Method and system for identifying damage of composite material fiber woven layer under dynamic impact loading condition

Abstract

The invention discloses a damage identification method and system for a composite material fiber braid layer under a dynamic impact loading condition, and the method comprises the steps: preparing a sample piece with a specific fiber braid layer according to a to-be-monitored composite material structure; applying dynamic impact loading to the sample piece by adopting an impact testing machine at preset impact energy and frequency; in the dynamic impact loading process, a high-sensitivity acoustic emission sensor array is used for monitoring a sample piece, and acoustic emission signals are collected in real time; the collected acoustic emission signals are preprocessed; performing time-frequency feature extraction on the preprocessed acoustic emission signal; and obtaining a damage identification index D based on the extracted time-frequency features. Compared with the prior art, the method not only comprehensively considers multiple factors and performs dynamic response analysis, noise suppression and signal extraction, but also has a time-frequency feature extraction capability and has good applicability and flexibility.

Description

Technical Field

[0001] The 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, automobile, construction and other fields due to their excellent mechanical properties, lightweight characteristics and good corrosion resistance. However, in practical applications, composite materials have complex damage mechanisms when subjected to dynamic impact loading. Dynamic impact loading may cause microcracks, interlayer delamination and other damages inside the material, which are often difficult to detect through 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 technology mainly relies on static testing and visual inspection, which often makes it difficult to monitor the material state in real time and has limited ability to identify minor damage. In addition, conventional acoustic emission monitoring methods also have limitations in signal processing and analysis, especially in high-noise environments. How to effectively identify useful information in acoustic emission signals becomes a major challenge. Therefore, there is an urgent need for a new type of 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 defects existing in the above-mentioned prior art, the present invention provides a method for identifying damage of 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 with 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, preprocessing the collected acoustic emission signals;

[0009] Step S109, extracting time-frequency features of the preprocessed 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, wherein 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, indicating the sensitivity of the sensor to the damage signal; t is the variable time, indicating 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, indicating the frequency characteristics when the shock is applied; x is the integral variable, indicating the instantaneous frequency of the signal in the time-frequency domain.

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

[0014] Wherein, in the step S105, the sampling frequency of the sensor array is dynamically adjusted to adapt to the signal changes under different impact energies to ensure 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] Wherein, the step S107 includes: using an adaptive noise elimination 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 the time.

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

[0022] The present invention also proposes a composite material 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 braiding 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, which 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, which is used to extract time-frequency features of 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 assess the damage degree under dynamic impact loading conditions, wherein 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, indicating the sensitivity of the sensor to the damage signal; t is the variable time, indicating 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, indicating the frequency characteristics when the shock is applied; x is the integral variable, indicating 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 prior art, 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 cAnd the standard deviation of signal distribution σ) to comprehensively evaluate the damage degree of the material. Compared with the existing technology that only considers one factor, this solution can provide a more comprehensive damage assessment and reflect 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 material damage during impact loading in real time and identify potential safety hazards in a timely manner. This real-time monitoring is often difficult to achieve in existing technologies.

[0035] This solution combines an adaptive noise elimination algorithm to dynamically adjust the denoising parameters while improving the quality of the acoustic emission signal. Compared with traditional methods, it can effectively reduce the interference of environmental noise on the signal and ensure more accurate damage identification. This advantage is particularly important in complex environments and can significantly improve the reliability of the signal.

[0036] Through the short-time Fourier transform (STFT), the scheme is able to extract the time-frequency characteristics of the acoustic emission signal. This time-frequency analysis capability enables the effective identification of instantaneous frequency and amplitude changes in the signal under dynamic impact conditions. This feature is generally 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 according to different weaving methods and fiber material characteristics. This wide applicability makes the solution more valuable in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present disclosure will become readily understood. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary 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] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all 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.

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

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

[0043] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after 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 determining" 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 term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

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

[0047] Embodiment 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 with 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, preprocessing the collected acoustic emission signals;

[0053] Step S109, extracting time-frequency features of the preprocessed 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, wherein 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, indicating the sensitivity of the sensor to the damage signal; t is the variable time, indicating 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, indicating the frequency characteristics when the shock is applied; x is the integral variable, indicating 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 the acoustic emission signal to ensure smooth processing of the signal.

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

[0059] Embodiment 2

[0060] The present invention proposes a method for identifying damage to a composite fiber braided layer 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 with 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, preprocessing the collected acoustic emission signals;

[0065] Step S109, extracting time-frequency features of the preprocessed 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, wherein 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, indicating the sensitivity of the sensor to the damage signal; t is the variable time, indicating 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, indicating the frequency characteristics when the shock is applied; x is the integral variable, indicating the instantaneous frequency of the signal in the time-frequency domain.

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

[0070] Wherein, in the step S105, the sampling frequency of the sensor array is dynamically adjusted to adapt to the signal changes under different impact energies to ensure 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 a high-frequency signal appears, the sampling frequency needs to be increased to capture the details of the signal; when the signal changes slowly, the sampling frequency can be reduced to reduce the amount of data.

[0073] By analyzing the frequency spectrum characteristics of the acoustic emission signal, the frequency range of the signal is determined. When the high-frequency component is detected to increase, the sampling frequency is automatically increased; when the frequency component of the signal is 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] Wherein, the step S107 includes: using an adaptive noise elimination algorithm to dynamically adjust denoising parameters according to real-time environmental noise to improve signal quality.

[0077] Adaptive noise removal algorithms are mainly based on the analysis of 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] Decompose the observed signal x(t) into the useful signal s(t) and the noise n(t): x(t) = s(t) + n(t), and use statistical methods to estimate the characteristics of the noise. 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 It can be expressed as: Where w(t) is the weight coefficient of the adaptive filter.

[0080] By monitoring the environmental noise level in real time, the noise signal n(t) is acquired using sensors. If the noise level is higher than the set threshold, the algorithm will adjust 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 an increase in ambient noise is detected, the system can dynamically increase the μ value to improve noise suppression capabilities. After adaptive noise elimination, the output signal It should be close to the real useful signal s(t), so as to provide 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 the time.

[0085] STFT converts the time signal into the time-frequency domain to extract the instantaneous frequency and amplitude information. This information can better describe the changing characteristics of the acoustic emission signal under dynamic impact loading conditions, especially the characteristics related to material damage. The instantaneous frequency and amplitude S(f,t) obtained by STFT can be used as the basic data for calculating the damage identification index D. These characteristics can directly affect the accuracy of damage identification.

[0086] Signal amplitude In the calculation of the damage identification index D, the acoustic emission signal amplitude A collected by the sensor 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 impact 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 evaluated more comprehensively.

[0087] Under dynamic impact, the damage state of the material may cause the instantaneous frequency and amplitude of the acoustic emission signal to change. 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 state 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 composite materials use high-strength carbon fiber, have excellent mechanical properties and lightweight characteristics, and adopt plain weave (plain weave) to enhance the impact resistance and rigidity of the material. Commonly used in aerospace and high-performance sports equipment, such as aircraft wings, racing car bodies, etc.

[0092] Glass fiber / polyester resin composite material, using glass fiber, has good corrosion resistance and cost-effectiveness. Adopt winding weaving (winding structure) to improve the tensile strength and toughness of the material. Widely used in shipbuilding, construction and automotive industries, such as FRP products, pipelines, etc.

[0093] Aramid fiber / resin composites, using aramid fibers (such as Kevlar), have excellent impact resistance and heat resistance. Twill weave (twill weave) is used to enhance the material's wear resistance and tear resistance. Commonly used in the manufacture of bulletproof vests, aviation components and high-strength ropes.

[0094] Natural fiber / bio-based resin composite materials use natural fibers (such as hemp or bamboo fibers), which are environmentally friendly and renewable. They use unidirectional weaving (unidirectional laying) to improve the strength and stiffness of the material while reducing weight. They are suitable for automotive interiors, building materials, and environmentally friendly packaging.

[0095] Embodiment 3

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

[0097] A sample preparation device, which is used to prepare a sample with a specific fiber braiding 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, which 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, which is used to extract time-frequency features of 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 assess the damage degree under dynamic impact loading conditions, wherein 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, indicating the sensitivity of the sensor to the damage signal; t is the variable time, indicating 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, indicating the frequency characteristics when the shock is applied; x is the integral variable, indicating the instantaneous frequency of the signal in the time-frequency domain.

[0105] Embodiment 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 disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, 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 containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit 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 of the above.

[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 separate 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 (AN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0110] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a 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 the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, 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, but is not intended to limit the present invention. All modifications, substitutions, 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 braided layer 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 with 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, preprocessing the collected acoustic emission signals; Step S109, extracting time-frequency features of the preprocessed 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, wherein 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, indicating the sensitivity of the sensor to the damage signal; t is the variable time, indicating 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, indicating the frequency characteristics when the shock is applied; x is the integral variable, indicating the instantaneous frequency of the signal in the time-frequency domain.

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

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

4. The method according to claim 3, characterized in that: 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, characterized in that: The step S107 includes: using an adaptive noise elimination algorithm to dynamically adjust denoising parameters according to real-time environmental noise to improve signal quality.

6. The method according to claim 5, characterized in that The adaptive noise elimination algorithm includes a minimum mean square error algorithm or a recursive least squares method.

7. The method according to claim 1, characterized in that: 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, characterized in that: 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 the time.

9. The method according to claim 1, characterized in that: The samples are composed of different fiber materials and weaving methods.

10. A composite fiber braid layer damage identification system under dynamic impact loading conditions, comprising: A sample preparation device, which is used to prepare a sample with a specific fiber braiding 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, which 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, which is used to extract time-frequency features of 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 assess the damage degree under dynamic impact loading conditions, wherein 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, indicating the sensitivity of the sensor to the damage signal; t is the variable time, indicating 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, indicating the frequency characteristics when the shock is applied; x is the integral variable, indicating the instantaneous frequency of the signal in the time-frequency domain.

Images