Composite material acoustic emission source positioning system and positioning method

Abstract

The invention provides a composite material acoustic emission source positioning system and method, and the method comprises the steps: respectively extracting the arrival time of an acoustic wave signal reaching each acoustic emission sensor, and calculating the arrival time difference between every two acoustic emission sensors; taking the time difference of arrival as a known condition, taking an acoustic emission source coordinate as an optimization variable, and combining with a pre-calibrated sound velocity characteristic of the composite material to establish an optimization model taking a root-mean-square error of a theoretical time difference of arrival and an actual time difference of arrival as a target function; and improving an original whale optimization algorithm by using a nonlinear convergence factor and a random initialization strategy, carrying out iterative solution on the optimization model by using the improved whale optimization algorithm, and when a preset iteration termination condition is met, taking an optimal solution output by the optimization model as a positioning result. The positioning accuracy of the composite material generation source is improved, and the positioning difficulty is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of nondestructive material testing, and in particular to a composite material acoustic emission source positioning system and positioning method. Background Art

[0002] Composite materials are widely used in aerospace, rail transportation and many other fields due to their excellent properties such as light weight, high strength and good corrosion resistance. However, during the manufacturing and use process, materials will inevitably be damaged, such as cracks and delamination, which may lead to reduced mechanical properties and shortened service life of the materials. Therefore, timely and accurate determination of the damage location is of great significance to ensure the safe service of the structure.

[0003] Acoustic emission technology is a dynamic non-destructive testing method that evaluates the health of materials by detecting elastic wave signals accompanied by microscopic damage caused by stress changes inside the material. Acoustic emission source positioning is the core of acoustic emission technology, which can determine the specific location of damage, achieve early warning of damage and timely repair, thereby ensuring the safe service of composite materials.

[0004] However, due to the anisotropy of composite materials, the propagation speed of sound waves has significant directional dependence, and traditional acoustic emission source positioning methods are difficult to achieve satisfactory positioning results in such materials. When determining the acoustic emission position of such materials, existing technologies usually require a large number of sensors and complex signal processing algorithms, resulting in high acoustic emission source positioning costs and positioning accuracy that is difficult to meet positioning requirements.

[0005] In view of this, a composite material acoustic emission source positioning system and positioning method are provided to solve the problem that the existing composite material acoustic emission source positioning method is high in cost and cannot meet the positioning accuracy requirements. Summary of the invention

[0006] The present invention provides a composite material acoustic emission source positioning system and positioning method, in order to solve the problem that the existing composite material source positioning method is high in cost and cannot meet the positioning accuracy requirements based on the improved whale algorithm, thereby improving the positioning accuracy of the composite material source and reducing its positioning difficulty.

[0007] The present invention provides a composite material acoustic emission source positioning system, the system comprising:

[0008] A signal generator, which is disposed on the surface of the composite material to be tested and is used to excite sound waves;

[0009] Acoustic emission sensors, at least two of which are spaced apart and distributed on the surface of the composite material to be tested; the signal generator is connected to the acoustic emission sensors via a BNC cable, and the acoustic emission sensors are used to receive the sound waves excited by the signal transmitter;

[0010] A signal amplifier, wherein the acoustic emission sensor is connected to the signal amplifier via a BNC cable;

[0011] Acoustic emission signal collection equipment, the signal amplifier is connected to the acoustic emission signal collection equipment via a BNC cable;

[0012] The intelligent terminal is connected to the acoustic emission signal acquisition device through a BNC cable, and the intelligent terminal is used to display and record the results of the signals collected by the acoustic emission signal acquisition device.

[0013] The present invention also provides a composite material acoustic emission source positioning method, based on the positioning system as described above, the method comprises:

[0014] The arrival time of the sound wave signal at each acoustic emission sensor is extracted respectively, and the arrival time difference between every two acoustic emission sensors is calculated;

[0015] Taking the arrival time difference as a known condition and the coordinates of the acoustic emission source as the optimization variable, combined with the pre-calibrated sound velocity characteristics of the composite material, an optimization model is established with the root mean square error between the theoretical arrival time difference and the actual arrival time difference as the objective function.

[0016] The original whale optimization algorithm is improved by using nonlinear convergence factors and random initialization strategies, and the improved whale optimization algorithm is used to iteratively solve the optimization model. When the preset iteration termination condition is met, the optimal solution output by the optimization model is used as the positioning result.

[0017] In some embodiments, pre-calibrating the acoustic velocity characteristics of the composite material specifically includes:

[0018] Calibrate the acoustic velocity of the acoustic emission signal propagation characteristics of the composite material to be tested, and obtain the relationship between the acoustic wave propagation velocity and angle;

[0019] The obtained sound wave propagation velocity and angle are fitted to obtain a velocity-angle fitting polynomial.

[0020] In some embodiments, extracting the arrival time of the sound wave signal at each acoustic emission sensor specifically includes:

[0021] The original signal collected by the acoustic emission sensor is divided into time windows, wherein the signal in the time window at least includes the first arrival mode;

[0022] Calculate the envelope signal of the original signal in the time window;

[0023] An AIC function is executed on the envelope signal, and the time corresponding to the minimum value of the AIC function is used as the arrival time of the signal.

[0024] In some embodiments, the original whale optimization algorithm is improved by using a nonlinear convergence factor and a random initialization strategy, specifically including:

[0025] Initialize the relevant parameters of the original whale optimization algorithm and randomly generate the position of each individual whale; the relevant parameters at least include the population size N, the maximum number of iterations and the search range;

[0026] Taking the arrival time difference as the theoretical arrival time difference, calculating the fitness value of each individual whale based on the speed-angle fitting polynomial and the objective function, and determining the individual whale and its position with the minimum current error;

[0027] Location updates are made based on a randomly generated nonce.

[0028] In some embodiments, performing a location update based on a randomly generated random number specifically includes:

[0029] When the random number is ≥ 0.5 and the absolute value of the coefficient vector is ≤ 1, the surrounding contraction mechanism is used to update the position;

[0030] When the random number is ≥ 0.5 and the absolute value of the coefficient vector is > 1, a random search mechanism is used to update the position;

[0031] When the random number is less than 0.5, a spiral search mechanism is used to update the position.

[0032] In some embodiments, the preset iteration termination condition is:

[0033] The current number of iterations is equal to the maximum number of iterations.

[0034] The present invention also provides a composite material acoustic emission source positioning device for implementing the above method, the device comprising:

[0035] A time calculation unit, used to extract the arrival time of the sound wave signal to each acoustic emission sensor, and calculate the arrival time difference between every two acoustic emission sensors;

[0036] A model building unit is used to take the arrival time difference as a known condition, take the coordinates of the acoustic emission source as an optimization variable, and combine the sound velocity characteristics of the composite material calibrated in advance to establish an optimization model with the root mean square error between the theoretical arrival time difference and the actual arrival time difference as the objective function;

[0037] The result generating unit is used to improve the original whale optimization algorithm by using a nonlinear convergence factor and a random initialization strategy, and adopt the improved whale optimization algorithm to iteratively solve the optimization model. When a preset iteration termination condition is met, the optimal solution output by the optimization model is used as the positioning result.

[0038] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described above when executing the program.

[0039] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method described above when executed by a processor.

[0040] The composite material acoustic emission source positioning system and positioning method provided by the present invention respectively extract the arrival time of the sound wave signal to each acoustic emission sensor, and calculate the arrival time difference between every two acoustic emission sensors; take the arrival time difference as a known condition, take the coordinates of the acoustic emission source as an optimization variable, and combine the sound velocity characteristics of the pre-calibrated composite material to establish an optimization model with the root mean square error of the theoretical arrival time difference and the actual arrival time difference as the objective function; use the nonlinear convergence factor and the random initialization strategy to improve the original whale optimization algorithm, and use the improved whale optimization algorithm to iteratively solve the optimization model. When the preset iteration termination condition is met, the optimal solution output by the optimization model can be used as the positioning result.

[0041] In this way, when locating the acoustic emission source of the composite material, the present invention takes into account the influence of the direction of the sound wave propagation speed caused by anisotropy, converts the acoustic emission source positioning problem into an optimization problem, takes the arrival time difference extracted by different sensors as a known condition, takes the coordinates of the acoustic emission source as the optimization variable, constructs the objective function, and uses the improved whale optimization algorithm for iterative solution to finally obtain the position of the acoustic emission source; solves the problem that the existing composite material source positioning method is high in cost and cannot meet the positioning accuracy requirements, thereby improving the positioning accuracy of the composite material source and reducing its positioning difficulty. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the present invention or 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1One of the flow charts of the composite material acoustic emission source positioning method provided by the present invention;

[0044] Figure 2 The second flowchart of the composite material acoustic emission source positioning method provided by the present invention;

[0045] Figure 3 Flow chart 3 of the composite material acoustic emission source positioning method provided by the present invention;

[0046] Figure 4 Flow chart 4 of the composite material acoustic emission source positioning method provided by the present invention;

[0047] Figure 5 It is a structural schematic diagram of an acoustic emission source positioning system in a specific usage scenario;

[0048] Figure 6 A schematic diagram of a sound velocity calibration experiment in a specific usage scenario;

[0049] Figure 7 A schematic diagram of the structure of the composite material acoustic emission source positioning device provided by the present invention;

[0050] Figure 8 This is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] In order to solve the problems existing in the prior art, the present invention provides a composite material acoustic emission source positioning method based on an improved whale optimization algorithm, which calibrates the sound velocity of the material to be tested; uses the Akaike information criterion and cubic spline interpolation to extract the arrival time of the signal received by each sensor in turn; converts the acoustic emission source positioning problem into an optimization problem; and uses the improved whale optimization algorithm to iteratively solve and obtain the positioning result. The acoustic emission source positioning method proposed in the present invention effectively solves the problem that the propagation speed of sound waves caused by the anisotropy of composite materials has significant directional dependence, avoids the algorithm from falling into the local optimum, and realizes the precise positioning of the composite material acoustic emission source.

[0053] In a specific embodiment, the composite material acoustic emission source positioning system provided by the present invention includes a signal generator, an acoustic emission sensor, a signal amplifier, an acoustic emission signal acquisition device and an intelligent terminal; wherein the signal generator is arranged on the surface of the composite material to be tested and is used to excite sound waves; the acoustic emission sensors are at least two and are spaced and distributed on the surface of the composite material to be tested; the signal generator is connected to the acoustic emission sensor via a BNC cable, the acoustic emission sensor is used to receive the sound waves excited by the signal transmitter, and the acoustic emission sensor is connected to the signal amplifier via a BNC cable; the signal amplifier is connected to the acoustic emission signal acquisition device via a BNC cable, the acoustic emission signal acquisition device is connected to the intelligent terminal via a BNC cable, and the intelligent terminal is used to display and record the results of the signals collected by the acoustic emission signal acquisition device. The intelligent terminal can be understood as a display and recording system, for example, it can be a terminal device such as a computer or a mobile phone.

[0054] The present invention also provides a composite material acoustic emission source positioning method, based on the positioning system as described above, such as Figure 1 As shown, the method comprises the following steps:

[0055] S110: extracting the arrival time of the sound wave signal at each acoustic emission sensor respectively, and calculating the arrival time difference between every two acoustic emission sensors; extracting the arrival time of the received signal of each sensor in turn by combining Akaike Information Criterion (AIC, a statistical model selection criterion) with cubic spline interpolation, and calculating the arrival time difference between every two sensors; in actual use scenarios, there can be multiple acoustic emission sensors, and each sensor is distributed in a regular polygon on the composite material to be tested; this embodiment takes four acoustic emission sensors as an example, and places the four acoustic emission sensors on the surface of the composite material to be tested in a square arrangement, and sets the coordinates of the four vertices of the square to S1 (x1, y1), S2 (x2, y2), S3 (x3, y3), and S4 (x4, y4), respectively, and uses a signal generator to excite a Sinker pulse as an acoustic emission source, and sets the coordinates of the acoustic emission source to S (x, y). In the process of extracting the time difference, the four sensors are combined in pairs to form a total of six pairs of time differences.

[0056] S120: Taking the arrival time difference as a known condition and the coordinates of the acoustic emission source as optimization variables, combined with the pre-calibrated sound velocity characteristics of the composite material, an optimization model is established with the root mean square error between the theoretical arrival time difference and the actual arrival time difference as the objective function.

[0057] In other words, the acoustic emission source positioning problem is transformed into an optimization problem, the arrival time difference extracted by different sensors is taken as a known condition, the coordinates of the acoustic emission source are taken as the optimization variable, and the root mean square error between the theoretical time difference and the actual time difference is established as the optimization model of the objective function in combination with the sound velocity characteristics of the measured material. The specific formula is as follows:

[0058]

[0059] Where N is the number of sensor pairs, Δt i is the actual arrival time difference of the i-th pair of sensors, Δt i0 is the theoretical arrival time difference.

[0060] S130: The original whale optimization algorithm is improved by using a nonlinear convergence factor and a random initialization strategy, and the improved whale optimization algorithm is used to iteratively solve the optimization model. When the preset iteration termination condition is met, the optimal solution output by the optimization model is used as the positioning result. That is to say, in the original whale optimization algorithm, a nonlinear convergence factor and a random initialization strategy are introduced, and the improved whale optimization algorithm is used to iteratively solve. When the iteration termination condition is met, the acoustic emission source positioning can be achieved. Among them, the preset iteration termination condition is that the current number of iterations is equal to the maximum number of iterations.

[0061] In some embodiments, the acoustic velocity characteristics of the composite material are pre-calibrated, such as Figure 2 As shown, the specific steps include:

[0062] S210: performing sound velocity calibration on the propagation characteristics of the acoustic emission signal of the composite material to be tested, and obtaining the relationship between the propagation velocity and angle of the sound wave;

[0063] S220: Fitting the obtained sound wave propagation velocity and angle to obtain a velocity-angle fitting polynomial.

[0064] That is to say, before extracting the arrival time, it is necessary to calibrate the sound velocity characteristics of the composite material to be tested in advance; specifically, the sound velocity calibration is performed on the propagation characteristics of the acoustic emission signal of the composite material to be tested, and the relationship between the sound wave propagation speed and the angle is obtained. The experimental results are fitted with a cubic polynomial to obtain a speed-angle fitting polynomial; the fitting formula contains two variables, angle θ and speed v. Among them, if Figure 6 As shown, in the process of measuring the sound wave propagation characteristics of the composite material to be tested, it is necessary to measure the relationship between the propagation velocity and the angle. At this time, seven directions of 0°, 15°, 30°, 45°, 60°, 75°, and 90° are selected for sound velocity measurement.

[0065] In step S110, Figure 3As shown, extracting the arrival time of the sound wave signal to each acoustic emission sensor specifically includes the following steps:

[0066] S310: Divide the original signal collected by the acoustic emission sensor into a time window, wherein the signal in the time window at least includes the first arrival mode; first, divide the original signal collected by each sensor into a time window to ensure that the part of the signal at least includes the first arrival mode, and then use cubic spline interpolation to improve the time resolution of the signal.

[0067] S320: Calculate the envelope signal of the original signal in the time window; in a specific usage scenario, the Hilbert transform may be used to calculate the signal envelope to characterize the original shape of the signal. The specific formula is as follows:

[0068]

[0069] make Then the Hilbert envelope spectrum can be expressed as:

[0070]

[0071] Among them, |h(f)| is the envelope signal of the original signal.

[0072] S330: Execute the AIC function on the envelope signal, and use the time corresponding to the minimum value of the AIC function as the arrival time of the signal; in statistics and data analysis, AIC (Akaike Information Criterion) is an indicator for model selection. In this embodiment, the specific formula of AIC is as follows:

[0073] AIC(t k ) = t k .log{var[x n (1,t k )]}+(t N -t k -1)·log{var[x n (t k+1 ,t N )]}

[0074] Among them, t k represents the time of point k, k∈(1,n), x n (1,t k ) represents the signal from the start time to the current time t k The calculation range of var is the variance function, which is defined as:

[0075]

[0076] Where l is the length of the signal, is the average value of the signal.

[0077] In step S130, Figure 4 As shown in the figure, the original whale optimization algorithm is improved by using nonlinear convergence factor and random initialization strategy, which specifically includes the following steps:

[0078] S410: Initialize the relevant parameters of the original whale optimization algorithm and randomly generate the position of each individual whale; the relevant parameters at least include the population size N, the maximum number of iterations Max_iter and the search range;

[0079] S420: Taking the arrival time difference as the theoretical arrival time difference, calculating the fitness value of each individual whale based on the speed-angle fitting polynomial and the objective function, and determining the individual whale and its position with the minimum current error;

[0080] S430: Update the position based on the randomly generated random number, and randomly generate a random number between [0, 1].

[0081] S450: Before the next iteration, 10% of the whales are randomly selected for position initialization;

[0082] S460: Return to step S420 and recalculate the fitness values ​​of all current whale individuals;

[0083] S470: Determine whether the current number of iterations is less than a preset value Max_iter. If so, return to step S450; otherwise, output the current optimal solution as the optimal estimate of the acoustic emission source position.

[0084] In order to ensure the accuracy of location update, in step S430, when the range of random number values ​​changes, different location update strategies may be adopted, specifically:

[0085] Among them, when the random number is ≥ 0.5 and the absolute value of the coefficient vector is ≤ 1, the surrounding contraction mechanism is used to update the position. The specific formula is as follows:

[0086] D=|C·X * (t)-X(t)|

[0087] X(t+1)=X * (t)-A·D

[0088] Among them, t represents the current iteration number, X * is the optimal position of the tth iteration, X is the position vector, A and C are coefficient vectors, and the formula is expressed as:

[0089] A=2a·ra

[0090] C=2·r

[0091] Among them, r is a random number between [0,1], and the convergence factor a decreases nonlinearly from 2 to 0 during the iteration process. The specific formula is as follows:

[0092]

[0093] Among them, m is the termination convergence factor, k is the exponent that controls the decay rate, and Max_iter is the maximum number of iterations.

[0094] When the random number is ≥ 0.5 and the absolute value of the coefficient vector is > 1, a random search mechanism is used to update the position. The specific formula is as follows:

[0095] D=|C·X rand -X|

[0096] X(t+1)=X rand -A.D

[0097] Among them, X rand It represents the position of a random point.

[0098] When the random number is less than 0.5, the spiral search mechanism is used to update the position. The specific formula is as follows:

[0099] X(t+1)=D′·e bl ·cos(2πl)+X * (t)

[0100] D′=|X * (t)-X(t)|

[0101] Among them, b is the empirical constant of the spiral equation, l is a random number between [-1,1], and D′ represents the distance between the individual’s current position and the optimal position.

[0102] For ease of understanding, the implementation process and technical effects of the positioning method provided by the present invention are briefly described below using a specific usage scenario as an example.

[0103] The embodiment of the present invention provides a composite material acoustic emission source positioning method based on an improved whale optimization algorithm, which can be used in the field of non-destructive testing of composite materials. In view of the problem that the anisotropy of composite materials causes the speed of sound wave propagation to be sensitive to direction, the present invention introduces the whale optimization algorithm. The whale optimization algorithm is a swarm intelligence optimization algorithm, which is modeled by simulating the predation behavior of humpback whale groups in nature. The algorithm mainly includes three parts: encirclement contraction, spiral contraction and random search.

[0104] (1) Surrounding contraction

[0105] Encirclement and contraction is to regard each whale in the group as a potential solution, that is, the position of each individual in the search space corresponds to a possible solution, and the entire group will approach the position of the currently identified optimal individual to achieve the behavior of encircling and surrounding the prey. This process can be expressed by the following formula:

[0106] D=|C·X * (t)-X(t)|

[0107] X(t+1)=X * (t)-A·D

[0108] In the formula, t represents the current iteration number, X * is the optimal position of the tth iteration, X is the position vector. A and C are coefficient vectors, and the formula is expressed as:

[0109] A=2a·ra

[0110] C=2·r

[0111] Where r is a random number between [0,1]. In the original whale optimization algorithm, the convergence factor a decreases linearly from 2 to 0 during the iteration process. In order to balance the global search and local development capabilities of the algorithm, the present invention introduces a nonlinear convergence factor. The specific formula is as follows:

[0112]

[0113] Where t represents the current number of iterations, Max_iter is the maximum number of iterations, k is the exponent that controls the decay rate, and m is the termination convergence factor.

[0114] (2) Spiral contraction

[0115] Spiral contraction is used to simulate the behavior of whales sending out spiral bubble nets to hunt. This method is to establish a spiral equation between each individual whale and the current optimal point, and its position update is performed by the following formula:

[0116] X(t+1)=D′·e bl ·cos(2πl)+X * (t)

[0117] D′=|X * (t)-X(t)|

[0118] Where b is the empirical constant of the spiral equation, l is a random number between [-1,1], and D′ represents the distance between the individual’s current position and the optimal position.

[0119] In each iteration, in order to more realistically simulate the humpback whale's predation behavior, the algorithm adopts two position update methods, each with a probability of 50%. The mathematical model of this process can be expressed as:

[0120]

[0121] Where p is a random number between [0,1].

[0122] (3) Random Search

[0123] The update method of the random search phase is similar to the surrounding contraction phase. The difference is that the coefficient vector |A|>1, and the position update is no longer based on the current best position, but is replaced by an arbitrary random point, which can be expressed by the following formula:

[0124] D=|C·X rand -X|

[0125] X(t+1)=X rand -A.D

[0126] Where, X rand It represents the position of a random point.

[0127] Figure 5 The schematic diagram of the acoustic emission source positioning system is shown. A carbon fiber composite laminate is used as the experimental object. The size of the plate is 700mm×700mm×3mm, and the fiber ply sequence is [0 / 45 / 90 / -45]4, with a total of 16 layers. To facilitate the description of the spatial position of the sound source and sensor, the geometric center of the material is taken as the coordinate origin, and a Cartesian rectangular coordinate system is established with the horizontal direction as the x-axis and the vertical direction as the y-axis. The coordinates of the four sensors are S1 (200, 200), S2 (-200, 200), S3 (-200, -200), and S4 (200, -200), respectively, in mm.

[0128] A signal generator is used to excite Sink pulses to simulate a real acoustic emission source. The arrival time of the signals received by the four sensors is extracted and input into the invented improved whale optimization algorithm. Table 1 shows the positioning results of each acoustic emission source. It can be seen from Table 1 that the method provided by the present invention achieves excellent positioning accuracy, with an average positioning error of 1.35%.

[0129] Table 1 Location results of various acoustic emission sources

[0130]

[0131]

[0132] In the above specific embodiments, the composite material acoustic emission source positioning method provided by the present invention extracts the arrival time of the sound wave signal to each acoustic emission sensor respectively, and calculates the arrival time difference between every two acoustic emission sensors; takes the arrival time difference as a known condition, takes the coordinates of the acoustic emission source as the optimization variable, and combines the sound velocity characteristics of the pre-calibrated composite material to establish an optimization model with the root mean square error of the theoretical arrival time difference and the actual arrival time difference as the objective function; improves the original whale optimization algorithm using a nonlinear convergence factor and a random initialization strategy, and uses the improved whale optimization algorithm to iteratively solve the optimization model. When the preset iteration termination condition is met, the optimal solution output by the optimization model can be used as the positioning result.

[0133] In this way, when locating the acoustic emission source of the composite material, the present invention takes into account the influence of the direction of the sound wave propagation speed caused by anisotropy, converts the acoustic emission source positioning problem into an optimization problem, takes the arrival time difference extracted by different sensors as a known condition, takes the coordinates of the acoustic emission source as the optimization variable, constructs the objective function, and uses the improved whale optimization algorithm for iterative solution to finally obtain the position of the acoustic emission source; solves the problem that the existing composite material source positioning method is high in cost and cannot meet the positioning accuracy requirements, thereby improving the positioning accuracy of the composite material source and reducing its positioning difficulty.

[0134] In addition to the above method, the present invention also provides a composite material acoustic emission source positioning device for implementing the above method, such as Figure 7 As shown, the device comprises:

[0135] The time calculation unit 710 is used to extract the arrival time of the sound wave signal to each acoustic emission sensor, and calculate the arrival time difference between every two acoustic emission sensors;

[0136] The model building unit 720 is used to take the arrival time difference as a known condition, take the coordinates of the acoustic emission source as an optimization variable, and combine the sound velocity characteristics of the composite material calibrated in advance to establish an optimization model with the root mean square error between the theoretical arrival time difference and the actual arrival time difference as the objective function;

[0137] The result generating unit 730 is used to improve the original whale optimization algorithm by using a nonlinear convergence factor and a random initialization strategy, and to iteratively solve the optimization model by using the improved whale optimization algorithm. When a preset iteration termination condition is met, the optimal solution output by the optimization model is used as the positioning result.

[0138] In some embodiments, pre-calibrating the acoustic velocity characteristics of the composite material specifically includes:

[0139] Calibrate the acoustic velocity of the acoustic emission signal propagation characteristics of the composite material to be tested, and obtain the relationship between the acoustic wave propagation velocity and angle;

[0140] The obtained sound wave propagation velocity and angle are fitted to obtain a velocity-angle fitting polynomial.

[0141] In some embodiments, extracting the arrival time of the sound wave signal at each acoustic emission sensor specifically includes:

[0142] The original signal collected by the acoustic emission sensor is divided into time windows, wherein the signal in the time window at least includes the first arrival mode;

[0143] Calculate the envelope signal of the original signal in the time window;

[0144] An AIC function is executed on the envelope signal, and the time corresponding to the minimum value of the AIC function is used as the arrival time of the signal.

[0145] In some embodiments, the original whale optimization algorithm is improved by using a nonlinear convergence factor and a random initialization strategy, specifically including:

[0146] Initialize the relevant parameters of the original whale optimization algorithm and randomly generate the position of each individual whale; the relevant parameters at least include the population size N, the maximum number of iterations and the search range;

[0147] Taking the arrival time difference as the theoretical arrival time difference, calculating the fitness value of each individual whale based on the speed-angle fitting polynomial and the objective function, and determining the individual whale and its position with the minimum current error;

[0148] Location updates are made based on a randomly generated nonce.

[0149] In some embodiments, performing a location update based on a randomly generated random number specifically includes:

[0150] When the random number is ≥ 0.5 and the absolute value of the coefficient vector is ≤ 1, the surrounding contraction mechanism is used to update the position;

[0151] When the random number is ≥ 0.5 and the absolute value of the coefficient vector is > 1, a random search mechanism is used to update the position;

[0152] When the random number is less than 0.5, a spiral search mechanism is used to update the position.

[0153] In some embodiments, the preset iteration termination condition is:

[0154] The current number of iterations is equal to the maximum number of iterations.

[0155] In the above specific embodiments, the composite acoustic emission source positioning device provided by the present invention extracts the arrival time of the sound wave signal to each acoustic emission sensor respectively, and calculates the arrival time difference between every two acoustic emission sensors; takes the arrival time difference as a known condition, takes the coordinates of the acoustic emission source as the optimization variable, and combines the sound velocity characteristics of the pre-calibrated composite material to establish an optimization model with the root mean square error of the theoretical arrival time difference and the actual arrival time difference as the objective function; improves the original whale optimization algorithm using a nonlinear convergence factor and a random initialization strategy, and uses the improved whale optimization algorithm to iteratively solve the optimization model. When the preset iteration termination condition is met, the optimal solution output by the optimization model can be used as the positioning result.

[0156] In this way, when locating the acoustic emission source of the composite material, the present invention takes into account the influence of the direction of the sound wave propagation speed caused by anisotropy, converts the acoustic emission source positioning problem into an optimization problem, takes the arrival time difference extracted by different sensors as a known condition, takes the coordinates of the acoustic emission source as the optimization variable, constructs the objective function, and uses the improved whale optimization algorithm for iterative solution to finally obtain the position of the acoustic emission source; solves the problem that the existing composite material source positioning method is high in cost and cannot meet the positioning accuracy requirements, thereby improving the positioning accuracy of the composite material source and reducing its positioning difficulty.

[0157] Figure 8 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 8 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the above method.

[0158] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the part of the technical solution of the present invention that essentially contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instruction sets that enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0159] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the above method.

[0160] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0161] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A composite material acoustic emission source positioning system, characterized in that: The system comprises: A signal generator, which is disposed on the surface of the composite material to be tested and is used to excite sound waves; Acoustic emission sensors, at least two of which are spaced apart and distributed on the surface of the composite material to be tested; the signal generator is connected to the acoustic emission sensors via a BNC cable, and the acoustic emission sensors are used to receive the sound waves excited by the signal transmitter; A signal amplifier, wherein the acoustic emission sensor is connected to the signal amplifier via a BNC cable; Acoustic emission signal collection equipment, the signal amplifier is connected to the acoustic emission signal collection equipment via a BNC cable; The intelligent terminal is connected to the acoustic emission signal acquisition device through a BNC cable, and the intelligent terminal is used to display and record the results of the signals collected by the acoustic emission signal acquisition device.

2. A composite material acoustic emission source positioning method, based on the positioning system according to claim 1, characterized in that: The method comprises: The arrival time of the sound wave signal at each acoustic emission sensor is extracted respectively, and the arrival time difference between every two acoustic emission sensors is calculated; Taking the arrival time difference as a known condition and the coordinates of the acoustic emission source as the optimization variable, combined with the pre-calibrated sound velocity characteristics of the composite material, an optimization model is established with the root mean square error between the theoretical arrival time difference and the actual arrival time difference as the objective function. The original whale optimization algorithm is improved by using nonlinear convergence factors and random initialization strategies, and the improved whale optimization algorithm is used to iteratively solve the optimization model. When the preset iteration termination condition is met, the optimal solution output by the optimization model is used as the positioning result.

3. The composite material acoustic emission source positioning method according to claim 2, characterized in that: Pre-calibrate the acoustic velocity characteristics of composite materials, including: Calibrate the acoustic velocity of the acoustic emission signal propagation characteristics of the composite material to be tested, and obtain the relationship between the acoustic wave propagation velocity and angle; The obtained sound wave propagation velocity and angle are fitted to obtain a velocity-angle fitting polynomial.

4. The composite material acoustic emission source positioning method according to claim 2, characterized in that: Extract the arrival time of the sound wave signal to each acoustic emission sensor, specifically including: The original signal collected by the acoustic emission sensor is divided into time windows, wherein the signal in the time window at least includes the first arrival mode; Calculate the envelope signal of the original signal in the time window; An AIC function is executed on the envelope signal, and the time corresponding to the minimum value of the AIC function is used as the arrival time of the signal.

5. The composite material acoustic emission source positioning method according to claim 2, characterized in that: The original whale optimization algorithm is improved by using nonlinear convergence factors and random initialization strategies, including: Initialize the relevant parameters of the original whale optimization algorithm and randomly generate the position of each individual whale; the relevant parameters at least include the population size N, the maximum number of iterations and the search range; Taking the arrival time difference as the theoretical arrival time difference, calculating the fitness value of each individual whale based on the speed-angle fitting polynomial and the objective function, and determining the individual whale and its position with the minimum current error; Location updates are made based on a randomly generated nonce.

6. The composite material acoustic emission source positioning method according to claim 5, characterized in that: The location update is based on a randomly generated random number, including: When the random number is ≥ 0.5 and the absolute value of the coefficient vector is ≤ 1, the surrounding contraction mechanism is used to update the position; When the random number is ≥ 0.5 and the absolute value of the coefficient vector is > 1, a random search mechanism is used to update the position; When the random number is less than 0.5, a spiral search mechanism is used to update the position.

7. The composite material acoustic emission source positioning method according to claim 5, characterized in that: The preset iteration termination condition is: The current number of iterations is equal to the maximum number of iterations.

8. A composite material acoustic emission source positioning device, used to implement the method according to any one of claims 2 to 7, the device comprising: A time calculation unit, used to extract the arrival time of the sound wave signal to each acoustic emission sensor, and calculate the arrival time difference between every two acoustic emission sensors; A model building unit is used to take the arrival time difference as a known condition, take the coordinates of the acoustic emission source as an optimization variable, and combine the sound velocity characteristics of the composite material calibrated in advance to establish an optimization model with the root mean square error between the theoretical arrival time difference and the actual arrival time difference as the objective function; The result generating unit is used to improve the original whale optimization algorithm by using a nonlinear convergence factor and a random initialization strategy, and adopt the improved whale optimization algorithm to iteratively solve the optimization model. When a preset iteration termination condition is met, the optimal solution output by the optimization model is used as the positioning result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 2 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 2 to 7 is implemented.

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