extraction, principal component analysis, cluster analysis, and inputs the dispersion model for verification to separate the signal into a reflection signal, a transmission signal, and a noise signal. Finally, the transmission signal is reconstructed and the separated signal is evaluated to obtain a high-quality separated transmission signal. The present invention effectively distinguishes between reflection signals, transmission signals, and environmental noise by analyzing the diffusion characteristics of sound waves of different frequencies in materials, thereby accurately identifying the energy signal that actually passes through the object. The high-quality separated transmission signal is used for air-coupled ultrasonic detection, effectively reducing the interference of reflection signals and noise signals and improving detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0105] Figure 1 It is a schematic block diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0106] To make the above-mentioned objects, features, and advantages of the present application more clearly understood, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings. The following description sets forth many specific details to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar improvements without violating the scope of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0107] In the description of this application, it should be understood that, unless otherwise expressly specified or limited, terms such as "installed," "connected," "connect," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections, electrical connections; direct connections, or indirect connections through an intermediate medium; and internal connections between two components or interactions between two components, unless otherwise expressly limited. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0108] Example 1:

[0109] Reference Figure 1 As shown, this embodiment provides an acoustic emission energy signal separation method, comprising the following steps:

[0110] Step S1: generating a plurality of single-frequency excitation signals, wherein the frequencies of the plurality of single-frequency excitation signals are logarithmically spaced;

[0111] Step S2: transmitting a plurality of the single-frequency excitation signals through a transducer, and receiving echoes to obtain a time domain signal data set;

[0112] Step S3: performing time-frequency analysis on the time domain signal data set to obtain a time-frequency spectrum set;

[0113] Step S4: extracting features from the time-frequency spectrum set to obtain a feature matrix;

[0114] Step S5: performing principal component analysis on the feature matrix to obtain a dimension-reduced feature matrix;

[0115] Step S6: performing cluster analysis on the dimension-reduced feature matrix to obtain a clustering result, wherein the clustering result includes a category label for each data point, and the category label includes reflection, transmission, and noise;

[0116] Step S7: constructing a dispersion model based on the clustering results and material physical parameters, incorporating the dispersion model into the dispersion model, and obtaining a verified clustering result;

[0117] Step S8: inputting the verification clustering result into the dispersion model, and based on the cluster center feature identification signal of the verification clustering result, marking the signal in the verification clustering result as a reflection signal, a transmission signal and a noise signal;

[0118] Step S9: superimposing and processing the transmission signals to obtain a reconstructed transmission signal;

[0119] Step S10: Calculating a separation effect index based on the single-frequency excitation signal and the reconstructed transmission signal.

[0120] Specifically, in step S1, the single-frequency excitation signal is a sine wave signal. In this embodiment, it is expressed as:

[0121] f i =f start *(f end / f start ) (i / (N-1))

[0122] Among them, f i represents the i-th frequency, f start and f end Indicates the starting and ending frequencies, and the total number of frequency points, i ranges from 0 to N-1.

[0123] As a preferred embodiment of this invention, the starting frequency f start is 100kHz, the end frequency f end The logarithmic spacing is 1MHz, and a wider frequency range can be covered with fewer frequency points, ensuring that both low and high frequencies are fully represented.

[0124] In this embodiment, the time-frequency analysis in step S3 is wavelet transform, which can provide good time resolution for high-frequency components and good frequency resolution for low-frequency components. At the same time, wavelet transform can adapt to signal characteristics and more effectively capture transient characteristics.

[0125] In some implementations of this embodiment, in step S3, the time-frequency analysis is continuous wavelet transform:

[0126] W(a,b)=(1 / sqrt(|a|))∫x(t)ψ*((tb) / a)dt;

[0127] Among them, ψ(t) represents the mother wavelet, a represents the scale parameter, and b represents the translation parameter.

[0128] In some other implementations of this embodiment, in step S3, the time-frequency analysis is discrete wavelet transform:

[0129] W[m,n]=(1 / sqrt(a0m ))∑x[k]ψ*((k-nb0 a0^m) / a0 m );

[0130] Among them, m and n are integers, a0>1 represents a fixed expansion step size, and b0>0 represents a position parameter.

[0131] In step S4, each row of the feature matrix represents a signal of a frequency in the time-frequency spectrum set, and each column represents a feature; the features include peak amplitude, root mean square value, form factor, kurtosis, spectral entropy, energy density ratio, center frequency, frequency diffusion width, diffusion coefficient, and group velocity dispersion.

[0132] Specifically, the following steps are included:

[0133] Step S4.1: Calculate the power spectral density (PSD) of the signal;

[0134] Step S4.2: normalizing the power spectral density;

[0135] Step S4.3: Calculate spectral entropy based on the normalized power spectral density;

[0136] Step S4.4: dividing the signal into a high frequency band and a low frequency band based on the frequency range;

[0137] Step S4.5: Calculate the signal energy in the high frequency band and the low frequency band;

[0138] Step S4.6: Calculate the energy density ratio of the high frequency band and the low frequency band according to the signal energy.

[0139] Spectral entropy is used to reflect the complexity or uniformity of frequency distribution:

[0140] H=-∑(p i *log2(p i ));

[0141] Among them, p i represents the normalized power spectral density at frequency bin i.

[0142] The energy density ratio is used to compare the energy in different frequency bands:

[0143] EDR=E high / E low ;

[0144] Among them E high Represents the energy in the high frequency band, E low Represents the energy in the low frequency band.

[0145] Energy is expressed as: E = ∑|X(f)|^2;

[0146] Where X(f) represents the Fourier transform of the signal.

[0147] The step S5 comprises the following steps:

[0148] Step S5.1: Obtain the feature matrix X, the size of which is m×n, where m is the number of samples and n is the number of features;

[0149] Step S5.2: Calculate the central characteristic matrix of the characteristic matrix:

[0150] X centered =X–μ;

[0151] Wherein, μ is the mean vector of the feature matrix;

[0152] Step S5.3: Calculate the covariance matrix of the centered feature matrix:

[0153] C=(1 / m)*X centered T *X centered ;

[0154] Step S5.4: Perform eigenvalue decomposition on the covariance matrix:

[0155] C=VΛV^T;

[0156] Where V represents the eigenvector matrix, and Λ represents the diagonal matrix of eigenvalues;

[0157] Step S5.5: Arrange the eigenvectors of the eigenvector matrix in descending order, and select the first k eigenvectors to form the projection matrix W; the k is such that the cumulative explained variance ratio reaches a preset threshold:

[0158] explained_variance_ratio = λ i / ∑λ i ;

[0159] where λ i represents the eigenvalue;

[0160] Step S5.6: Project the centered feature matrix to the new subspace to obtain the reduced-dimensional feature matrix:

[0161] Y=X centered *W.

[0162] In step S6, a K-means algorithm is used to perform cluster analysis. In this embodiment, the algorithm K=3, representing the reflection signal, the transmission signal, and the noise signal respectively. The cluster analysis includes the following steps:

[0163] Step S6.1: define the neighborhood radius and the minimum number of points in the dense area;

[0164] Step S6.2: For each point in the reduced dimension feature matrix, if the number of points in its neighborhood is greater than or equal to the minimum number of points in the dense area, it is defined as a core point;

[0165] Step S6.3: For a core point, add all points in its neighborhood to a cluster, and add all points in the neighborhood of the cluster to the cluster, and repeat until no new points are added to the cluster;

[0166] Step S6.4: Define the points that do not belong to any cluster as noise points, and output a clustering result, which includes all the clusters and the noise points.

[0167] In some embodiments of the present invention, the dimension-reduced feature matrix is ​​expressed as:

[0168] F=[F1,F2,F3,F4,F5];

[0169] Among them, F1 represents the frequency characteristic, F2 represents the energy characteristic, F3 represents the propagation characteristic, F4 represents the dispersion attenuation characteristic, and F5 represents the time characteristic, which are respectively expressed as:

[0170] F1=[σf,Bw,fc];

[0171] Where σf represents the frequency diffusion width: σf=sqrt(∑(f-fc) 2 |X(f)| 2 / ∑|X(f)| 2 ); Bw represents the bandwidth, which is -3dB in this embodiment, thereby determining the frequency range in which the amplitude of the spectrum drops by 3dB: Bw = f upper -f lower , fc represents the center frequency: fc=Σ(f·|X(f)| 2 ) / Σ|X(f)| 2 ; Where X(f) represents the signal spectrum, calculated by fast Fourier transform: X(f) = FFT(x(t));

[0172] F2=[E ratio ,SNR,A norm ];

[0173] Among them, [E ratio represents the energy ratio, [E ratio =E high / E low , E high =∫|X(f)| 2 df and f∈[f c ,fmax ], E low =∫|X(f)| 2 df and f∈[f min ,f c In this embodiment, since the actual distance does not need to be considered, the ratio is used to eliminate the influence of the distance, and the SNR is calculated by segmented integration after FFT; SNR = 10log 10 (P signal / P noise ) is called signal-to-noise ratio, where P signal =Σ|X s (f)| 2 Indicates signal power, P noise =Σ|X n (f)| 2 represents the noise power X s and X n Represent the signal / noise spectrum respectively; A norm represents the normalized amplitude, A max =max{|x(t)|},t∈[0,T],A norm =|x(t)| / A max .

[0174]

[0175] Among them, vg norm represents the normalized group velocity, vg=dω / dβ(ω), vg norm =vg / c0, c0 represents the longitudinal wave speed / transverse wave speed / other characteristic sound speed; Where φ(f) = arg(X(f)), which is the phase angle of X(f), f1 and f2 represent the common characteristic frequency points, which are the main frequency ±Δf in this embodiment; t rel =t / t first Represents relative time features, t is assumed to be the current time point, t first is the moment when the signal exceeds the threshold for the first time.

[0176] F4=[D,R freq ,η];

[0177] Where D = -ω / v·dv / dω represents the dispersion coefficient, v represents the velocity of sound, ω represents the angular frequency, and the phase velocity v(ω) = [v(ω) = ω / β(ω), R freq =|X(f1)| / |X(f2)| represents the frequency ratio. In this embodiment, a frequency ratio independent of the path is selected. f1 and f2 represent characteristic frequencies, i.e., the main frequency ±Δf; η = -ln(A(ω) / A0(ω)) / η refrepresents the normalized attenuation index, A(ω) and A0(ω) represent the current frequency amplitude and reference amplitude respectively, and η_ref represents the normalized reference value;

[0178] F5=[t rel ,t ratio ,t spread ];

[0179] Among them, t rel =t / t first represents the normalized value relative to the first wave arrival time; t ratio =t peak / t first , represents the ratio of peak time to first wave time; t spread =(t end -t start ) / t first Indicates the time width.

[0180] Therefore, in this embodiment, the cluster marked as a reflected signal has the following characteristics:

[0181] 0.8 <R freq <1.2;D <D th (D th ≈0.2);η(ω)<η th1 (η th1 ≈0.3); E ratio >0.7;t rel ≈1;t ratio <1.2;t spread <spread th1 ;

[0182] Clusters marked as transmission signals have the following characteristics:

[0183] 0.3 <R freq <0.8; D>D th η th1 <η(ω)<η th2 (η th2 ≈0.7); 0.3 <E ratio <0.7; t rel >1.2;t ratio >1.5; spread th1 <t spread <spread th2 ;

[0184] Clusters labeled as noise signals have the following characteristics:

[0185] R freq Random distribution; η(ω)>η th2 ;SNR <SNRth (SNR th ≈10dB); E ratio <0.3; t_rel randomly distributed; t ratio Unstable; spread >spread th2 .

[0186] In step S7, an accurate dispersion model is constructed using the Kramers-Kronig relationship, which specifically includes the following steps:

[0187] Define the complex wave number: k(ω)=β(ω)+iα(ω);

[0188] Among them, β represents the phase constant and α represents the attenuation coefficient;

[0189] The Kramers-Kronig relation for wave number is expressed as:

[0190] β(ω)=β0+(2 / π)P∫[0,∞](xα(x) / (x 2 -ω 2 ))dx;

[0191] α(ω)=-(2ω / π)P∫[0,∞](β(x)-β0 / (x 2 -ω 2 ))dx;

[0192] Here, P represents the Cauchy principal value integral and β0 represents the low-frequency limit of β.

[0193] In this embodiment, β(ω) and α(ω) are expressed by approximate realization:

[0194] β(ω)≈β0+(2 / π)∑[i](Δω i α i ω i / (ω i 2 -ω 2 ));

[0195] α(ω)≈-(2ω / π)∑[i](Δω i (β i -β0) / (ω i 2 -ω 2 ));

[0196] Among them, Δω i represents the frequency step, α i and β i Represents the frequency ω i The measured value at .

[0197] Calculate the phase velocity:

[0198] v p (ω) = ω / β(ω);

[0199] Calculate the group velocity:

[0200] v g (ω) = dω / dβ(ω);

[0201] Step S7.1: Based on the clustering results, the transmission signal dispersion analysis calculates the frequency-diffusion relationship, the reflection signal dispersion analysis calculates the frequency dispersion, and calculates the comprehensive dispersion function;

[0202] Step S7.1.1: Extract frequency diffusion features, where f represents the signal frequency, fc represents the center frequency, X(f) represents the signal spectrum, |X(f)| 2 Represents the power spectral density, and the frequency diffusion width σf describes the degree of signal broadening in the frequency domain:

[0203] σf=sqrt(Σ(f-fc) 2 |X(f)| 2 / Σ|X(f)| 2 );

[0204] Determine the frequency dependence of α(ω) and v(ω)

[0205] Establish the frequency-diffusion relationship, D trans (ω) describes the frequency broadening characteristics of the transmitted signal, quantifies the discreteness of the signal frequency distribution, and reflects the frequency change law of the sound wave when propagating in different media:

[0206] D trans (ω)=f(σf,α(ω),v(ω));

[0207] Step S7.1.2: Extract frequency dispersion characteristics and calculate v(ω) and R(ω)

[0208] Analyze dispersion patterns and study frequency dependence

[0209] Establish the frequency-dispersion relationship, D ref (ω) characterizes the dispersion characteristics of the reflected signal:

[0210] D ref (ω)=g(v(ω),R(ω));

[0211] Step S7.1.3: Calculate the integrated dispersion function:

[0212] D(ω)=Σ(wi*Di(ω)) / Σwi;

[0213] Where D(ω) represents the comprehensive dispersion function, Di(ω) represents the dispersion function of each group, and wi represents the weight based on signal quality and physical rationality;

[0214] Step S7.2: Calculate the diffusion function of each cluster group, check consistency and update the dispersion classification, and calculate the diffusion difference of each cluster group:

[0215] Step S7.2.1: Extract frequency diffusion features for each cluster group and calculate σf and Dc;

[0216] Analyze diffusion laws and study changes in α(ω) and v(ω);

[0217] Compute the diffusion function:

[0218] D(ω)=(f max -f min ) / f center ;

[0219] Step S7.2.2: Calculate the dispersion characteristic variance:

[0220] Var(D)=Σ(Di-D mean ) 2 / n;

[0221] Identify abnormal diffusion points:

[0222] |Di-D mean |>3σ;

[0223] Eliminate outliers, reclassify, and update dispersion classification;

[0224] Step S7.2.3: Analyze the dispersion differences between different categories: Calculate the dispersion feature differences;

[0225] Verify the rationality of dispersion physics: check whether it meets the physical laws of the material;

[0226] Calculate the diffusion difference:

[0227] ΔD=|D1(ω)-D2(ω)| / D ref ;

[0228] Step S7.3: According to the updated clustering model, verify based on the theoretical dispersion model and update the dispersion classification:

[0229] Step S7.3.1: Calculate the frequency spread width:

[0230] σf=sqrt(Σ(f-fc) 2 |X(f)| 2 / Σ|X(f)| 2 );

[0231] Calculate the diffusion characteristic deviation:

[0232] δD=|Dm-Dc| / Dc;

[0233] Where Dm represents the measured diffusion function and Dc represents the theoretical diffusion function;

[0234] Calculate frequency dispersion:

[0235] ΔV=|V(f2)-V(f1)| / V(f1);

[0236] Step S7.3.2: For each cluster group, if (σf < 0.2) && (δD < 0.15) && (ΔV < 0.1) is satisfied, the cluster group passes; if not, the model parameters are adjusted;

[0237] Step S7.3.3: Remove points that do not conform to the dispersion law:

[0238] |DD mean |>2σ;

[0239] Re-divide the boundary based on ΔD, calculate the dispersion feature center and update D mean and σ.

[0240] The step S9 includes the following steps:

[0241] Step S9.1: Constructing a signal model based on the transmission signal:

[0242] y=Φx+n;

[0243] Where y represents the measurement signal, Φ represents the measurement matrix, x represents the original signal, and n represents the noise;

[0244] Step S9.2: Compressed sensing problem:

[0245] min||x||1subject to||y-Φx||2≤ε;

[0246] Among them, ||·||1 represents the L1 norm, ||·||2 represents the L2 norm, and ε represents the noise level;

[0247] Step S9.3: Implement the basis pursuit denoising algorithm:

[0248] min(1 / 2)||y-Φx||2 2 +λ||x||1;

[0249] Where λ represents the regularization parameter;

[0250] Step S9.4: Implement the orthogonal matching pursuit algorithm:

[0251] r0=y, x0=0;

[0252] For k = 1 to K:

[0253]

[0254] ii.Λ k =Λ_{k-1}∪{λ k};

[0255] iii.x k =arg min||y-Φ_Λ k x||2iv.r k =y-Φx k Return x k ;

[0256] Among them, φ j represents the columns of Φ, Denotes a submatrix of Φ, whose columns are represented by Λ k Index. By applying compressed sensing techniques, the complete transmission signal can be reconstructed from a reduced number of measurements, potentially speeding up the acquisition process and reducing data storage requirements.

[0257] The separation effect indicators in step S10 include: signal-to-noise ratio, mean square error, peak signal-to-noise ratio, structural similarity index, correlation coefficient, spectrum consistency, and energy recovery ratio. The specific calculation method is as follows.

[0258] Signal-to-Noise Ratio (SNR):

[0259] SNR=10*log10(P 信号 / P 噪声 )

[0260] Where P_signal represents the power of the signal, and P_noise represents the power of the noise.

[0261] P 信号 =∑|s[n]| 2 ;

[0262] P 噪声 =∑|x[n]-s[n]| 2 ;

[0263] Where s[n] represents the original signal and x[n] represents the reconstructed signal.

[0264] Mean Square Error (MSE): MSE = (1 / N) * ∑ (x[n] - s[n]) 2 Where N is the signal length.

[0265] Peak Signal-to-Noise Ratio (PSNR):

[0266] PSNR=10*log10(MAX 2 / MSE);

[0267] Here, MAX represents the maximum possible value of the signal.

[0268] Structural Similarity Index (SSIM):

[0269] SSIM=(2μxμs+c1)(2σxs+c2) / ((μx 2 +μs 2 +c1)(σx 2 +σs 2 +c2));

[0270] Where μx and μs represent the means of x and s respectively, σx and σs represent the standard deviations, σxs represents the covariance, and c1 and c2 are constants.

[0271] Correlation coefficient:

[0272] R=∑((x[n]-μx)(s[n]-μs)) / (σx*σs);

[0273] Spectrum consistency:

[0274] SC=∑min(|X(f)|,|S(f)|) / max(∑|X(f)|,∑|S(f)|);

[0275] where X(f) and S(f) represent the spectra of x[n] and s[n] respectively.

[0276] Energy recovery ratio:

[0277] RR=∑|x[n]| 2 / ∑|s[n]| 2 .

[0278] Example 2:

[0279] This embodiment provides an acoustic emission sensor, which realizes signal separation by using the acoustic emission energy signal separation method described in the first embodiment.

[0280] This embodiment provides a method for separating acoustic emission energy signals. This method obtains a time-domain signal dataset by emitting multiple single-frequency excitation signals and receiving echoes. Time-frequency analysis, feature extraction, principal component analysis, cluster analysis, and input into a dispersion model for verification are then performed to separate the signal into a reflection signal, a transmission signal, and a noise signal. Finally, the transmission signal is reconstructed and the separated signal is evaluated to obtain a high-quality separated transmission signal. By analyzing the diffusion characteristics of sound waves of different frequencies in a material, the present invention effectively distinguishes between reflection signals, transmission signals, and ambient noise, thereby accurately identifying the energy signal that actually passes through an object. The high-quality separated transmission signal is used for air-coupled ultrasonic testing, effectively reducing the interference of reflection and noise signals and improving detection accuracy.

[0281] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0282] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for separating acoustic emission energy signals, characterized in that: The following steps are involved: Step S1: generating a plurality of single-frequency excitation signals, wherein the frequencies of the plurality of single-frequency excitation signals are logarithmically spaced; Step S2: transmitting a plurality of the single-frequency excitation signals through a transducer, and receiving echoes to obtain a time domain signal data set; Step S3: performing time-frequency analysis on the time domain signal data set to obtain a time-frequency spectrum set; Step S4: extracting features from the time-frequency spectrum set to obtain a feature matrix; Step S5: performing principal component analysis on the feature matrix to obtain a dimension-reduced feature matrix; The step S5 comprises the following steps: Step S5.1: Obtain the feature matrix X, the size of which is m×n, where m is the number of samples and n is the number of features; Step S5.2: Calculate the central characteristic matrix of the characteristic matrix: X centered = X – μ; Wherein, μ is the mean vector of the feature matrix; Step S5.3: Calculate the covariance matrix C of the centered feature matrix: ； Step S5.4: Perform eigenvalue decomposition on the covariance matrix: C = VΛV^T; Where V represents the eigenvector matrix, and Λ represents the diagonal matrix of eigenvalues; Step S5.5: Arrange the eigenvectors of the eigenvector matrix in descending order, and select the first k eigenvectors to form the projection matrix W; the k is such that the cumulative explained variance ratio reaches a preset threshold: explained_variance_ratio = λ i / ∑λ i ; where λ i represents the eigenvalue; Step S5.6: Project the centered feature matrix to the new subspace to obtain the reduced-dimensional feature matrix: ； Step S6: performing cluster analysis on the dimension-reduced feature matrix to obtain a clustering result, wherein the clustering result includes a category label for each data point, and the category label includes reflection, transmission, and noise; Step S7: constructing a dispersion model based on the clustering results and material physical parameters, incorporating the dispersion model into the dispersion model, and obtaining a verified clustering result; The step S7 includes the following steps: Step S7.1: Based on the clustering results, the transmission signal dispersion analysis calculates the frequency-diffusion relationship, the reflection signal dispersion analysis calculates the frequency dispersion, and calculates the comprehensive dispersion function; Step S7.2: Calculate the diffusion function of each cluster group, check consistency and update the dispersion classification, and calculate the diffusion difference of each cluster group: Step S7.3: According to the updated clustering model, verify based on the theoretical dispersion model and update the dispersion classification; Step S8: inputting the verification clustering result into the dispersion model, and based on the cluster center feature identification signal of the verification clustering result, marking the signal in the verification clustering result as a reflection signal, a transmission signal and a noise signal; Step S9: superimposing and processing the transmission signals to obtain a reconstructed transmission signal; The step S9 includes the following steps: Step S9.1: Constructing a signal model based on the transmission signal: y = Φx + n; Where y represents the measurement signal, Φ represents the measurement matrix, x represents the original signal, and n represents the noise; Step S9.2: Compressed sensing problem: ； in, represents the L1 norm, represents the L2 norm, ε represents the noise level; Step S9.3: Implement the basis pursuit denoising algorithm: ； Where λ represents the regularization parameter; Step S9.4: Implement the orthogonal matching pursuit algorithm: ； For k = 1 to K: ； ； return ; in, represents the columns of Φ, Denotes a submatrix of Φ whose columns are represented by index; Step S10: Calculating a separation effect index based on the single-frequency excitation signal and the reconstructed transmission signal.

2. The method for separating acoustic emission energy signals according to claim 1, wherein: In step S1, the single-frequency excitation signal is a sinusoidal wave signal.

3. The method for separating acoustic emission energy signals according to claim 1, wherein: In step S3, the time-frequency analysis is continuous wavelet transform: ； Among them, ψ(t) represents the mother wavelet, a represents the scale parameter, and b represents the translation parameter.

4. The method for separating acoustic emission energy signals according to claim 1, wherein: In step S3, the time-frequency analysis is discrete wavelet transform: ； Among them, m and n are integers, a0>1 represents a fixed expansion step size, and b0>0 represents a position parameter.

5. The method for separating acoustic emission energy signals according to claim 1, wherein: In step S4, each row of the feature matrix represents a signal of a frequency in the time-frequency spectrum set, and each column represents a feature; the features include peak amplitude, root mean square value, form factor, kurtosis, spectral entropy, energy density ratio, center frequency, frequency diffusion width, diffusion coefficient, and group velocity dispersion.

6. The method for separating acoustic emission energy signals according to claim 1, wherein: In step S6, the cluster analysis includes the following steps: Step S6.1: define the neighborhood radius and the minimum number of points in the dense area; Step S6.2: For each point in the reduced dimension feature matrix, if the number of points in its neighborhood is greater than or equal to the minimum number of points in the dense area, it is defined as a core point; Step S6.3: For a core point, add all points in its neighborhood to a cluster, and add all points in the neighborhood of the cluster to the cluster, and repeat until no new points are added to the cluster; Step S6.4: Define the points that do not belong to any cluster as noise points, and output a clustering result, which includes all the clusters and the noise points.

7. An acoustic emission sensor, characterized in that: The acoustic emission sensor realizes signal separation by the acoustic emission energy signal separation method according to any one of claims 1 to 6.

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