Damage identification method and system for composite fiber braided layers under dynamic impact loading conditions

Abstract

The present invention discloses a method and system for identifying damage in composite fiber braids under dynamic impact loading conditions. The method comprises: preparing a sample with a specific fiber braid based on the composite material structure to be monitored; applying dynamic impact loading to the sample using an impact testing machine at a preset impact energy and frequency; monitoring the sample during the dynamic impact loading process using a high-sensitivity acoustic emission sensor array to collect acoustic emission signals in real time; preprocessing the collected acoustic emission signals; extracting time-frequency features from the preprocessed acoustic emission signals; and obtaining a damage identification index D based on the extracted time-frequency features. Compared to existing technologies, the present invention not only comprehensively considers multiple factors, performing dynamic response analysis, noise suppression, and signal extraction, but also possesses the ability to extract time-frequency features, offering excellent applicability and flexibility.

Description

Technical Field

[0001] The present invention belongs to the field of computer system engineering, and in particular relates to a method and system for identifying damage of a composite fiber braided layer under dynamic impact loading conditions. Background Art

[0002] With the development of modern materials science, composite materials are widely used in aerospace, automotive, construction and other fields due to their excellent mechanical properties, lightweight characteristics, and good corrosion resistance. However, in practical applications, composite materials are subject to complex damage mechanisms when subjected to dynamic impact loading. Dynamic impact loading can cause damage such as microcracks and interlaminar delamination within the material, which is often difficult to detect using conventional detection methods. Therefore, the development of effective damage identification technology is of great significance to ensure the safety and reliability of materials.

[0003] Traditional damage monitoring techniques rely primarily on static testing and visual inspection, which often make it difficult to monitor material conditions in real time and have limited ability to identify subtle damage. Furthermore, conventional acoustic emission monitoring methods also have limitations in signal processing and analysis. This presents a significant challenge, particularly in high-noise environments, where effectively identifying useful information from acoustic emission signals presents a significant challenge. Therefore, there is an urgent need for a novel damage identification method that can monitor material damage in real time under dynamic impact conditions and provide accurate assessments. Summary of the Invention

[0004] In view of the above-mentioned defects in the prior art, the present invention provides a method for identifying damage to a composite fiber braided layer under dynamic impact loading conditions, comprising the following steps:

[0005] Step S101: preparing a sample with a specific fiber braided layer according to the composite material structure to be monitored;

[0006] Step S103: applying dynamic impact loading to the sample using an impact testing machine at a preset impact energy and frequency;

[0007] Step S105: During the dynamic impact loading process, a high-sensitivity acoustic emission sensor array is used to monitor the sample and collect acoustic emission signals in real time;

[0008] Step S107: pre-processing the collected acoustic emission signals;

[0009] Step S109: extracting time-frequency features from the pre-processed acoustic emission signal;

[0010] Step S1011: Based on the extracted time-frequency features, a damage identification index D is obtained to evaluate the damage degree under dynamic impact loading conditions. The damage identification index D is calculated using the following formula:

[0011]

[0012] D is the damage identification index, 0≤D; T is the time window, which represents the total time period of data collection; N is the number of signal sources, which represents the number of acoustic emission sensors used in the monitoring process; A i is the amplitude of the acoustic emission signal collected by the i-th sensor, which indicates the sensitivity of the sensor to the damage signal; t is the variable time, which indicates the instantaneous time in the signal collection; t i is the time point when the i-th acoustic emission signal is triggered, indicating the specific moment when the acoustic emission signal occurs; σ is the standard deviation, indicating the distribution width of the signal and controlling the smoothness of the signal; E is the dynamic impact energy, indicating the magnitude of the impact energy applied to the sample; f c is the specific frequency of the dynamic shock, which represents the frequency characteristics when the shock is applied; x is the integral variable, which represents the instantaneous frequency of the signal in the time-frequency domain.

[0013] In step S103, a graded impact loading method is adopted to gradually increase the impact energy to observe the damage evolution process of the material under different energies.

[0014] In step S105 , the sampling frequency of the sensor array is dynamically adjusted to adapt to signal changes under different impact energies, thereby ensuring the accuracy and reliability of the signal.

[0015] The dynamic adjustment of the sampling frequency of the sensor array includes: automatically increasing the sampling frequency when an increase in high-frequency components is detected; and reducing the sampling frequency when the frequency components of the signal are low.

[0016] The step S107 includes: using an adaptive noise cancellation algorithm to dynamically adjust denoising parameters according to real-time environmental noise to improve signal quality.

[0017] The adaptive noise elimination algorithm includes a minimum mean square error algorithm or a recursive least squares method.

[0018] Wherein, the step S109 includes performing time-frequency analysis on the acoustic emission signal using short-time Fourier transform (STFT) to obtain instantaneous frequency and amplitude information.

[0019] Among them, the following formula is used to obtain instantaneous frequency and amplitude information:

[0020] Among them, S(f,t) represents the extracted time-frequency features, x(t) is the acoustic emission signal, w(t-τ) is the window function, f is the frequency of the acoustic emission signal, and t is time.

[0021] The samples are composed of different fiber materials and weaving methods.

[0022] The present invention also proposes a composite fiber braid layer damage identification system under dynamic impact loading conditions, comprising:

[0023] A sample preparation device, which is used to prepare a sample with a specific fiber braid layer according to the composite material structure to be monitored, wherein the sample is composed of different fiber materials and braiding methods;

[0024] An impact testing machine, which is used to apply dynamic impact loading to the sample with a preset impact energy and frequency;

[0025] A high-sensitivity acoustic emission sensor array is used to monitor the sample during the dynamic impact loading process and collect acoustic emission signals in real time;

[0026] A preprocessing module, which is used to preprocess the collected acoustic emission signals;

[0027] A time-frequency feature extraction module is used to extract time-frequency features from the preprocessed acoustic emission signal;

[0028] The damage degree assessment module is used to calculate the damage identification index D based on the extracted time-frequency features to evaluate the damage degree under dynamic impact loading conditions. The damage identification index D is calculated using the following formula:

[0029] in,

[0030] D is the damage identification index, 0≤D; T is the time window, which represents the total time period of data collection; N is the number of signal sources, which represents the number of acoustic emission sensors used in the monitoring process; A i is the amplitude of the acoustic emission signal collected by the i-th sensor, which indicates the sensitivity of the sensor to the damage signal; t is the variable time, which indicates the instantaneous time in the signal collection; t i is the time point when the i-th acoustic emission signal is triggered, indicating the specific moment when the acoustic emission signal occurs; σ is the standard deviation, indicating the distribution width of the signal and controlling the smoothness of the signal; E is the dynamic impact energy, indicating the magnitude of the impact energy applied to the sample; f c is the specific frequency of the dynamic shock, which represents the frequency characteristics when the shock is applied; x is the integral variable, which represents the instantaneous frequency of the signal in the time-frequency domain.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] Compared with the existing technology, it has the following significant beneficial effects:

[0033] The technical solution introduces multiple factors (such as acoustic emission signal amplitude, dynamic impact energy E, impact frequency f cThe damage degree of the material is comprehensively assessed by using the standard deviation (σ) of the signal distribution. Compared with existing technologies that only consider a single factor, this solution can provide a more comprehensive damage assessment, reflecting the true state of the material under dynamic impact loading.

[0034] By integrating the time window T in the formula, the damage response of the material at different time stages can be effectively captured. This dynamic analysis capability enables the solution to monitor the evolution of damage in the material during impact loading in real time, promptly identifying potential safety hazards. This type of real-time monitoring is often difficult to achieve with existing technologies.

[0035] This solution, combined with an adaptive noise cancellation algorithm, dynamically adjusts denoising parameters while improving the quality of acoustic emission signals. Compared to traditional methods, it effectively reduces the interference of ambient noise on the signal, ensuring more accurate damage identification. This advantage is particularly important in complex environments and can significantly improve signal reliability.

[0036] This solution uses the Short-Time Fourier Transform (STFT) to extract the time-frequency characteristics of acoustic emission signals. This time-frequency analysis capability effectively identifies instantaneous frequency and amplitude changes in the signal, even in dynamic impact conditions. This feature is often lacking in existing technologies, limiting the understanding of complex signal characteristics.

[0037] This technical solution can be applied to a variety of materials and structures, especially in the field of composite materials, and can be flexibly adjusted to different weaving methods and fiber material properties. This wide applicability makes the solution more valuable in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0039] Figure 1 The flowchart shows a method for identifying damage of a composite fiber braided layer under dynamic impact loading conditions according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings. It is apparent that the embodiments described are only some, not all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0041] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a," "an," "the," and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.

[0042] It should be understood that although the terms "first," "second," "third," etc. may be used to describe "...," these "..." should not be limited to these terms. These terms are merely used to distinguish "...." For example, "first..." could also be referred to as "second...", and similarly, "second..." could also be referred to as "first..." without departing from the scope of the present invention.

[0043] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0044] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0045] It should also be noted that the terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such product or device. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the product or device comprising the element.

[0046] The optional embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0047] Example 1

[0048] like Figure 1 As shown, the present invention discloses a method for identifying and evaluating damage of a composite fiber braided layer under dynamic impact loading conditions, comprising the following steps:

[0049] Step S101: preparing a sample with a specific fiber braided layer according to the composite material structure to be monitored;

[0050] Step S103: applying dynamic impact loading to the sample using an impact testing machine at a preset impact energy and frequency;

[0051] Step S105: During the dynamic impact loading process, a high-sensitivity acoustic emission sensor array is used to monitor the sample and collect acoustic emission signals in real time;

[0052] Step S107: pre-processing the collected acoustic emission signals;

[0053] Step S109: extracting time-frequency features from the pre-processed acoustic emission signal;

[0054] Step S1011: Based on the extracted time-frequency features, a damage identification index D is obtained to evaluate the damage degree under dynamic impact loading conditions. The damage identification index D is calculated using the following formula:

[0055] in,

[0056] D is the damage identification index, 0≤D; T is the time window, which represents the total time period of data collection; N is the number of signal sources, which represents the number of acoustic emission sensors used in the monitoring process; A i is the amplitude of the acoustic emission signal collected by the i-th sensor, which indicates the sensitivity of the sensor to the damage signal; t is the variable time, which indicates the instantaneous time in the signal collection; t i is the time point when the i-th acoustic emission signal is triggered, indicating the specific moment when the acoustic emission signal occurs; σ is the standard deviation, indicating the distribution width of the signal and controlling the smoothness of the signal; E is the dynamic impact energy, indicating the magnitude of the impact energy applied to the sample; f c is the specific frequency of the dynamic shock, which represents the frequency characteristics when the shock is applied; x is the integral variable, which represents the instantaneous frequency of the signal in the time-frequency domain.

[0057] Among them, dynamic impact energy E and specific impact frequency f c It can reflect the impact of acoustic emission signals under specific impact conditions. Gaussian function Describe the distribution of acoustic emission signals to ensure smooth signal processing.

[0058] Positive values ​​of D indicate an increase in the degree of damage. A larger value indicates more severe damage and is affected by the dynamic impact energy and frequency.

[0059] Example 2

[0060] The present invention proposes a method for identifying damage to a composite fiber braid under dynamic impact loading conditions, comprising the following steps:

[0061] Step S101: preparing a sample with a specific fiber braided layer according to the composite material structure to be monitored;

[0062] Step S103: applying dynamic impact loading to the sample using an impact testing machine at a preset impact energy and frequency;

[0063] Step S105: During the dynamic impact loading process, a high-sensitivity acoustic emission sensor array is used to monitor the sample and collect acoustic emission signals in real time;

[0064] Step S107: pre-processing the collected acoustic emission signals;

[0065] Step S109: extracting time-frequency features from the pre-processed acoustic emission signal;

[0066] Step S1011: Based on the extracted time-frequency features, a damage identification index D is obtained to evaluate the damage degree under dynamic impact loading conditions. The damage identification index D is calculated using the following formula:

[0067] in,

[0068] D is the damage identification index, 0≤D; T is the time window, which represents the total time period of data collection; N is the number of signal sources, which represents the number of acoustic emission sensors used in the monitoring process; A i is the amplitude of the acoustic emission signal collected by the i-th sensor, which indicates the sensitivity of the sensor to the damage signal; t is the variable time, which indicates the instantaneous time in the signal collection; t i is the time point when the i-th acoustic emission signal is triggered, indicating the specific moment when the acoustic emission signal occurs; σ is the standard deviation, indicating the distribution width of the signal and controlling the smoothness of the signal; E is the dynamic impact energy, indicating the magnitude of the impact energy applied to the sample; f c is the specific frequency of the dynamic shock, which represents the frequency characteristics when the shock is applied; x is the integral variable, which represents the instantaneous frequency of the signal in the time-frequency domain.

[0069] In step S103, a graded impact loading method is adopted to gradually increase the impact energy to observe the damage evolution process of the material under different energies.

[0070] In step S105 , the sampling frequency of the sensor array is dynamically adjusted to adapt to signal changes under different impact energies, thereby ensuring the accuracy and reliability of the signal.

[0071] The dynamic adjustment of the sampling frequency of the sensor array includes: automatically increasing the sampling frequency when an increase in high-frequency components is detected; and reducing the sampling frequency when the frequency components of the signal are low.

[0072] The sampling frequency directly affects the resolution and real-time performance of the signal. When high-frequency signals are present, the sampling frequency needs to be increased to capture signal details; when signals change slowly, the sampling frequency can be reduced to reduce the amount of data.

[0073] By analyzing the spectral characteristics of the acoustic emission signal, the frequency range of the signal is determined. When an increase in high-frequency components is detected, the sampling frequency is automatically increased; when the frequency components of the signal are low, the sampling frequency is reduced.

[0074] At each stage of impact loading, the sampling frequency is adjusted according to the signal characteristics (such as amplitude, frequency, duration, etc.) monitored in real time.

[0075] Set upper and lower limits for sensitivity and sampling frequency, and trigger automatic adjustments when signal characteristics exceed or fall below these thresholds.

[0076] The step S107 includes: using an adaptive noise cancellation algorithm to dynamically adjust denoising parameters according to real-time environmental noise to improve signal quality.

[0077] Adaptive noise cancellation algorithms are primarily based on analyzing signal and noise characteristics, dynamically adjusting denoising parameters to adapt to real-time environmental changes. Commonly used algorithms include adaptive filters such as the least mean square error (LMS) algorithm and the recursive least squares (RLS) algorithm.

[0078] The specific steps include:

[0079] The observed signal x(t) is decomposed into the useful signal s(t) and the noise n(t): x(t) = s(t) + n(t). The characteristics of the noise are estimated using statistical methods. Usually, the spectral characteristics of the noise are obtained by analyzing the short-time Fourier transform (STFT) or wavelet transform of the signal. According to the noise characteristics, the parameters of the filter are dynamically adjusted to eliminate the noise component. The output of the adaptive filter is It can be expressed as: Where w(t) is the weight coefficient of the adaptive filter.

[0080] By monitoring the ambient noise level in real time, a sensor is used to acquire the noise signal n(t). If the noise level is higher than a set threshold, the algorithm adjusts the denoising parameters to enhance noise suppression. Based on the noise estimate, the parameters w(t) of the adaptive filter are dynamically updated. For example, the LMS algorithm can be used to update the weights: w(t+1)=w(t)+μ·e(t)·x(t), where μ is the learning rate, which controls the step size of the weight update. is the error signal, and d(t) is the desired signal.

[0081] In acoustic emission monitoring, the monitoring system can analyze the noise level of the signal in real time and automatically adjust the parameters of the denoising algorithm. For example, at the beginning of impact loading, if the ambient noise increases, the system can dynamically increase the μ value to improve the noise suppression capability. After adaptive noise elimination, the output signal It should be close to the real useful signal s(t), thus providing higher quality input data for subsequent damage identification.

[0082] Wherein, the step S109 includes performing time-frequency analysis on the acoustic emission signal using short-time Fourier transform (STFT) to obtain instantaneous frequency and amplitude information.

[0083] Among them, the following formula is used to obtain instantaneous frequency and amplitude information:

[0084] Among them, S(f, t) represents the extracted time-frequency features, x(t) is the acoustic emission signal, w(t-τ) is the window function, f is the frequency of the acoustic emission signal, and t is time.

[0085] The STFT transforms the time signal into the time-frequency domain to extract instantaneous frequency and amplitude information. This information better describes the characteristics of acoustic emission signals under dynamic impact loading, particularly those related to material damage. The instantaneous frequency and amplitude S(f,t) obtained through the STFT serve as the basis for calculating the damage identification index D. These characteristics directly impact the accuracy of damage identification.

[0086] In the calculation of the damage identification index D, the acoustic emission signal amplitude A collected by the sensor is i It can be obtained from the amplitude information of STFT. By analyzing the amplitude at different time points, the degree of damage can be judged more accurately. The frequency characteristics of dynamic shock f c It can also be obtained through time-frequency analysis. By combining the frequency information extracted by STFT with the dynamic impact frequency, the damage impact can be more comprehensively evaluated.

[0087] Under dynamic impact, the material's damage state can cause changes in the instantaneous frequency and amplitude of the acoustic emission signal. The time-frequency information provided by STFT can capture these changes and provide real-time feedback for the calculation of D.

[0088] In the calculation formula of D, the characteristics of the acoustic emission signal (including the amplitude and frequency information obtained from the STFT) are integrated into the calculation of the damage identification index, so that D can reflect the damage status under specific impact conditions.

[0089] The samples are composed of different fiber materials and weaving methods.

[0090] The samples include but are not limited to:

[0091] Carbon fiber / epoxy resin composites utilize high-strength carbon fibers, which offer excellent mechanical properties and lightweight properties. A plain weave is used to enhance the material's impact resistance and rigidity. These composites are commonly used in aerospace and high-performance sports equipment, such as aircraft wings and racing car bodies.

[0092] Glass fiber / polyester resin composites utilize glass fiber for excellent corrosion resistance and cost-effectiveness. A twisted braid (wound structure) enhances the material's tensile strength and toughness. They are widely used in the marine, construction, and automotive industries, such as fiberglass products and pipelines.

[0093] Aramid fiber / resin composites, using aramid fibers (such as Kevlar), offer excellent impact and heat resistance. A twill weave enhances the material's abrasion and tear resistance. They are commonly used in the manufacture of body armor, aviation components, and high-strength ropes.

[0094] Natural fiber / bio-based resin composites utilize natural fibers (such as hemp or bamboo), which are environmentally friendly and renewable. Unidirectional weaving (one-way laying) enhances the material's strength and stiffness while reducing weight. They are suitable for automotive interiors, construction materials, and environmentally friendly packaging.

[0095] Example 3:

[0096] The present invention also proposes a composite fiber braid layer damage identification system under dynamic impact loading conditions, which includes:

[0097] A sample preparation device, which is used to prepare a sample with a specific fiber braid layer according to the composite material structure to be monitored, wherein the sample is composed of different fiber materials and braiding methods;

[0098] An impact testing machine, which is used to apply dynamic impact loading to the sample with a preset impact energy and frequency;

[0099] A high-sensitivity acoustic emission sensor array is used to monitor the sample during the dynamic impact loading process and collect acoustic emission signals in real time;

[0100] A preprocessing module, which is used to preprocess the collected acoustic emission signals;

[0101] A time-frequency feature extraction module is used to extract time-frequency features from the preprocessed acoustic emission signal;

[0102] The damage degree assessment module is used to calculate the damage identification index D based on the extracted time-frequency features to evaluate the damage degree under dynamic impact loading conditions. The damage identification index D is calculated using the following formula:

[0103] in,

[0104] D is the damage identification index, 0≤D; T is the time window, which represents the total time period of data collection; N is the number of signal sources, which represents the number of acoustic emission sensors used in the monitoring process; A i is the amplitude of the acoustic emission signal collected by the i-th sensor, which indicates the sensitivity of the sensor to the damage signal; t is the variable time, which indicates the instantaneous time in the signal collection; t i is the time point when the i-th acoustic emission signal is triggered, indicating the specific moment when the acoustic emission signal occurs; σ is the standard deviation, indicating the distribution width of the signal and controlling the smoothness of the signal; E is the dynamic impact energy, indicating the magnitude of the impact energy applied to the sample; f c is the specific frequency of the dynamic shock, which represents the frequency characteristics when the shock is applied; x is the integral variable, which represents the instantaneous frequency of the signal in the time-frequency domain.

[0105] Example 4:

[0106] An embodiment of the present disclosure provides a non-volatile computer storage medium, wherein the computer storage medium stores computer-executable instructions, and the computer-executable instructions can execute the method steps described in the above embodiment.

[0107] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0108] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0109] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0110] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0111] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.

[0112] The above introduces the preferred embodiments of the present invention, which is intended to make the spirit of the present invention clearer and easier to understand, and is not intended to limit the present invention. Any modifications, replacements, and improvements made within the spirit and principles of the present invention should be included in the scope of protection outlined by the claims attached to the present invention.

Claims

1. A method for identifying damage to a composite fiber braid under dynamic impact loading conditions, characterized in that: The following steps are involved: Step S101: preparing a sample with a specific fiber braided layer according to the composite material structure to be monitored; Step S103: applying dynamic impact loading to the sample using an impact testing machine at a preset impact energy and frequency; Step S105: During the dynamic impact loading process, a high-sensitivity acoustic emission sensor array is used to monitor the sample and collect acoustic emission signals in real time; Step S107: pre-processing the collected acoustic emission signals; Step S109: extracting time-frequency features from the pre-processed acoustic emission signal; Step S1011: Based on the extracted time-frequency features, a damage identification index D is obtained to evaluate the damage degree under dynamic impact loading conditions. The damage identification index D is calculated using the following formula: in, D is the damage identification index, 0≤D; T is the time window, which represents the total time period of data collection; N is the number of signal sources, which represents the number of acoustic emission sensors used in the monitoring process; A i is the amplitude of the acoustic emission signal collected by the i-th sensor, which indicates the sensitivity of the sensor to the damage signal; t is the variable time, which indicates the instantaneous time in the signal collection; t i is the time point when the i-th acoustic emission signal is triggered, indicating the specific moment when the acoustic emission signal occurs; σ is the standard deviation, indicating the distribution width of the signal and controlling the smoothness of the signal; E is the dynamic impact energy, indicating the magnitude of the impact energy applied to the sample; f c is the specific frequency of the dynamic shock, which represents the frequency characteristics when the shock is applied; x is the integral variable, which represents the instantaneous frequency of the signal in the time-frequency domain.

2. The method according to claim 1, wherein: In step S103, a graded impact loading method is adopted to gradually increase the impact energy to observe the damage evolution process of the material under different energies.

3. The method according to claim 1, wherein: In step S105 , the sampling frequency of the sensor array is dynamically adjusted to adapt to signal changes under different impact energies, thereby ensuring the accuracy and reliability of the signal.

4. The method according to claim 3, wherein: The dynamic adjustment of the sampling frequency of the sensor array includes: automatically increasing the sampling frequency when an increase in high-frequency components is detected; and reducing the sampling frequency when the frequency components of the signal are low.

5. The method according to claim 1, wherein: The step S107 includes: using an adaptive noise cancellation algorithm to dynamically adjust denoising parameters according to real-time environmental noise to improve signal quality.

6. The method according to claim 5, wherein: The adaptive noise cancellation algorithm includes a minimum mean square error algorithm or a recursive least squares method.

7. The method according to claim 1, wherein: The step S109 includes performing time-frequency analysis on the acoustic emission signal using short-time Fourier transform (STFT) to obtain instantaneous frequency and amplitude information.

8. The method according to claim 7, wherein: The following formula is used to obtain instantaneous frequency and amplitude information: Among them, S(f,t) represents the extracted time-frequency features, x(t) is the acoustic emission signal, w(t-τ) is the window function, f is the frequency of the acoustic emission signal, and t is time.

9. The method according to claim 1, wherein: The samples are made of different fiber materials and weaving methods.

10. A composite fiber braid damage identification system under dynamic impact loading conditions, comprising: A sample preparation device, which is used to prepare a sample with a specific fiber braid layer according to the composite material structure to be monitored, wherein the sample is composed of different fiber materials and braiding methods; An impact testing machine, which is used to apply dynamic impact loading to the sample with a preset impact energy and frequency; A high-sensitivity acoustic emission sensor array is used to monitor the sample during the dynamic impact loading process and collect acoustic emission signals in real time; A preprocessing module, which is used to preprocess the collected acoustic emission signals; A time-frequency feature extraction module is used to extract time-frequency features from the preprocessed acoustic emission signal; The damage degree assessment module is used to calculate the damage identification index D based on the extracted time-frequency features to evaluate the damage degree under dynamic impact loading conditions. The damage identification index D is calculated using the following formula: in, D is the damage identification index, 0≤D; T is the time window, which represents the total time period of data collection; N is the number of signal sources, which represents the number of acoustic emission sensors used in the monitoring process; A i is the amplitude of the acoustic emission signal collected by the i-th sensor, which indicates the sensitivity of the sensor to the damage signal; t is the variable time, which indicates the instantaneous time in the signal collection; t i is the time point when the i-th acoustic emission signal is triggered, indicating the specific moment when the acoustic emission signal occurs; σ is the standard deviation, indicating the distribution width of the signal and controlling the smoothness of the signal; E is the dynamic impact energy, indicating the magnitude of the impact energy applied to the sample; f c is the specific frequency of the dynamic shock, which represents the frequency characteristics when the shock is applied; x is the integral variable, which represents the instantaneous frequency of the signal in the time-frequency domain.

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