Composite material low-speed impact positioning method and device, electronic equipment and time difference positioning algorithm

Abstract

The application relates to the field of structural health monitoring, and discloses a composite material low-speed impact positioning method and device, electronic equipment and a time difference positioning algorithm. The composite material low-speed impact positioning method comprises the following steps: synchronously collecting multi-channel acoustic emission signals generated after a composite material is impacted to obtain multi-channel digital signals; processing the digital signals of each channel to extract envelope signals corresponding to each channel; extracting corresponding wave arrival times from the envelope signals of each channel based on an adaptive wave arrival time extraction method; and determining the position coordinates of an impact point by using a four-point circular arc time difference positioning algorithm based on the known coordinates of each channel sensor, the propagation speed of sound waves in the composite material and the wave arrival times of each channel. The application effectively solves the technical problem of inaccurate wave arrival time extraction caused by signal distortion and noise interference, and realizes high-precision and high-robustness positioning of a composite material low-speed impact point.

Description

Technical Field

[0001] This invention relates to the field of structural health monitoring, specifically to a method and device for locating composite material under low-speed impact, electronic equipment, and time-difference positioning algorithm. Background Technology

[0002] Composite materials have been widely used in aerospace and other fields due to their excellent specific strength and specific modulus. However, composite structures are vulnerable to low-velocity impacts (such as tool drops, bird strikes, hail, etc.) during service, which may cause internal damage and lead to structural performance degradation. Therefore, real-time and accurate impact monitoring of composite structures is crucial.

[0003] Acoustic emission (AE) technology is one of the effective means to achieve online monitoring of structural impact. Currently, there are two main types of AE data acquisition systems on the market: general-purpose and specialized. The former has a wide range of applications and comprehensive functions, but it is bulky, energy-intensive, and complex to deploy, making it unsuitable for high-speed, lightweight aircraft with limited energy resources. The latter is generally designed for specific materials or fields, requiring specialized design, and is not suitable for aircraft with complex structures.

[0004] Acoustic emission localization methods based on time-of-arrival (TOA) acquire elastic wave signals generated by impact using multiple sensors and estimate the impact source location by utilizing the differences in TOA among these signals. However, in composite material structures, due to material anisotropy, structural complexity, and environmental noise interference, the acoustic emission signals generated by impact undergo severe waveform distortion and attenuation during propagation. This makes it extremely difficult to accurately and reliably extract the TOA from the original signal. Existing methods often rely on preset fixed thresholds or cross-correlation analysis to identify the TOA, but these methods are susceptible to noise interference when the signal-to-noise ratio is low or the signal characteristics are not significant, leading to inaccurate TOA extraction and ultimately resulting in significant localization errors.

[0005] Therefore, improving the accuracy and robustness of wave arrival time extraction under complex working conditions of composite materials is a key technical challenge for achieving high-precision impact positioning. Summary of the Invention

[0006] The main objective of this invention is to provide a method and apparatus for low-velocity impact positioning of composite materials, in order to solve the technical problems of inaccurate time of arrival extraction and low positioning accuracy caused by signal distortion and noise interference in the existing composite material impact monitoring, and to achieve high-precision and high-robust low-velocity impact positioning of composite materials.

[0007] A method for positioning composite materials under low-velocity impact, characterized in that it includes: Simultaneously acquire multi-channel acoustic emission signals generated by composite materials after impact to obtain multi-channel digital signals; The digital signals of each channel are processed to extract the envelope signal corresponding to each channel; Based on the adaptive time-of-arrival extraction method, the corresponding time of arrival is extracted from the envelope signal of each channel; Based on the known coordinates of each channel sensor, the propagation speed of sound waves in the composite material, and the arrival time of each channel, the position coordinates of the impact point are determined using a four-point circular arc time difference positioning algorithm.

[0008] Furthermore, the synchronous acquisition of multi-channel acoustic emission signals generated by the composite material after impact includes: Simulated acoustic emission signals generated in composite materials after impact are collected synchronously using multiple acoustic emission sensors. The acoustic emission analog signal is amplified and filtered; The processed analog signal is converted into a multi-channel digital signal and uploaded.

[0009] Furthermore, the processing of the digital signals of each channel to extract the envelope signal corresponding to each channel includes: The digital signal of each channel is subjected to sliding windowing to reduce spectral leakage and then detrending to obtain a detrended signal; Empirical mode decomposition is performed on the detrended signal to extract the first intrinsic mode function component; The Hilbert transform is performed on the first intrinsic mode function component to obtain the corresponding envelope signal.

[0010] Furthermore, the detrending process specifically involves removing the linear trend term of the signal by subtracting the mean amplitude of the signal from the original signal.

[0011] Furthermore, the adaptive time-of-arrival extraction method extracts the corresponding time of arrival from the envelope signal of each channel. Specifically, for the envelope signal of each channel, the time of arrival of that channel is determined based on the minimum point of the Akaike information criterion function.

[0012] Furthermore, the Akaike information criterion function The expression is: ; in The envelope signal sequence is a single-channel signal. For signal length, For signal segmentation point, This represents variance calculation, where the time of arrival corresponds to the... When the function reaches its minimum value value.

[0013] Furthermore, the determination of the impact point's location coordinates using a four-point circular arc time difference positioning algorithm, based on the known coordinates of each channel sensor, the propagation speed of sound waves in the composite material, and the arrival time of each channel, includes: Establish a set of distance difference equations based on the time of arrival; The position coordinates of the impact point are obtained by solving the distance difference equations using the least squares method.

[0014] Furthermore, the distance difference equations are as follows: in, Let be the coordinates of the impact point to be determined. For the first The known coordinates of each sensor, The speed at which sound waves propagate in composite materials. For the first The arrival time corresponding to each sensor. The moment the impact occurs.

[0015] Furthermore, the synchronous acquisition is triggered by an external trigger signal.

[0016] Furthermore, the multi-channel acoustic emission signal is a four-channel acoustic emission signal.

[0017] A low-speed impact positioning device for composite materials, comprising: The signal acquisition hardware module is used to synchronously acquire multi-channel acoustic emission signals generated by composite materials after impact, and obtain multi-channel digital signals. The processing module is used to process the digital signals of each channel and extract the envelope signal corresponding to each channel; The time of arrival extraction module is used to extract the corresponding time of arrival from the envelope signal of each channel based on the adaptive time of arrival extraction method. The positioning module is used to determine the position coordinates of the impact point based on the known coordinates of each channel sensor, the propagation speed of sound waves in the composite material, and the wave arrival time of each channel, using a four-point circular arc time difference positioning algorithm.

[0018] Furthermore, the signal acquisition hardware module includes: The sensor unit is used to synchronously acquire simulated acoustic emission signals generated in the composite material after impact through multiple acoustic emission sensors; The signal conditioning unit is used to amplify and filter the acoustic emission analog signal; The analog-to-digital converter unit is used to convert the processed analog signal into a multi-channel digital signal.

[0019] Furthermore, the processing module includes: The detrending unit is used to detrend the digital signal of each channel by subtracting the mean amplitude, so as to obtain a detrended signal. The decomposition unit is used to perform empirical mode decomposition on the detrended signal and extract the first intrinsic mode function component. The envelope extraction unit is used to perform Hilbert transform on the first intrinsic mode function components to obtain the corresponding envelope signal.

[0020] An electronic device includes: a data acquisition unit, a memory, a transmitter, and a controller. The data acquisition unit acquires analog signals, the memory stores data and functional programs, the transmitter performs wireless communication with a host computer, and the controller is responsible for running the functional programs to drive three other modules. When the electronic device executes the programs, it implements the method for acquiring low-speed impact signals of composite materials.

[0021] A positioning algorithm is provided, which is stored and run by a host computer. When the algorithm is executed by the host computer, it implements the low-speed impact positioning method for composite materials.

[0022] Compared with the prior art, the beneficial effects of the present invention are: This invention ensures time reference consistency for multi-channel signals through synchronous acquisition, improves the signal-to-noise ratio (SNR) through a pre-processing unit, reduces spectral aliasing, suppresses common-mode interference, and enhances signal acquisition accuracy. By performing sliding windowing, detrending processing, and empirical mode decomposition on the original digital signal, baseline disturbances caused by system drift and environmental temperature changes can be effectively suppressed, and the dispersion effect of the signal propagating in composite materials can be compensated, thereby separating the first eigenmode function component that best characterizes the impact source. Furthermore, the envelope signal is extracted using Hilbert transform, significantly enhancing the abrupt change characteristics of the signal arrival time. Based on this, an adaptive time-of-arrival (TOA) extraction method based on the Akaike information criterion is adopted. This method automatically determines the TOA based on changes in signal statistical characteristics, without the need for a preset fixed threshold, thus effectively overcoming the poor robustness of traditional threshold methods when the SNR changes. It can more accurately capture the initial arrival point from the envelope signal affected by noise interference and waveform distortion. Finally, using a four-point circular arc time difference positioning algorithm, a set of distance difference equations is established based on redundant TOA information provided by four sensors, and the impact point location is solved using the least squares method. This algorithm can, to a certain extent, suppress the influence of wave velocity uncertainty and measurement errors of individual sensors. The above steps are interconnected and work together to effectively solve the technical problem of inaccurate wave arrival time extraction caused by signal distortion and noise interference in the background technology, and finally achieve high-precision and robust positioning of low-velocity impact points of composite materials. Attached Figure Description

[0023] Figure 1This is a schematic diagram of the monitoring system of the present invention; Figure 2 A schematic diagram of the installation of the acoustic emission sensor and monitoring system; Figure 3 This is a schematic diagram of a ball-dropping test platform. Figure 4 A flowchart of the monitoring system's workflow; Figure 5 This is a diagram illustrating the positioning effect of low-speed impact. The labels in the diagram are as follows: 1 is the acoustic emission sensor, 2 is the wing, 3 is the signal line, 4 is the wireless high-speed data acquisition card, 5 is the wireless transmission unit, and 6 is the host computer. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The method provided by this invention is mainly applied to health monitoring scenarios of composite material structures in aerospace, wind turbine blades, and other fields. In such scenarios, the structure may be subjected to low-speed impacts such as tool drops, bird strikes, and hail, requiring a method that can accurately pinpoint the impact location online in order to promptly assess damage and formulate maintenance strategies. Figure 4 A schematic diagram of an implementation process for a low-velocity impact positioning method for composite materials is shown.

[0026] like Figure 4 As shown, an embodiment of the present invention provides a method for positioning composite materials under low-velocity impact, the method comprising the following steps: Simultaneously acquire multi-channel acoustic emission signals generated by composite materials after impact to obtain multi-channel digital signals; The digital signals of each channel are processed to extract the envelope signal corresponding to each channel; Based on the adaptive time-of-arrival extraction method, the corresponding time of arrival is extracted from the envelope signal of each channel; Based on the known coordinates of each channel sensor, the propagation speed of sound waves in the composite material, and the arrival time of each channel, the position coordinates of the impact point are determined using a four-point circular arc time difference positioning algorithm.

[0027] This method enhances arrival characteristics through a series of hardware signal conditioning and digital signal preprocessing steps, and adopts an adaptive time-of-arrival extraction strategy. It effectively overcomes the problem of difficult time-of-arrival extraction caused by severe acoustic emission signal distortion and low signal-to-noise ratio in composite materials, and finally achieves high-precision impact point localization.

[0028] In this embodiment, the synchronous acquisition of multi-channel acoustic emission signals generated by the composite material after impact can be specifically performed as follows: synchronously acquiring the acoustic emission analog signals generated in the composite material after impact through multiple acoustic emission sensors; amplifying and filtering the acoustic emission analog signals; and converting the processed analog signals into multi-channel digital signals.

[0029] Specifically, multiple acoustic emission sensors (e.g., resonant sensors with a center frequency of 40 kHz) are attached to the surface of the composite material specimen and arranged in an axially symmetrical manner to cover the monitoring area. When an impact occurs, the sensors convert the detected mechanical vibration into a weak electrical signal (i.e., an acoustic emission analog signal). This signal is first amplified by a preamplifier circuit (e.g., 40 dB gain), and then filtered by a bandpass filter (e.g., 50 kHz - 300 kHz) to remove out-of-band noise and improve the signal-to-noise ratio. Considering the input range of the data acquisition card, the amplified and filtered signal also needs to pass through an attenuation bias circuit composed of a precision operational amplifier (e.g., LMC6482) to adjust the signal to a suitable voltage range (e.g., 0 V to +5 V). Finally, a high-speed analog-to-digital converter (e.g., AD9220) is used to synchronously sample the conditioned analog signal and convert it into a multi-channel digital signal for computer processing. The sampling rate must at least satisfy the Nyquist sampling theorem, for example, not less than 800 kHz, and the resolution must be not less than 10 bits. Preferably, synchronous acquisition is triggered by an external trigger signal, such as the falling edge when the suction cup switch is disconnected in a ball-dropping experiment. The acquisition delay is precisely set by calculating the free fall time to ensure that the acoustic emission signal segment generated by the impact can be fully captured. Furthermore, the multi-channel acoustic emission signal is preferably a four-channel acoustic emission signal to improve positioning accuracy using a four-point circular arc time difference positioning algorithm.

[0030] Figure 3A schematic diagram of the falling ball test platform is shown. The resonant acoustic emission sensor used in this experiment has a center frequency of 40kHz and a frequency range of 50kHz to 400kHz, which can basically cover the frequency of all damage signals induced by low-speed impact. The preamplifier is located between the acoustic emission sensor and the data acquisition card, and can directly affect the processing of the acoustic signal by the subsequent filtering and biasing circuits, preventing the signal from being overwhelmed by noise. The filtering section can filter out circuit switching noise and electromagnetic radiation noise, allowing signals within a limited frequency band to pass through, thereby improving the signal-to-noise ratio. The signal biasing section adjusts the amplified and filtered signal to match the input range of the data acquisition card. Since the acoustic emission sensor generates a small signal, direct data acquisition may affect the acquisition accuracy. A 40dB gain preamplifier circuit is selected, and the amplified signal amplitude is -10V to +10V. Considering that the main acquisition range of the sensor is 50kHz to 400kHz, and most of the noise is in the low-frequency range, a bandpass filter with a cutoff frequency of 50kHz to 300kHz is finally selected.

[0031] Based on the above design conditions and the input range of the data acquisition card, the signal adjusted by the preamplifier circuit still needs to be attenuated and biased to 0V to +5V. Considering the airborne operating conditions of the system, an operational amplifier with ultra-low bias current and a precision CMOS rail-to-rail output swing within 20mV is selected to design the signal bias circuit LMC6482. Its attenuation bias equation is: ; in It is the signal after amplification and filtering. for , for It is a voltage divider resistor. After the voltage is divided by the resistor, the signal amplitude is -2.5V to +2.5V. However, the input range of the data acquisition card is greater than 0V, so a bias voltage of 2.5V is also required. This is the output signal after attenuation and biasing. The frequency range of the acoustic signal is less than 400kHz. According to the Nyquist theorem, the sampling frequency must be at least 800kHz, and based on the sensitivity of the sensor, a resolution of at least 10 bits is selected. The AD9220 is selected as the analog-to-digital converter module to convert the above signal-conditioned analog signal into a computer-recognizable digital signal.

[0032] ; in It is an analog input. It is the minimum analog input (0V). It is the maximum analog input (+5V). It outputs digital signals. The maximum value of the output digital signal is 4095. It is the minimum value of the output digital signal, 0.

[0033] In time-of-flight acoustic emission impact localization, at least three channels of sensor signals are required to estimate the location of a single impact point. For ease of subsequent analysis, we opt for time-of-flight localization based on four sensors to improve accuracy. A single impact event, from the generation of the elastic wave to its propagation to the sensor and the end of the signal transmission, takes approximately 2 ms. Based on the sampling rate and acquisition accuracy mentioned above, each impact generates approximately 400 Kbit of data.

[0034] ; n is the number of sampling points, and N is the number of channels (4). The sampling rate is 2MHz. The sampling time is 4ms.

[0035] To enable the Bluetooth Low Energy module and synchronously drive the four-channel analog-to-digital converter module, the STM32F103 was selected as the control module. Multiple timers were used for synchronous triggering, with TIM1 acting as the master timer to generate a PWM wave as the clock signal for the AD9220. The 12-bit output of the AD9220 was connected to the same GPIO port. Compared to using the CPU to store data, DMA copies data from the peripheral address space to the memory address space, providing high-speed data transfer between peripherals and memory without CPU processing, and can simultaneously achieve a 2MHz sampling rate and high storage efficiency for all four channels.

[0036] The master clock frequency is 72MHz. The prescaler coefficient is 6. The automatic reload cycle is 6. The final sampling rate is 2MHz.

[0037] The wireless transmission unit is used to connect the terminal and the host computer, and to package and upload digital signals to the host computer.

[0038] Comparing the transmission rate, latency, interference resistance, networking conditions, and power consumption of different wireless transmission methods, Bluetooth Low Energy (BLE) was selected as the data transmission method. It uses the 2.4GHz ISM band, has an uplink rate of up to 2Mb / s, supports both broadcast and observer modes, and has a simultaneous transmit power of approximately 100mW. After the data storage is complete, the terminal's Bluetooth is activated, and after confirming the connection to the host computer's Bluetooth, four channels of data are uploaded sequentially. After the transmission is complete, the device enters sleep mode.

[0039] A ball drop test bench was built, which is a three-axis adjustable low-speed impact ball drop test bench. A wireless data acquisition card was used to collect time-domain impact signals at different ball drop heights and positions. This experiment utilizes the aforementioned data acquisition card to obtain acoustic emission data from the ball-dropping experiment. The data acquisition card has a maximum sampling rate of 10MHz, a sampling precision of 12 bits, and 4 channels. The ball-dropping experimental platform consists of an XYZ three-axis adjustable support, three types of balls with masses of 50g, 100g, and 200g, a 400×400×5mm carbon fiber composite board, an electric suction cup, a wooden pad, and clamps. The XYZ motion support can adjust the falling position and height of the ball to achieve impact positioning at different impact points. When the falling height remains constant, using balls of different masses to conduct impact tests can generate different impact forces, which can be used for random impact force positioning experiments. A 3×3 grid is drawn on the 40×40cm carbon fiber board, with each cell size of 7×7cm. The intersection of each grid line serves as the impact positioning point. The ball is fixed using an electric suction cup. When the suction cup switch is pressed, the ball falls, completing one impact test. Ten impact tests are randomly performed at each impact point for each of the three masses. In this experiment, the falling edge of the suction cup switch being disconnected was used as the external contact signal of the data acquisition card. Through calculation and debugging, it was determined that when the ball falls from a height of 51cm, the delay of the acquisition card should be set to 316ms in order to capture the arrival time of the sound signal within a 4ms sampling period.

[0040]

[0041] Let be the time of free fall of the ball, h be the height of fall, and g be the acceleration due to gravity.

[0042] In actual ball-drop tests, wooden pads are needed to isolate the composite material plate from the ground. This reduces environmental noise interference and acoustic signal leakage. When installing the sensors, coupling agent is applied to prevent signal leakage, and the sensors are fixed to the test plate using clamps before being placed flat on the wooden pads. The sensors are installed symmetrically along the axis to cover the entire monitoring area. Compared to four-point diagonal installation or single-sided arrangement, this method has the fewest monitoring blind spots.

[0043] After the terminal sends the signal to the host computer, the host computer receives and saves the signal.

[0044] In this embodiment, the digital signals of each channel are processed to extract the envelope signals corresponding to each channel. Specifically, this can be performed as follows: sliding window and detrending processing is applied to the digital signals of each channel to obtain a detrended signal; empirical mode decomposition is performed on the detrended signal to extract the first intrinsic mode function component; and Hilbert transform is performed on the first intrinsic mode function component to obtain the corresponding envelope signal.

[0045] Specifically, sliding window and detrending processing reduce the spectral leakage of the signal by using Hamming window sliding window technology and remove the linear trend term of the signal by subtracting the mean of the signal amplitude.

[0046] The processor uses a sliding window to multiply the signal point-by-point with a Hamming window function, smoothly transitioning both ends to zero, thereby suppressing spectral leakage and improving frequency resolution accuracy. Then, it calculates the mean of all sampled points and subtracts it from each point to make the signal mean zero, effectively eliminating low-frequency baseline disturbances caused by test system drift or ambient temperature changes, facilitating subsequent analysis. For the detrended signal, the processor calls an empirical mode decomposition algorithm to adaptively decompose the signal into a series of intrinsic mode function (EMF) components from high to low frequencies. Since the main damage characteristics of the impact are concentrated in the high-frequency band, the first EMF component, with the highest energy proportion, retains complete time-of-arrival (TOA) information and is closest to the original waveform characteristics. Finally, the processor performs a Hilbert transform on the first EMF component to calculate its analytic signal magnitude, thus obtaining the envelope of the channel signal. This envelope signal highlights the abrupt changes in signal amplitude, laying the foundation for accurate TOA extraction.

[0047] To address the need for locating acoustic emission sources in composite material structures, the series of operations described above, including sliding window detrending, empirical mode decomposition (EMD), and envelope extraction, not only effectively suppress background noise but also fully consider the attenuation characteristics and dispersion effects of acoustic emission signals propagating in composite materials, providing high-precision input data for subsequent time-of-arrival-based damage localization.

[0048] In this embodiment, the corresponding arrival time is extracted from the envelope signal of each channel based on the adaptive time of arrival extraction method. Specifically, for the envelope signal of each channel, the arrival time of the channel is determined based on the minimum point of the Akaike information criterion function.

[0049] This paper presents a combined adaptive time-of-arrival (TOA) extraction and four-point circular arc positioning method based on the Akaike information criterion (AIC). Traditional TOA extraction methods rely on fixed threshold detection or cross-correlation analysis, but multiple scattering within composite materials and environmental interference make it difficult for fixed threshold methods to accurately capture the initial point of arrival. In such cases, directly using the TOA obtained by traditional methods is not the optimal choice for accurate impact positioning. To address this issue, an AIC adaptive parameter extraction method is used to extract the TOA, and a four-point circular arc positioning algorithm is then employed to achieve high-precision impact location.

[0050] The Acoustic Interaction (AIC) criterion primarily utilizes the characteristic differences between noise and signal to automatically determine the time of arrival (TOA) based on the local statistical transform of the signal. By calculating the minimum value of the AIC function within a sliding time window, the first arrival time of the acoustic emission signal in each sensor channel is obtained. This eliminates the need for preset thresholds, effectively adapting to acoustic emission signals under different signal-to-noise ratios. The AIC function is defined as follows: ; in and The signals at the segmentation points are respectively Subsequences before and after, Let k be the signal length, k be the signal segmentation point, and var(·) represent variance calculation. The minimum point of this function corresponds to the moment when the statistical characteristics of the signal change most drastically, i.e., the time of arrival.

[0051] The principle behind this method is that the AIC function measures the difference in statistical characteristics (expressed as the logarithm of variance) between two subsequences before and after the segment point k to find the location where the signal's statistical characteristics change abruptly. For envelope signals, the area before and after the point of arrival typically corresponds to a noise segment and a signal segment, respectively, with significant differences in their statistical characteristics (variance). The processor iterates through the values ​​of k and calculates the corresponding AIC(k), finding the k that minimizes the function value. This point is considered the one with the most drastic change in statistical characteristics and is thus the most probable time of arrival (TOA) point. This method does not require a preset fixed threshold and can adapt to signals under different signal-to-noise ratio conditions, thereby obtaining a more stable and reliable TOA estimate.

[0052] In this embodiment, based on the known coordinates of each channel sensor, the propagation speed of sound waves in the composite material, and the arrival time of each channel, the position coordinates of the impact point are determined using a four-point circular arc time difference positioning algorithm. Specifically, this can be achieved by: establishing a set of distance difference equations based on the arrival time; and solving the set of distance difference equations using the least squares method to obtain the position coordinates of the impact point.

[0053] Four-point circular arc positioning utilizes the arrival time differences of multiple sensors to construct a redundant set of equations. This method determines the location of the sound source by establishing a set of distance difference equations. Specifically, let the coordinates of the four sensors be... The speed of sound wave propagation in composite materials is This can be obtained through online calibration or anisotropic wave velocity models. The impact point... Satisfying the system of equations ; in The moment of impact. Eliminating the equations by subtracting each other pairwise. , get about The linear equations were solved using the least squares method. The above four-point circular arc positioning method can effectively suppress the influence of wave velocity uncertainty and measurement noise, and the analysis of a single impact event only takes 0.52s.

[0054] like Figure 5 The figure shows the impact location results under three random impact forces: 0.25J, 0.5J, and 1J. When the impact energy is 0.25J, the average location error across 16 points is 2.96cm, with the largest error at P3-1 (4.96cm) and the smallest at P3-2 (approximately 0.51cm). Under 0.5J impact energy, the average error is 2.31cm, with the smallest error at P2-2 (approximately 0.30cm) and the largest error at P1-3 (approximately 4.51cm). For the 1J impact event, the smallest average error is 2.02cm, with the largest error at P4-4 (approximately 4.95cm) and the smallest error at P3-2 (approximately 0.03cm). Under the unknown impact force, the smallest average location error is at P2-3 (approximately 0.55cm), and the largest is at P4-2 (approximately 4.00cm). The average positioning error of 8 points is less than 2.5 cm, and the average positioning error of all other points except P4-2 is less than 3.5 cm. This further demonstrates that the proposed method has excellent damage positioning accuracy under unknown impact force.

[0055] This application also provides a low-speed impact positioning device for composite materials. The device includes a signal acquisition hardware module, a processing module, a time-of-arrival (TOA) extraction module, and a positioning module. The signal acquisition hardware module synchronously acquires multi-channel acoustic emission signals generated after the composite material is impacted, obtaining multi-channel digital signals. The processing module processes the digital signals of each channel and extracts the envelope signal corresponding to each channel. The TOA extraction module extracts the corresponding TOA from the envelope signal of each channel based on an adaptive TOA method. The positioning module determines the position coordinates of the impact point using a four-point circular arc time-of-arrival (CODA) positioning algorithm based on the known coordinates of each channel sensor, the propagation speed of sound waves in the composite material, and the TOA of each channel. The signal acquisition hardware module may further include a sensor unit, a signal conditioning unit, and an analog-to-digital conversion unit. The processing module may further include a detrending unit, a decomposition unit, and an envelope extraction unit. The functional implementation of each module corresponds to the steps in the aforementioned method embodiments and will not be repeated here.

[0056] This application also provides an electronic device, which includes a collector, a memory, a transmitter, and a controller.

[0057] The data acquisition unit is used to acquire raw analog impact signals, including hardware signal conditioning and analog-to-digital conversion. The memory is used to temporarily store digital signals and function programs.

[0058] The transmitter is used to wirelessly upload the temporarily stored data to the host computer. The transmitter can be Bluetooth Low Energy or other communication methods.

[0059] The data acquisition unit, memory, and transmitter can be connected to the controller via a bus. The electronic device may also include necessary components such as communication interfaces and input / output devices.

[0060] This application also provides a time difference positioning algorithm, which, when executed by a host computer, implements the steps of the low-speed impact positioning method for composite materials described above.

[0061] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A low-velocity impact positioning method for composite materials, characterized in that, include: Simultaneously acquire multi-channel acoustic emission signals generated by composite materials after impact to obtain multi-channel digital signals; The digital signals of each channel are processed to extract the envelope signal corresponding to each channel; Based on the adaptive time-of-arrival extraction method, the corresponding time of arrival is extracted from the envelope signal of each channel; Based on the known coordinates of each channel sensor, the propagation speed of sound waves in the composite material, and the arrival time of each channel, the position coordinates of the impact point are determined using a four-point circular arc time difference positioning algorithm.

2. The low-velocity impact positioning method for composite materials according to claim 1, characterized in that, The synchronous acquisition of multi-channel acoustic emission signals generated by the composite material after impact includes: Simulated acoustic emission signals generated in composite materials after impact are collected synchronously using multiple acoustic emission sensors. The acoustic emission analog signal is amplified and filtered; The processed analog signal is converted into a multi-channel digital signal and uploaded.

3. The low-velocity impact positioning method for composite materials according to claim 1, characterized in that, The process of processing the digital signals of each channel to extract the envelope signal corresponding to each channel includes: The digital signal of each channel is subjected to sliding windowing to reduce spectral leakage and then detrending to obtain a detrended signal; The detrending process specifically involves removing the linear trend term of the signal by subtracting the mean amplitude of the signal from the original signal. Empirical mode decomposition is performed on the detrended signal to extract the first intrinsic mode function component; The Hilbert transform is performed on the first intrinsic mode function component to obtain the corresponding envelope signal.

4. The low-velocity impact positioning method for composite materials according to claim 1, characterized in that, The adaptive time-of-arrival extraction method extracts the corresponding time of arrival from the envelope signal of each channel. Specifically, for the envelope signal of each channel, the time of arrival of that channel is determined based on the minimum point of the Akaike information criterion function.

5. The low-velocity impact positioning method for composite materials according to claim 4, characterized in that, The Akaike Information Criterion Function The expression is: ; in, The envelope signal sequence is a single-channel signal. For signal length, For signal segmentation point, This represents variance calculation, where the time of arrival corresponds to the... When the function reaches its minimum value value.

6. The low-velocity impact positioning method for composite materials according to claim 1, characterized in that, The method for determining the position coordinates of the impact point using a four-point circular arc time difference positioning algorithm, based on the known coordinates of each channel sensor, the propagation speed of sound waves in the composite material, and the time of arrival of each channel, includes: Establish a set of distance difference equations based on the time of arrival; The position coordinates of the impact point are obtained by solving the distance difference equations using the least squares method.

7. The low-velocity impact positioning method for composite materials according to claim 6, characterized in that, The distance difference equations are as follows: in, Let be the coordinates of the impact point to be determined. For the first The known coordinates of each sensor, The speed at which sound waves propagate in composite materials. For the first The arrival time corresponding to each sensor. The moment the impact occurs.

8. A low-speed impact positioning device for composite materials, characterized in that, include: The signal acquisition hardware module is used to synchronously acquire multi-channel acoustic emission signals generated by composite materials after impact, and obtain multi-channel digital signals. The processing module is used to process the digital signals of each channel and extract the envelope signal corresponding to each channel; The time of arrival extraction module is used to extract the corresponding time of arrival from the envelope signal of each channel based on the adaptive time of arrival extraction method. The positioning module is used to determine the position coordinates of the impact point based on the known coordinates of each channel sensor, the propagation speed of sound waves in the composite material, and the wave arrival time of each channel, using a four-point circular arc time difference positioning algorithm. The signal acquisition hardware module includes: The sensor unit is used to synchronously acquire simulated acoustic emission signals generated in the composite material after impact through multiple acoustic emission sensors; The signal conditioning unit is used to amplify and filter the acoustic emission analog signal; The analog-to-digital converter unit is used to convert the processed analog signal into a multi-channel digital signal; The processing module includes: The detrending unit is used to subtract the mean amplitude from the digital signal of each channel for detrending processing to obtain a delinearized trend signal. The decomposition unit is used to perform empirical mode decomposition on the detrended signal and extract the first intrinsic mode function component. The envelope extraction unit is used to perform Hilbert transform on the first intrinsic mode function components to obtain the corresponding envelope signal.

9. An electronic device, characterized in that, include: The device comprises a data acquisition unit, a memory, a transmitter, and a controller. The data acquisition unit acquires analog signals, the memory stores data and functional programs, the transmitter performs wireless communication with a host computer, and the controller is responsible for running the functional programs to drive the other three modules. When the electronic device executes the programs, it implements the composite material low-speed impact signal acquisition method as described in claims 1 to 2.

10. A time difference positioning algorithm, wherein a host computer stores and runs the algorithm, characterized in that, When the algorithm is executed by the host computer, it implements the low-speed impact positioning method for composite materials as described in any one of claims 3 to 7.

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