Elastomer layer carbon fiber composite material damage positioning method based on elliptic probability fusion

Abstract

The invention discloses an elastomer layer carbon fiber composite material damage positioning method based on elliptic probability fusion, which comprises the following steps: taking different sensors as excitation sources, and collecting Lamb wave signals of a plurality of sensing paths; performing continuous wavelet transform by using a complex Morlet wavelet, and extracting actually measured flight time; introducing a layered wave velocity correction model to optimize an elliptical orbit method, and calculating theoretical flight time and damage probability; introducing a dynamic short-axis optimization model to optimize a probability weighting method, and calculating a damage probability; generating prior distribution by using results calculated by an elliptical orbit method and a probability weighting method; constructing a likelihood function based on the difference between the actually measured flight time and the theoretical flight time; the posterior distribution of the damage position is obtained through the Bayesian theorem in combination with the prior distribution and the likelihood function; sampling the posterior distribution by using an adaptive MCMC algorithm, and generating a probability cloud picture of the damage position; and outputting a final imaging result of the damage position, and positioning the damage position.

Description

Technical Field

[0001] The present invention belongs to the technical field of composite material damage self-perception, and in particular relates to a method for locating damage of an elastomer layer carbon fiber composite material based on ellipse probability fusion. Background Art

[0002] Carbon fiber composites containing elastomer layers have been widely used in many fields such as aerospace, new energy vehicles, and high-end equipment maintenance due to their lightweight and high-strength properties. For example, in the field of new energy vehicles, they are used for battery pack casings and lightweight body parts (such as doors, chassis reinforcements, etc.); in the field of aerospace, they are used in aircraft skins, spacecraft shells and other structural parts; in the field of high-end equipment maintenance, wind turbine blades (carbon fiber-elastomer sandwich structures), industrial robot arms, etc. also use this material extensively; however, during use, such materials are prone to damage due to factors such as collisions and fatigue. If the damage is not discovered and located in time, serious consequences may occur. For example, in new energy vehicles, damage to the battery pack casing may lead to battery safety problems and shorten the battery pack life; in the field of aerospace, the accumulation of hidden damage to aircraft skins and spacecraft shells may cause structural failure and endanger flight safety; therefore, high-precision damage location of carbon fiber composites containing elastomer layers is crucial.

[0003] Because Lamb waves are sensitive to minute damage, Lamb wave damage localization technology based on the elliptical trajectory method is currently the mainstream technology for high-precision damage localization in carbon fiber composite materials containing elastomer layers. Existing technologies, such as patent CN110376282A, "A Lamb Wave Damage Localization Method Based on Elliptical Probability and Bayesian Estimation," integrate the elliptical trajectory method with the probabilistic damage reconstruction method. Using Bayesian estimation, this method combines the non-straight path detection capabilities of the elliptical trajectory method with the straight path detection capabilities of the probabilistic damage reconstruction method to achieve high-precision damage localization. However, existing technologies have many shortcomings. The material property differences between the elastomer layer and the carbon fiber layer cause the Lamb wave propagation velocity to exhibit layered variations. However, existing technologies use a single group velocity assumption and do not consider the layered variations in wave velocity caused by the elastomer layer. Therefore, they cannot accurately model the wave velocity distribution of multi-material composite structures, ultimately leading to large damage location errors. Furthermore, because the wave velocity mutation at the interface between the elastomer layer and the carbon fiber layer is not modeled, the elliptical trajectory generation is inaccurate. Furthermore, the probability weighted method relies on empirical parameters in its application, making it difficult to adapt to the complex signal propagation characteristics of CFRP materials containing elastomer layers. Furthermore, the elastomer layer may introduce nonlinear variations in wave velocity or path interference, leading to enhanced noise interference. However, traditional methods do not optimize the Bayesian fusion framework for such complex structures, resulting in positioning results that are susceptible to noise and unstable. The multi-layer heterogeneity of carbon fiber composites containing elastomer layers complicates the Lamb wave propagation path. However, existing methods mainly target homogeneous materials or simple laminates and do not consider the need for wave velocity correction at the interface between the elastomer layer and the carbon fiber layer. Therefore, they cannot be directly applied to composites containing elastomer layers. Summary of the Invention

[0004] The purpose of the present invention is to provide a damage localization method for elastomer layer carbon fiber composite materials based on elliptical probability fusion, which can significantly improve the accuracy and robustness of damage localization in multi-layer heterogeneous composite materials and adapt to complex wave velocity distribution and noise interference.

[0005] The technical solution provided by the present invention is:

[0006] A damage location method for elastomer layer carbon fiber composite material based on ellipse probability fusion, comprising:

[0007] Step 1: Install sensors on a damaged carbon fiber composite laminate containing an elastomer layer, use different sensors as excitation sources, and use an ultrasonic guided wave damage diagnosis platform system to collect Lamb wave signals from multiple sensing paths;

[0008] Step 2: Using complex Morlet wavelet to perform continuous wavelet transform on the Lamb wave signal to extract the measured flight time;

[0009] Step 3: Introduce the layered wave velocity correction model to optimize the elliptical trajectory method, and use the optimized elliptical trajectory method to calculate the theoretical flight time and damage probability;

[0010] Step 4: Introduce the dynamic short axis optimization model to optimize the probability weighted method, and use the optimized probability weighted method to calculate the damage probability;

[0011] Step 5: Generate a prior distribution of the damage probability calculated using the elliptical trajectory method and the probability weighted method; construct a likelihood function based on the difference between the measured flight time and the theoretical flight time; and obtain a posterior distribution of the damage location by combining the prior distribution and the likelihood function using Bayes' theorem.

[0012] Step 6: Use the adaptive MCMC algorithm to sample the posterior distribution and generate a probability cloud map of the damage location; output the final imaging result of the damage location to locate the damage location.

[0013] Preferably, the process of introducing a layered wave velocity correction model to optimize the elliptical trajectory method and calculating the theoretical flight time and damage probability is as follows: using a DC calculator to obtain group velocity samples in different directions, and using linear interpolation to construct a continuous group velocity function; introducing a layered wave velocity correction model to obtain a corrected continuous group velocity function; using the corrected continuous group velocity function to calculate the theoretical flight time; and using the measured flight time and the theoretical flight time to calculate the damage probability.

[0014] Preferably, the modified continuous group velocity function is:

[0015]

[0016] Where, v g (θ) is the modified continuous group velocity function, which reflects the change of wave velocity with direction in CFRP with elastic layer; i is the number of sensing path, i-th sensing path; θ is the target point, the given angle parameter; θ i ,θ i+1 are the two nearest neighboring points of the target point θ; For the two nearest neighbor points θ i ,θ i+1 The corresponding speed value; θ1 is the starting angle; θ2 is the angle greater than θ1 and closest to θ1; is the velocity value corresponding to θ1; is the speed value corresponding to θ2; θ n is the final angle; θ n-1 is less than θ n And with θ n The nearest adjacent angle; is with θ n The corresponding speed value; is with θ n-1 The corresponding speed value.

[0017] Preferably, the formula for calculating the theoretical flight time using the modified continuous group velocity function is:

[0018]

[0019] Where (x, y) is each grid intersection point, i.e., the damage location; T i TH (x,y) is the theoretical flight time of the i-th sensing path when the damage position is (x,y); (x r ,y r ) is the receiving point; θ r,i is the angle of each grid intersection point relative to the receiving point; v g (θ r,i ) is a continuous group velocity function of the angle of each grid intersection point relative to the receiving point, by replacing θ r,i Substitute the modified continuous group velocity function v g (θ) is obtained; (x e ,y e ) is the excitation point; θ e,i is the angle of each grid intersection point relative to the excitation point; v g (θ e,i ) is a continuous group velocity function of the angle of each grid intersection point relative to the excitation point, and θ e,i Substitute the modified continuous group velocity function v g (θ) is obtained.

[0020] Preferably, the formula for calculating the damage probability using the optimized probability weighted method is:

[0021]

[0022] Where p elliptical (x, y) is the damage probability calculated at the damage position (x, y) using the optimized elliptical trajectory method, that is, the probability distribution of the elliptical trajectory; N P is the total number of sensing paths; τ0 is the attenuation factor; T i is the measured flight time of the i-th sensor path.

[0023] Preferably, the process of introducing the dynamic short-axis optimization model to optimize the probability weighted method is as follows: for the Lamb wave signals of the carbon fiber composite laminate containing an elastomer layer in a healthy state and a damaged state, the first wave packet data of the Lamb wave signal collected by the receiving end of each sensing path is selected, and the dynamic shape parameter calculation is used to screen the main damage path, and an adaptive imaging model is constructed based on the screening results.

[0024] Preferably, the formula for calculating the damage probability using the optimized probability weighted method is:

[0025]

[0026] Where p rapid (x, y) is the damage probability calculated at the damage location (x, y) using the optimized probability weighting method, that is, the probability distribution of probability weighting; k is the number of the ellipse, that is, the ellipse determined by the k-th sensing path; M is the total number of ellipses; ρ is the correlation coefficient; W′ k (x,y) is the weight function; c k is the coordinate of the center of the ellipse; α k is the shape parameter.

[0027] Preferably, the prior distribution is:

[0028]

[0029] Where p prior (x,y) is the prior distribution.

[0030] Preferably, the formula for constructing the likelihood function is:

[0031]

[0032] Where p(D|U) is the likelihood function; D is the set of representations of the measured flight time, U represents the uncertainty parameter, U = (x, y, σ); σ is an adjustable parameter that reflects the uncertainty of measurement noise.

[0033] The beneficial effects of the present invention are:

[0034] The damage localization method for elastomer layer carbon fiber composite materials based on elliptical probability fusion provided by the present invention accurately locates damage and reduces errors through a layered wave velocity correction model and a dynamic short axis optimization algorithm; the adaptive MCMC algorithm and the optimized Bayesian fusion framework enhance the anti-interference capability; it is also applicable to a variety of multi-layer heterogeneous composite materials and is widely used in multiple fields, with higher computational efficiency and more flexible parameter optimization; it effectively improves the accuracy, robustness and material adaptability of damage localization of carbon fiber composite materials containing elastomer layers. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of the damage location method for elastomer layer carbon fiber composite materials based on ellipse probability fusion according to the present invention.

[0036] Figure 2This is a schematic diagram of the sensor locations and damage locations on the carbon fiber composite material laminate containing an elastomer layer according to the present invention.

[0037] Figure 3 This is a schematic diagram of the scattered signal described in the present invention.

[0038] Figure 4 It is the time-frequency spectrum of the continuous wavelet transform of the scattered signal described in the present invention.

[0039] Figure 5 This is the ToF calculation graph based on wavelet transform described in the present invention.

[0040] Figure 6 This is a diagram showing the positioning results of the damage position 1 using the existing technology as described in the present invention.

[0041] Figure 7 This is a diagram showing the positioning results of the damage location 1 obtained by using the elastic layer carbon fiber composite material damage positioning method based on ellipse probability fusion as described in the present invention.

[0042] Figure 8 This is a diagram showing the positioning results of the damage position 2 using the existing technology as described in the present invention.

[0043] Figure 9 This is a diagram showing the positioning results of the damage location 2 using the elastic layer carbon fiber composite material damage positioning method based on ellipse probability fusion as described in the present invention.

[0044] Figure 10 This is a diagram showing the positioning results of the damage position 3 using the existing technology described in the present invention.

[0045] Figure 11 This is a diagram showing the positioning results of the damage location 3 using the elastic layer carbon fiber composite material damage positioning method based on ellipse probability fusion as described in the present invention. DETAILED DESCRIPTION

[0046] The present invention will be described in further detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0047] like Figure 1 As shown, the present invention provides a method for locating damage in an elastomer layer carbon fiber composite material based on ellipse probability fusion, and the specific implementation process is as follows:

[0048] Step 1: Install sensors on a damaged carbon fiber composite material (CFRP) laminate containing an elastomer layer and determine their relative placement. Using different sensors as excitation sources to form multiple sensing paths, an ultrasonic guided wave damage diagnosis platform system is used to collect Lamb wave signals from these multiple sensing paths. The ultrasonic guided wave damage diagnosis platform system includes an excitation source sensor, a receiving sensor, and a wave signal processing algorithm module. The excitation source sensor is used to transmit an excitation Lamb wave signal, and the receiving sensor is used to collect Lamb wave signals in both damaged and healthy states of the carbon fiber composite material containing an elastomer layer. Within the same sensing path, the wave signal processing algorithm module uses the difference between the damaged and healthy Lamb wave signals collected by the receiving sensor to obtain a scattered signal.

[0049] Step 2: Use complex Morlet wavelet to perform continuous wavelet transform on the scattered signal; select the complex Morlet wavelet as the basis function. When using the complex Morlet wavelet to analyze the scattered Lamb wave, the mapping relationship between each frequency and scale parameter is expressed as:

[0050]

[0051] Where, f is the Lamb wave excitation frequency, f mz is the center frequency of the complex Morlet wavelet, f sc is the sampling frequency of the scattered signal; a is the scale parameter, which determines the time-frequency resolution of the basis function, corresponds to the frequency domain distribution characteristics of the scattered signal, and is used to control the up and down scaling (frequency change) of the wavelet function;

[0052] The continuous wavelet transform of the scattered signal can be defined as the inner product operation of the basis function and the scattered signal at different scales and positions, which can be expressed as:

[0053]

[0054] Where CWT(a, b) is the continuous wavelet transform (CWT) coefficient, CWT(a, b) represents the local characteristics of the scattered signal at the corresponding time and frequency; b is the displacement parameter, which represents the time positioning of the basis function, reflects the time evolution law of the scattered signal characteristics, and is used to control the left and right translation (time change) of the wavelet function; S(t) is the scattered signal; ψ is the basis function, which is a function with localized characteristics in time and frequency, and is used to extract the time-frequency characteristics of the scattered signal; ψ * is the complex conjugate of the basis function; t is time;

[0055] The time-frequency spectrum of the continuous wavelet transform of the scattered signal is generated by taking the frequency change corresponding to the scale parameter a (obtained by converting the frequency-scale mapping relationship) as the ordinate and the time change corresponding to the displacement parameter b as the abscissa.

[0056] The scattered signal after continuous wavelet transform and the excitation Lamb wave signal emitted by the excitation source sensor are combined in the ToF calculation graph based on wavelet transform. The time corresponding to the first peak of the scattered signal is subtracted from the time corresponding to the peak of the excitation signal to calculate the flight time characteristic value, that is, the measured flight time (ToF).

[0057] Step 3: Discretize the inspection area into multiple grid structures, and each grid intersection has an independent theoretical flight time; assume that each grid intersection is the damage location, and use the measured flight time and the theoretical flight time to calculate the damage probability of each grid intersection.

[0058] When calculating the theoretically predicted flight time, a layered wave velocity correction model is introduced to perform wave velocity anisotropy correction and calibration to avoid the errors caused by the assumption of isotropic wave velocity in traditional methods.

[0059] The DC calculator (Dispersion Calculator) is used to obtain group velocity samples in different directions. The continuous group velocity function is constructed using the linear interpolation method. The layered wave velocity correction model is introduced to obtain the corrected continuous group velocity function, which is expressed as:

[0060]

[0061] Where, v g (θ) is the modified continuous group velocity function, which reflects the change of wave velocity with direction in CFRP with elastic layer; i is the number of sensing path, i-th sensing path; θ is the target point, the given angle parameter; θ i ,θ i+1 are the two nearest neighboring points of the target point θ; For the two nearest neighbor points θ i ,θ i+1 The corresponding speed value; θ1 is the starting angle; θ2 is the angle greater than θ1 and closest to θ1; is the velocity value corresponding to θ1; is the speed value corresponding to θ2; θ n is the final angle; θ n-1 is less than θ n And with θ n The nearest adjacent angle; is with θ n The corresponding speed value; is with θ n-1 The corresponding speed value.

[0062] To calculate the theoretical flight time at each grid intersection, the angle is needed to determine the group velocity of each path segment:

[0063] Each grid intersection point (x, y) is relative to the excitation point (x e ,y e ) angle θ e,i It can be expressed as:

[0064] θ e,i =arctan 2(y e -y,x e -x)

[0065] Where (x, y) is each grid intersection point, i.e. the damage location; (x e ,y e ) is the excitation point; θ e,i is the angle of each grid intersection point relative to the excitation point; arctan 2 is the inverse tangent function, which can correctly handle the quadrant of the angle and return a value in the range of (-π, π];

[0066] Each grid intersection point (x, y) is relative to the receiving point (x r ,y r ) angle θ r,i It can be expressed as:

[0067] θ r,i =arctan 2(y r -y,x r -x)

[0068] In the formula, (x r ,y r ) is the receiving point; θ r,i is the angle of each grid intersection point relative to the receiving point;

[0069] Based on the above perspectives, the theoretical flight time of each grid intersection point is calculated using the optimized elliptical trajectory method:

[0070]

[0071] Where, T i TH (x, y) is the theoretical flight time of the i-th sensing path when the damage position is (x, y); v g (θ r,i ) is a continuous group velocity function of the angle of each grid intersection point relative to the receiving point, by replacing θ r,i Substitute the modified continuous group velocity function v g (θ) is obtained; v g (θe,i ) is a continuous group velocity function of the angle of each grid intersection point relative to the excitation point, and θ e,i Substitute the modified continuous group velocity function v g (θ) is obtained.

[0072] The error between the theoretically predicted damage location and the actual damage location is represented by ε. The relationship between the measured flight time and the theoretical flight time of the i-th sensor path is:

[0073] T i =T i TH (x,y)+ε

[0074] Where, T i is the measured flight time of the ith sensor path, s; ε is the error variable;

[0075] The damage probability of each grid intersection point is calculated using the measured flight time and the theoretical flight time:

[0076]

[0077] Where p elliptical (x, y) is the damage probability calculated at the damage position (x, y) using the optimized elliptical trajectory method, that is, the probability distribution of the elliptical trajectory; N P is the total number of sensing paths; τ0 is the attenuation factor, which is set to 0.01ms in this study.

[0078] The anisotropic characteristics of wave velocity are incorporated into the theoretical time of flight (ToF) calculation of the elliptical trajectory method to optimize the damage localization accuracy; the optimized elliptical trajectory method is used to significantly improve the damage localization accuracy.

[0079] Step 4: For the Lamb wave signals of the CFRP laminate containing an elastomer layer in the healthy state and the damaged state, the first wave packet data of the Lamb wave signal collected by the receiving sensor at the receiving end of each sensing path is selected, and a dynamic short axis optimization model is introduced. Dynamic shape parameter calculation is used to replace the traditional method's reliance on fixed parameters. By screening the main damage paths, redundant or interfering information is eliminated, and data quality is optimized. An adaptive imaging model is constructed based on the screening results to enhance the damage localization capability. The optimized probability weighted method is used to calculate the damage probability of each grid intersection:

[0080]

[0081] Where p rapid(x, y) is the damage probability calculated at the damage location (x, y) using the optimized probability weighting method, that is, the probability distribution of probability weighting; k is the number of the ellipse, that is, the ellipse determined by the k-th sensing path; M is the total number of ellipses; ρ is the correlation coefficient; W′ k (x,y) is the weight function; c k is the coordinate of the center of the ellipse; α k is the shape parameter.

[0082] The optimized probability weighted method is used to significantly improve the damage location accuracy.

[0083] Step 5: The probability distribution of the elliptical trajectory method and the probability distribution of the probability weighting method are combined to generate a priori distribution, which is expressed as:

[0084]

[0085] Where p prior (x,y) is the prior distribution;

[0086] The prior distribution adopts the contribution of geometric mean equilibrium elliptical locus method and probability weighting method, and is normalized;

[0087] The uncertain parameters are expressed as a vector U = (x, y, σ); the measured flight time is expressed as Based on the difference between the measured flight time and the corrected theoretically predicted flight time, the formula for constructing the likelihood function is:

[0088]

[0089] Where p(D|U) is the likelihood function; σ is an adjustable parameter that reflects the uncertainty of measurement noise;

[0090] Assuming that the error variable ε follows a normal distribution with mean zero and standard deviation σ, the likelihood function can be written as:

[0091]

[0092] Using Bayesian estimation, the prior distribution is multiplied by the likelihood function and normalized to calculate the posterior distribution of the damage location:

[0093] p(U|D)∝p(D|U)p prior (x,y)

[0094] Where p(U|D) is the posterior probability density function of the predicted damage coordinates, that is, the posterior distribution.

[0095] Step 6: Use the adaptive MCMC algorithm to sample the posterior distribution. The specific process is as follows:

[0096] Step 1. Set the initial state (x0, y0, σ0) = (125, 125, 5), which is located near the center of the grid. This choice is based on the geometric symmetry of the grid range (0 to 300 mm) to ensure that the initial point is representative.

[0097] Step 2: Generate candidate states using normal distribution:

[0098]

[0099] Where x prop is the proposed x coordinate value; y prop is the proposed y-coordinate value; σ prop is the proposed standard deviation; sd x sd is the standard deviation control parameter of the x-coordinate, which is used to adjust the exploration range of the x-coordinate; y sd is the standard deviation control parameter of the y coordinate, which is used to adjust the exploration range of the y coordinate; σ is the standard deviation control parameter of the standard deviation, which is used to adjust the exploration range of the standard deviation; To represent the standard normal distribution with a mean of 0 and a variance of 1, it is a random variable used to introduce randomness;

[0100] in,

[0101] The standard deviation of the normal distribution sd x 、sd y 、sd σ Control the jump range of candidate states to balance exploration efficiency and acceptance rate.

[0102] Step 3: Set the sampling boundary conditions to ensure that the target value meets the actual physical environment condition constraints during sampling. The sampling boundary conditions are:

[0103]

[0104] The step 3 avoids sampling beyond the grid range or generating non-physical noise parameters.

[0105] Step 4: Calculate the reception rate for each sample value (x, y) and σ:

[0106] α=min{1,exp[log p(x prop ,y prop ,σ prop |D)-log p(U|D)]}

[0107] Where α is an acceptance probability, which is used to decide whether to accept the new proposed state.

[0108] If the random number rand < α, then accept the new state (x prop ,y prop ,σ prop ), otherwise keep the current state; this mechanism ensures that the chain converges to the posterior distribution.

[0109] Step 5: Sampling is performed, with a total sampling number N=100000. Through the above steps, the adaptive MCMC algorithm successfully generates samples of the posterior distribution.

[0110] The sampling results of the adaptive MCMC algorithm are combined with the posterior probability density function for visualization to generate imaging results of the damage location; the area with a confidence level greater than 85% is selected, the final imaging result of the damage location is output, the damage location is predicted, and the damage location is located.

[0111] Step 7: Evaluate the positioning accuracy of the improved method by comparing the error between the actual damage location and the predicted damage location.

[0112] In the damage location method for elastomer layer carbon fiber composite materials based on elliptical probability fusion provided by the present invention, a machine learning model can be used to replace the layered wave velocity correction model to predict the wave velocity distribution, but it requires a large amount of training data and is relatively costly; a statistical model can be used to replace the Bayesian framework, but the computational complexity is significantly increased.

[0113] like Figure 2 As shown, in this embodiment, the CFRP laminate containing an elastomer layer is a square with a side length of 300 mm; one sensor is set at each of the four corners of the CFRP laminate containing an elastomer layer, and the sensor is 50 mm away from the edge; three impact positions are set in the CFRP laminate containing an elastomer layer as damage positions to be detected, namely: impact position 1 (125, 125), impact position 2 (200, 125) and impact position 3 (125, 150).

[0114] To investigate damage at different locations on CFRP laminates containing elastomer layers, Lamb wave technology was used. Sensors 1, 2, and 3 were used as excitation source sensors, and sensors 1, 2, 3, and 4 were used as receiving sensors. A sensing path was formed between any two sensors. Lamb wave detection signals from a total of six sensing paths were collected. A sampling frequency of 12 MHz was used, and 4,000 data points were recorded to ensure high resolution of the signal and integrity of the time domain information.

[0115] like Figure 3As shown, the ultrasonic guided wave damage diagnosis platform system uses the Lamb wave signals in the damaged state and the healthy state collected by the receiving sensor to obtain the scattered signal; the horizontal axis of the spectrum of the scattered signal is time, the unit is second (s), and the vertical axis is amplitude, the unit is millimeter (mm).

[0116] like Figure 4 As shown in FIG, the time-frequency spectrum of the scattering signal after continuous wavelet transformation, the horizontal axis is time, the unit is second (s), and the vertical axis is the normalized amplitude.

[0117] like Figure 5 As shown in the ToF calculation diagram based on wavelet transform, the horizontal axis is time in seconds (s), and the vertical axis is normalized amplitude. The time corresponding to the first peak of the scattered signal is subtracted from the time corresponding to the peak of the excitation signal to obtain the measured time of flight (ToF).

[0118] like Figure 6-11 As shown, the black circle represents the actual damage location, and the red cross represents the predicted damage location. Under the same data acquisition conditions, the damage localization method for elastomer-layer carbon fiber composite materials based on elliptical probability fusion provided by this invention significantly improves positioning accuracy compared to existing technologies, while effectively reducing the misjudgment rate of non-damaged locations.

[0119] The present invention provides an elastomer layer carbon fiber composite material damage location method based on elliptical probability fusion. For carbon fiber composite materials containing elastomer layers, a layered wave velocity correction model is proposed, which can accurately describe the wave velocity distribution in the elastomer layer and the carbon fiber layer, especially the wave velocity transition characteristics at the interface between the elastomer layer and the carbon fiber layer. The layered wave velocity correction model constructs a continuous group velocity function through a linear interpolation method, which solves the positioning error problem caused by the single wave velocity assumption in the traditional method; the elliptical trajectory method is improved by introducing a layered wave velocity correction model, and the elliptical equation is dynamically adjusted to adapt to the composite material structure containing an elastomer layer. The improved elliptical trajectory method can significantly improve the accuracy of damage location, especially in complex multi-layer heterogeneous materials; a dynamic short axis optimization algorithm is proposed to replace the traditional method of fixed shape The dependence of parameters is eliminated by screening the main damage paths and eliminating redundant information, which optimizes data quality, further improves the accuracy of damage location, enhances the adaptability of the probability weighted method under complex signal propagation characteristics, and can adapt to multi-layer heterogeneous materials; in the Bayesian estimation framework, the flight time data (likelihood function) after wave velocity correction is combined with the prior distribution of path correlation to optimize the damage location results; the introduction of the adaptive MCMC algorithm can dynamically adjust the sampling step size according to the posterior distribution, ensure the efficient convergence of the posterior distribution, reduce the computational complexity, and improve the real-time performance of damage location, making it suitable for real-time monitoring scenarios; in view of the nonlinear changes in wave velocity and path interference that may be introduced by the elastomer layer, the Bayesian fusion framework is optimized to enhance the robustness of the positioning results, ensure stable convergence in complex noise environments, and maintain high-precision positioning.

[0120] The damage localization method for elastomer layer carbon fiber composite materials based on elliptical probability fusion provided by the present invention can monitor damage caused by collision or fatigue in real time in the field of new energy vehicles, thereby extending the life of battery packs; accurately locate minor damage in the field of aerospace, thereby avoiding structural failure caused by the accumulation of hidden damage; and reduce downtime for maintenance in the field of high-end equipment maintenance, thereby improving the reliability of equipment operation.

[0121] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A damage location method for elastomer layer carbon fiber composite material based on ellipse probability fusion, characterized in that: include: Step 1: Install sensors on a damaged carbon fiber composite laminate containing an elastomer layer, use different sensors as excitation sources, and use an ultrasonic guided wave damage diagnosis platform system to collect Lamb wave signals from multiple sensing paths; Step 2: Using complex Morlet wavelet to perform continuous wavelet transform on the Lamb wave signal to extract the measured flight time; Step 3: Introduce the layered wave velocity correction model to optimize the elliptical trajectory method, and use the optimized elliptical trajectory method to calculate the theoretical flight time and damage probability; Step 4: Introduce the dynamic short axis optimization model to optimize the probability weighted method, and use the optimized probability weighted method to calculate the damage probability; Step 5: Generate a prior distribution of the damage probability calculated using the elliptical trajectory method and the probability weighted method; construct a likelihood function based on the difference between the measured flight time and the theoretical flight time; and obtain a posterior distribution of the damage location by combining the prior distribution and the likelihood function using Bayes' theorem. Step 6: Use the adaptive MCMC algorithm to sample the posterior distribution and generate a probability cloud map of the damage location; output the final imaging result of the damage location to locate the damage location.

2. The damage location method for elastomer layer carbon fiber composite material based on ellipse probability fusion according to claim 1 is characterized in that: The process of introducing the layered wave velocity correction model to optimize the elliptical trajectory method and calculating the theoretical flight time and damage probability is as follows: using a DC calculator to obtain group velocity samples in different directions, and using linear interpolation to construct a continuous group velocity function; introducing the layered wave velocity correction model to obtain a corrected continuous group velocity function; using the corrected continuous group velocity function to calculate the theoretical flight time; and using the measured flight time and the theoretical flight time to calculate the damage probability.

3. The damage location method for elastomer layer carbon fiber composite material based on ellipse probability fusion according to claim 2 is characterized in that: The modified continuous group velocity function is: Where, v g (θ) is the modified continuous group velocity function, which reflects the change of wave velocity with direction in CFRP containing elastic layer; i is the number of sensing path, i-th sensing path; θ is the target point, the given angle parameter; θ i ,θ i+1 are the two nearest neighboring points of the target point θ; For the two nearest neighbor points θ i ,θ i+1 The corresponding speed value; θ1 is the starting angle; θ2 is the angle greater than θ1 and closest to θ1; is the velocity value corresponding to θ1; is the speed value corresponding to θ2; θ n is the final angle; θ n-1 is less than θ n And with θ n The nearest adjacent angle; is with θ n Corresponding speed value; is with θ n-1 The corresponding speed value.

4. The damage location method for elastomer layer carbon fiber composite material based on ellipse probability fusion according to claim 3 is characterized in that: The formula for calculating the theoretical flight time using the modified continuous group velocity function is: Where (x, y) is each grid intersection point, i.e., the damage location; T i TH (x,y) is the theoretical flight time of the i-th sensing path when the damage position is (x,y); (x r ,y r ) is the receiving point; θ r,i is the angle of each grid intersection point relative to the receiving point; v g (θ r,i ) is a continuous group velocity function of the angle of each grid intersection point relative to the receiving point, by replacing θ r,i Substitute the modified continuous group velocity function v g (θ) is obtained; (x e ,y e ) is the excitation point; θ e,i is the angle of each grid intersection point relative to the excitation point; v g (θ e,i ) is a continuous group velocity function of the angle of each grid intersection point relative to the excitation point, and θ e,i Substitute the modified continuous group velocity function v g (θ) is obtained.

5. The damage location method for elastomer layer carbon fiber composite material based on ellipse probability fusion according to claim 4 is characterized in that: The formula for calculating the damage probability using the optimized probability weighted method is: Where p elliptical (x, y) is the damage probability calculated at the damage position (x, y) using the optimized elliptical trajectory method, that is, the probability distribution of the elliptical trajectory; N P is the total number of sensing paths; τ0 is the attenuation factor; T i is the measured flight time of the i-th sensor path.

6. The damage location method for elastomer layer carbon fiber composite material based on ellipse probability fusion according to claim 1 is characterized in that: The process of introducing the probability weighted method for dynamic short-axis optimization model optimization is as follows: for the Lamb wave signals of carbon fiber composite laminates containing elastomer layers in healthy and damaged states, the first wave packet data of the Lamb wave signal collected at the receiving end of each sensing path is selected, and the dynamic shape parameter calculation is used to screen the main damage paths, and an adaptive imaging model is constructed based on the screening results.

7. The damage location method for elastomer layer carbon fiber composite material based on ellipse probability fusion according to claim 6 is characterized in that: The formula for calculating the damage probability using the optimized probability weighted method is: Where p rapid (x, y) is the damage probability calculated at the damage location (x, y) using the optimized probability weighting method, that is, the probability distribution of probability weighting; k is the number of the ellipse, that is, the ellipse determined by the k-th sensing path; M is the total number of ellipses; ρ is the correlation coefficient; W k ′(x,y) is the weight function; c k is the coordinate of the center of the ellipse; α k is the shape parameter.

8. The damage location method for elastomer layer carbon fiber composite material based on ellipse probability fusion according to claim 5 or 7, characterized in that: The prior distribution is: Where p prior (x,y) is the prior distribution.

9. The damage location method for elastomer layer carbon fiber composite material based on ellipse probability fusion according to claim 8, characterized in that: The formula for constructing the likelihood function is: Where p(D|U) is the likelihood function; D is the set of representations of the measured flight time, U represents the uncertainty parameter, U = (x, y, σ); σ is an adjustable parameter that reflects the uncertainty of measurement noise.

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