Reconstruction generation method and system of super Gaussian signal for vehicle structure vibration test

Abstract

The invention discloses a reconstruction generation method and system of a super Gaussian signal for a vehicle structure vibration test. The method comprises the following steps: S1, a signal acquisition module acquires power spectral density data of vehicle road excitation; s2, the data processing terminal generates a Gaussian vibration signal G (t) according to the received power spectrum density data; s3, according to the target kurtosis Ky required by the vibration test of the vehicle structure, solving through a preset nonlinear equation to obtain an index parameter p, and S4, generating a low-frequency Gaussian vibration signal g (t) by the data processing terminal, and calculating and generating an amplitude modulation signal u (t) according to the solved p value. S5, generating a super Gaussian signal Y (t); and S6, taking the super Gaussian signal Y (t) as an excitation signal required by the vibration test of the vehicle structure. According to the invention, efficient online reconstruction of the super Gaussian signal is realized, and the structure vibration test requirements in the high-reliability field of automobile engineering and the like are met.

Description

Technical Field

[0001] This invention relates to the field of vehicle structure vibration testing technology, and in particular to a method and system for reconstructing and generating a super-Gaussian signal for vehicle structure vibration testing. It is applicable to vibration testing of mechanical structures such as vehicle frames and suspensions, with the super-Gaussian signal serving as the excitation signal required for vehicle structure vibration testing. Background Technology

[0002] In high-reliability fields such as automotive engineering, fatigue damage to vehicle structures under complex random loads remains a significant factor affecting the accuracy of vehicle service performance and lifespan assessment. To achieve reliable fatigue analysis and lifespan prediction, the key lies in the reasonable characterization and effective reconstruction of actual vehicle service loads. Current vehicle structural vibration tests often use random signals following a Gaussian distribution as input. However, numerous experimental results show that actual road excitations generally exhibit significant super-Gaussian characteristics, with their probability distributions differing significantly from the Gaussian distribution in higher-order statistical indicators such as kurtosis. Super-Gaussian excitations often lead to higher peak stresses and a greater risk of fatigue damage in the vehicle structural response. If models or vibration tests are still built based on the Gaussian assumption, the probability of high-amplitude events will be underestimated, resulting in biased fatigue life predictions.

[0003] Furthermore, for vehicles traveling on complex terrain, the super-Gaussian vibration excitation signal introduced from the ground, after being amplified by the suspension, can cause severe vibrations, and in severe cases, even suspension breakage, leading to serious safety accidents. To more realistically reproduce this complex excitation environment, researchers have proposed various methods for synthesizing super-Gaussian random signals, such as polynomial transform, phase modulation, Poisson pulse, and amplitude modulation. Among these, amplitude modulation is widely used due to its flexible control over kurtosis and ease of implementation. This method generates a super-Gaussian signal with the target kurtosis by multiplying a stationary Gaussian signal with a slowly varying amplitude modulation signal. However, traditional amplitude modulation requires multiple iterations, resulting in low efficiency and making online reconstruction of the super-Gaussian signal impossible. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for reconstructing and generating a super-Gaussian signal for vehicle structure vibration testing, wherein the generated super-Gaussian signal serves as the excitation signal required for vehicle structure vibration testing.

[0005] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows: Firstly, a method for reconstructing and generating a super-Gaussian signal used in vehicle structure vibration testing, implemented by a vibration signal processing system, wherein the vibration signal processing system includes a signal acquisition module and a data processing terminal; the signal acquisition module is communicatively connected to the data processing terminal, and the data processing terminal is communicatively connected to an external vibration test bench; the method includes the following steps:

[0006] S1. The signal acquisition module acquires the power spectral density data of the vehicle road excitation and transmits the power spectral density data to the data processing terminal;

[0007] S2. The data processing terminal generates a Gaussian vibration signal G(t) with a mean of 0 by inverse fast Fourier transform based on the received power spectral density data.

[0008] S3. Based on the target kurtosis required for vehicle structural vibration testing. The data processing terminal obtains the exponential parameter p by solving a preset nonlinear equation, wherein the nonlinear equation is: ;

[0009] in It is a gamma function;

[0010] S4. The data processing terminal generates a low-frequency Gaussian vibration signal g(t) independent of the Gaussian vibration signal G(t), and g(t) has the same time length as G(t). Based on the obtained p value, an amplitude modulation signal u(t) is calculated and generated. ;

[0011] S5. The data processing terminal multiplies the Gaussian vibration signal G(t) with the amplitude modulation signal u(t) to generate a target kurtosis that meets the requirements of vehicle structure vibration testing. The super-Gaussian signal Y(t), ;

[0012] S6. The data processing terminal transmits the generated super-Gaussian signal Y(t) to an external vibration test bench as the excitation signal required for vehicle structure vibration testing.

[0013] Furthermore, the signal acquisition module includes an accelerometer and a spectrum analyzer. The accelerometer acquires the raw vibration signal of the vehicle road excitation, and the spectrum analyzer converts the raw vibration signal into power spectral density data and transmits it to the data processing terminal.

[0014] Furthermore, in the method described, the data processing terminal is an industrial computer equipped with a signal processing algorithm program, which has a built-in gamma function calculation library and a nonlinear equation solving module, and calls the library functions to solve for the exponential parameter p.

[0015] Furthermore, in the method, the frequency range of the low-frequency Gaussian vibration signal g(t) is 0.01Hz to 10Hz, and the frequency range of the Gaussian vibration signal G(t) is 10Hz to 5000Hz, thus forming a frequency separation between the two.

[0016] Furthermore, in the method, the target kurtosis required for the vehicle structure vibration test... The value range is 3.25 to 12.

[0017] In a second aspect, a vibration signal processing system is provided, the system comprising a signal acquisition module and a data processing terminal, wherein the signal acquisition module is communicatively connected to the data processing terminal and the data processing terminal is communicatively connected to an external vibration test bench.

[0018] The signal acquisition module is used to acquire power spectral density data of vehicle road excitation and transmit the power spectral density data to the data processing terminal.

[0019] The data processing terminal is configured to perform the following operations:

[0020] S11. Based on the received power spectral density data, a Gaussian vibration signal G(t) with a mean of 0 is generated by inverse fast Fourier transform;

[0021] S12. Based on the target kurtosis required for vehicle structural vibration testing. The data processing terminal obtains the exponential parameter p by solving a preset nonlinear equation, wherein the nonlinear equation is: ;

[0022] in It is a gamma function;

[0023] S13. Generate a low-frequency Gaussian vibration signal g(t) independent of the Gaussian vibration signal G(t), where g(t) and G(t) have the same time length. Calculate and generate the amplitude modulation signal u(t) based on the obtained p value. ;

[0024] S14. Multiply the Gaussian vibration signal G(t) with the amplitude modulation signal u(t) to generate the target kurtosis required for vehicle structural vibration testing. The super-Gaussian signal Y(t), ;

[0025] The data processing terminal transmits the generated super-Gaussian signal Y(t) to an external vibration test bench as the excitation signal required for vehicle structure vibration testing.

[0026] Furthermore, in the system, the signal acquisition module includes an accelerometer and a spectrum analyzer. The accelerometer is used to acquire the original vibration signal of the vehicle road excitation, and the spectrum analyzer is used to convert the original vibration signal into power spectral density data and then transmit it to the data processing terminal.

[0027] Furthermore, in the system described above, the data processing terminal is an industrial computer equipped with signal processing algorithm programs, with a built-in gamma function calculation library and a nonlinear equation solving module, which calls library functions to solve for the exponential parameter p.

[0028] Furthermore, in the system, the frequency range of the low-frequency Gaussian vibration signal g(t) is 0.01Hz to 10Hz, and the frequency range of the Gaussian vibration signal G(t) is 10Hz to 5000Hz, thus forming a frequency separation between the two.

[0029] The beneficial effects of this invention are as follows: The reconstruction generation method and system for the super-Gaussian signal used in vehicle structure vibration testing of this invention avoids the external iterative loop of repeated statistical verification and adjustment of the generated signal to control kurtosis in the traditional amplitude modulation method. Instead, it directly determines the modulation parameter p by solving an equation with a clear analytical relationship with kurtosis. This equation itself can be solved quickly using efficient numerical methods, realizing efficient online reconstruction of the super-Gaussian signal. The system of this invention realizes an integrated process of "data acquisition-signal generation", with high signal reconstruction efficiency and can truly restore the super-Gaussian vibration excitation characteristics of actual vehicles in service. It significantly improves the accuracy of vehicle structure vibration response characteristic analysis and fatigue life assessment, and is suitable for the structural vibration testing needs of high reliability fields such as automotive engineering. Attached Figure Description

[0030] Figure 1 It is the power spectral density type corresponding to the NAVMATP-9492 random vibration test specification;

[0031] Figure 2 This is a graph showing the variation of the kurtosis of a simulated time-domain signal with a given kurtosis.

[0032] Figure 3 This is a schematic diagram of a super-Gaussian signal generated by an amplitude-modulated signal and a Gaussian signal.

[0033] Figure 4 It is a graph of the fourth-order central moment value of u(t) calculated and simulated;

[0034] Figure 5 It is a flowchart illustrating the change of kurtosis of a time-domain signal with respect to a given kurtosis;

[0035] Figure 6 This is a flowchart of the calculation and simulation of the fourth-order central moment value of u(t). Detailed Implementation

[0036] The present invention will now be described in further detail with reference to the accompanying drawings and preferred embodiments. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0037] In this embodiment, the method for reconstructing and generating the super-Gaussian signal used in vehicle structure vibration testing is implemented by a vibration signal processing system. This system includes a signal acquisition module and a data processing terminal; the signal acquisition module and the data processing terminal can be directly connected via a data signal line or communicate via an industrial Ethernet network. The data processing terminal communicates with an external vibration test bench through a direct signal interface. The hardware parameters and functional configurations of each module are as follows:

[0038] Signal acquisition module: Includes an accelerometer and a spectrum analyzer. The accelerometer is used to acquire the raw vibration signal from vehicle road excitation, and the spectrum analyzer is used to convert the raw vibration signal into power spectral density data before transmitting it to the data processing terminal. The accelerometer has an acquisition accuracy of ±0.01g and a sampling frequency of ≥10kHz, suitable for acquiring raw vibration signals from vehicle road excitation; the spectrum analyzer has a resolution frequency range of 0.01Hz~5000Hz, and can convert the raw vibration signal into power spectral density (PSD) data that meets the experimental requirements before transmitting it to the data processing terminal.

[0039] Data processing terminal: An industrial computer equipped with signal processing algorithm programs, with a built-in gamma function calculation library and nonlinear equation solving module. It calls library functions to solve for the exponential parameter p. The reconstruction time for a single set of super-Gaussian signals is ≤0.5s. It also supports docking with signal acquisition modules and vibration test benches to realize data reception and signal output.

[0040] External vibration test bench: An electro-hydraulic servo vibration table is used to meet the testing needs of vehicle structures, such as vibration fatigue testing.

[0041] In this embodiment, the vehicle structure vibration test aims to assess the fatigue life of the automotive suspension structure, referring to the NAVMATP-9492 random vibration test specification, and the corresponding power spectral density type is as follows. Figure 1 As shown, target kurtosis The value range is 3.25 to 12 (covering the super-Gaussian vibration excitation characteristics of typical road conditions such as urban roads, highways, and off-road roads).

[0042] In a first aspect, a method for reconstructing and generating a super-Gaussian signal used in vehicle structural vibration testing is provided. This method is implemented by a vibration signal processing system, which includes a signal acquisition module and a data processing terminal. The signal acquisition module and the data processing terminal are communicatively connected, and the data processing terminal interacts with signals from an external vibration test bench. The method includes the following steps:

[0043] S1. The signal acquisition module acquires the power spectral density data of the vehicle road excitation and transmits the power spectral density data to the data processing terminal;

[0044] An accelerometer is installed at the connection point between the vehicle suspension and the body. It accompanies the test vehicle through real-world road testing, simultaneously collecting raw vibration signals generated by road excitation during vehicle movement. A spectrum analyzer receives the raw vibration signals transmitted by the accelerometer in real time and converts them into frequency-compliant signals through frequency domain analysis. Figure 1 The power spectral density (PSD) data for the NAVMAT spectrum shown covers the main vibration frequency band of automobiles from 10 Hz to 5000 Hz, and is consistent with... Figure 1 The frequency ranges are completely consistent, and the spectrum analyzer transmits the converted power spectral density data to the data processing terminal via industrial Ethernet.

[0045] S2. The data processing terminal generates a Gaussian vibration signal G(t) with a mean of 0 by inverse fast Fourier transform based on the received power spectral density data.

[0046] The data processing terminal receives the matched data transmitted by the spectrum analyzer via the industrial Ethernet interface. Figure 1 The power spectral density data is processed using the built-in Inverse Fast Fourier Transform (IFFT) algorithm to perform a fast frequency-to-time domain transformation, generating a Gaussian vibration signal G(t) with a mean of 0. This Gaussian vibration signal G(t) has a frequency range of 10Hz to 5000Hz and a duration of 300s, consistent with the signal acquisition duration of the actual vehicle road test, providing a basic signal source for subsequent amplitude modulation and super-Gaussian signal generation.

[0047] S3. Based on the target kurtosis required for vehicle structural vibration testing. The data processing terminal obtains the exponential parameter p by solving a preset nonlinear equation, wherein the nonlinear equation is: ;

[0048] in It is a gamma function;

[0049] In this embodiment, the target kurtosis is... The values ​​are successively set to 3.25, 3.5, 4.0...12.0, with a step size of 0.25. Figure 2 The figure shows the variation of the kurtosis of the simulated time-domain signal with a given kurtosis. Figure 5 This is a flowchart illustrating the change in kurtosis of a simulated time-domain signal relative to a given kurtosis; in this embodiment, the target kurtosis... The range of values ​​corresponds to Figure 2 The given kurtosis range for the middle horizontal axis. Figure 2 In the diagram, the black asterisk (*) represents the simulated kurtosis value obtained from the simulation based on the given kurtosis value, and the red line is the straight line of the function y=x. The data processing terminal calls the preset nonlinear equation solving module, and uses the built-in gamma function calculation library to quickly and accurately solve for the gamma function value, thereby obtaining the kurtosis values ​​for each target. The one-to-one corresponding exponential parameter p.

[0050] S4. The data processing terminal generates a low-frequency Gaussian vibration signal g(t) independent of the Gaussian vibration signal G(t), and g(t) has the same time length as G(t). Based on the obtained p value, the amplitude modulation signal u(t) is calculated and generated. ;

[0051] The data processing terminal generates a low-frequency Gaussian vibration signal g(t) independent of the Gaussian vibration signal G(t). The frequency range of this low-frequency Gaussian vibration signal g(t) is 0.01Hz to 10Hz, and the duration is set to 300s, which is exactly the same as the duration of the Gaussian vibration signal G(t), thus meeting the timing matching requirements of subsequent amplitude modulation. At the same time, the low-frequency range of 0.01Hz to 10Hz and the frequency range of 10Hz to 5000Hz of G(t) form frequency separation, effectively avoiding the decrease in kurtosis control accuracy caused by frequency aliasing.

[0052] S5. The data processing terminal multiplies the Gaussian vibration signal G(t) with the amplitude modulation signal u(t) to generate the target kurtosis that meets the requirements of vehicle structural vibration testing. The super-Gaussian signal Y(t), ;

[0053] The data processing terminal performs point-by-point multiplication of the Gaussian vibration signal G(t) with the amplitude modulation signal u(t) to generate a super-Gaussian signal Y(t). This super-Gaussian signal Y(t) covers a frequency range of 0.01Hz to 5000Hz, fully reproducing the full-frequency vibration excitation characteristics during actual vehicle operation, and its kurtosis value matches the target kurtosis required for vehicle structural vibration testing. .correspond Figure 2 The variation trends of the simulated time-domain signal kurtosis on the vertical axis and the given kurtosis on the horizontal axis show a high degree of consistency, fully demonstrating that the generated super-Gaussian signal can match the target kurtosis requirements and can realistically reproduce the super-Gaussian vibration excitation characteristics during the actual service process of automobiles.

[0054] S6. The data processing terminal transmits the generated super-Gaussian signal Y(t) to an external vibration test bench as the excitation signal required for vehicle structure vibration testing.

[0055] The data processing terminal transmits the super-Gaussian signal Y(t) generated in step S5 to an external vibration test bench via a signal interface, such as... Figure 3 The diagram shows a schematic of a super-Gaussian signal generated from an amplitude-modulated signal and a Gaussian signal. The vibration test bench uses the received super-Gaussian signal Y(t) as the excitation signal and drives the test bench surface to vibrate synchronously according to the time-domain characteristics of the signal, thereby realizing the super-Gaussian vibration test of the automotive suspension structure and providing test excitation for the vibration response characteristic analysis and fatigue life assessment of the suspension structure.

[0056] In a second aspect, a vibration signal processing system is provided, the system comprising a signal acquisition module and a data processing terminal, the signal acquisition module being communicatively connected to the data processing terminal, and the data processing terminal interacting with signals from an external vibration test bench;

[0057] The signal acquisition module is used to acquire power spectral density data of vehicle road excitation and transmit the power spectral density data to the data processing terminal.

[0058] The data processing terminal is configured to perform the following operations:

[0059] S11. Based on the received power spectral density data, a Gaussian vibration signal G(t) with a mean of 0 is generated by inverse fast Fourier transform;

[0060] S12. Based on the target kurtosis required for vehicle structural vibration testing. The data processing terminal obtains the exponential parameter p by solving a preset nonlinear equation, wherein the nonlinear equation is: ;

[0061] in It is a gamma function;

[0062] S13. Generate a low-frequency Gaussian vibration signal g(t) independent of the Gaussian vibration signal G(t), where g(t) and G(t) have the same time length. Calculate and generate the amplitude modulation signal u(t) based on the obtained p value. ;

[0063] S14. Multiply the Gaussian vibration signal G(t) with the amplitude modulation signal u(t) to generate the target kurtosis required for vehicle structural vibration testing. The super-Gaussian signal Y(t), ;

[0064] The data processing terminal transmits the generated super-Gaussian signal Y(t) to an external vibration test bench as the excitation signal required for vehicle structure vibration testing.

[0065] Nonlinear equations The derivation process:

[0066] Since the mean of the Gaussian signal G(t) is zero, the mean of the generated super-Gaussian signal Y(t) is also zero. G(t) and u(t) are independent, therefore the kurtosis of the super-Gaussian signal Y(t) is... for:

[0067] ;

[0068] in, It is the mean of the kurtosis of u(t).

[0069] The above formula can be written as equation (8);

[0070] .

[0071] Kurtosis is defined as the fourth center distance value of the signal. To obtain the desired target kurtosis... Therefore, we only need to focus on the fourth center distance value of u(t).

[0072] The m:th moment (m-th center distance value) of u(t) can be written as:

[0073] ;

[0074] Use the following equation for transformation: ;

[0075] Where Γ is the gamma function.

[0076] The general formula for the m-th moment of u(t) is:

[0077] ;

[0078] When m=4, we can conclude that Closed-form formula relating to p-value:

[0079] ;

[0080] ;

[0081] From equation (13), it can be seen that, given the target kurtosis The value of p in the closed-form formula can be solved using the following nonlinear equation:

[0082] .

[0083] Formula derivation and verification: such as Figure 4 The graph shown represents the calculated and simulated fourth-order central moment values ​​of u(t). Figure 6 This is a flowchart of the calculation and simulation of the fourth-order central moments of u(t), as shown below. Figure 4 The calculated value of the fourth-order central moment of the amplitude-modulated signal u(t) shown is in high agreement with the simulated value, proving that the exponential parameter p is related to the target kurtosis. Verify the correctness of the relation.

[0084] In this embodiment, the generated super-Gaussian signal Y(t) undergoes comprehensive time-frequency domain feature detection and is used as an excitation signal for vibration testing of the automotive suspension structure. The final implementation results are verified by referring to the attached figures:

[0085] like Figure 2 As shown, within the value range of 3.25 to 12, the kurtosis of the simulated time-domain signal does not deviate significantly from the given kurtosis.

[0086] The low-frequency Gaussian signal (0.01Hz to 10Hz) and the main Gaussian signal (10Hz to 5000Hz) are well separated, with no frequency aliasing.

[0087] The entire signal reconstruction process requires no manual intervention, meeting the real-time input requirements of the vibration test bench for excitation signals.

[0088] In summary, the super-Gaussian signal reconstruction generation method and system for vehicle structure vibration testing of the present invention avoids the external iterative loop of repeated statistical verification and adjustment of the generated signal to control kurtosis in the traditional amplitude modulation method. Instead, it directly determines the modulation parameter p by solving an equation with a clear analytical relationship with kurtosis. This equation itself can be solved quickly using efficient numerical methods, realizing efficient online reconstruction of the super-Gaussian signal. The system of the present invention realizes an integrated process of "data acquisition-signal generation", with high signal reconstruction efficiency and can realistically restore the super-Gaussian vibration excitation characteristics of actual vehicles in service. It significantly improves the accuracy of vehicle structure vibration response characteristic analysis and fatigue life assessment, and is suitable for the structural vibration testing needs of high reliability fields such as automotive engineering.

[0089] The above description is merely a specific embodiment of the present invention. Various examples and illustrations do not constitute a limitation on the substantive content of the present invention. Those skilled in the art can modify or transform the specific embodiments described above after reading the specification without departing from the essence and scope of the invention.

Claims

1. A method for reconstructing and generating a super-Gaussian signal used in vehicle structural vibration testing, implemented by a vibration signal processing system, the vibration signal processing system comprising a signal acquisition module and a data processing terminal; the signal acquisition module and the data processing terminal are communicatively connected, and the data processing terminal is communicatively connected to an external vibration test bench, characterized in that, The method includes the following steps: S1. The signal acquisition module acquires the power spectral density data of the vehicle road excitation and transmits the power spectral density data to the data processing terminal; S2. The data processing terminal generates a Gaussian vibration signal G(t) with a mean of 0 by inverse fast Fourier transform based on the received power spectral density data. S3. Based on the target kurtosis required for vehicle structural vibration testing. The data processing terminal obtains the exponential parameter p by solving a preset nonlinear equation, wherein the nonlinear equation is: ; in It is a gamma function; S4. The data processing terminal generates a low-frequency Gaussian vibration signal g(t) independent of the Gaussian vibration signal G(t), and g(t) has the same time length as G(t). Based on the obtained p value, an amplitude modulation signal u(t) is calculated and generated. ; S5. The data processing terminal multiplies the Gaussian vibration signal G(t) with the amplitude modulation signal u(t) to generate a target kurtosis that meets the requirements of vehicle structure vibration testing. The super-Gaussian signal Y(t), ; S6. The data processing terminal transmits the generated super-Gaussian signal Y(t) to an external vibration test bench as the excitation signal required for vehicle structure vibration testing.

2. The method according to claim 1, characterized in that, The signal acquisition module includes an accelerometer and a spectrum analyzer. The accelerometer acquires the original vibration signal of the vehicle road excitation, and the spectrum analyzer converts the original vibration signal into power spectral density data and transmits it to the data processing terminal.

3. The method according to claim 1, characterized in that, The data processing terminal is an industrial computer equipped with signal processing algorithm programs. It has a built-in gamma function calculation library and a nonlinear equation solving module, and calls library functions to solve for the exponential parameter p.

4. The method according to claim 1, characterized in that, The low-frequency Gaussian vibration signal g(t) has a frequency range of 0.01Hz to 10Hz, and the Gaussian vibration signal G(t) has a frequency range of 10Hz to 5000Hz, thus forming a frequency separation.

5. The method according to claim 1, characterized in that, The target kurtosis required for the vehicle structure vibration test The value range is 3.25 to 12.

6. A vibration signal processing system, characterized in that, The system includes a signal acquisition module and a data processing terminal. The signal acquisition module is communicatively connected to the data processing terminal, and the data processing terminal is communicatively connected to an external vibration test bench. The signal acquisition module is used to acquire power spectral density data of vehicle road excitation and transmit the power spectral density data to the data processing terminal. The data processing terminal is configured to perform the following operations: S11. Based on the received power spectral density data, a Gaussian vibration signal G(t) with a mean of 0 is generated by inverse fast Fourier transform; S12. Based on the target kurtosis required for vehicle structural vibration testing. The data processing terminal obtains the exponential parameter p by solving a preset nonlinear equation, wherein the nonlinear equation is: ; in It is a gamma function; S13. Generate a low-frequency Gaussian vibration signal g(t) independent of the Gaussian vibration signal G(t), where g(t) and G(t) have the same time length. Calculate and generate the amplitude modulation signal u(t) based on the obtained p value. ; S14. Multiply the Gaussian vibration signal G(t) with the amplitude modulation signal u(t) to generate the target kurtosis required for vehicle structural vibration testing. The super-Gaussian signal Y(t), ; The data processing terminal transmits the generated super-Gaussian signal Y(t) to an external vibration test bench as the excitation signal required for vehicle structure vibration testing.

7. The system according to claim 6, characterized in that, The signal acquisition module includes an accelerometer and a spectrum analyzer. The accelerometer is used to acquire the original vibration signal of the vehicle road excitation, and the spectrum analyzer is used to convert the original vibration signal into power spectral density data and then transmit it to the data processing terminal.

8. The system according to claim 6, characterized in that, The data processing terminal is an industrial computer equipped with signal processing algorithm programs. It has a built-in gamma function calculation library and a nonlinear equation solving module, and calls library functions to solve for the exponential parameter p.

9. The system according to claim 6, characterized in that, The low-frequency Gaussian vibration signal g(t) has a frequency range of 0.01Hz to 10Hz, and the Gaussian vibration signal G(t) has a frequency range of 10Hz to 5000Hz, thus forming a frequency separation.

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