Non-Gaussian random vibration signal generation method and system for controlling kurtosis and peak factor

Abstract

The invention discloses a non-Gaussian random vibration signal generation method and system for controlling kurtosis and a peak factor. The method comprises the following steps: setting a reference condition for generating a non-Gaussian random vibration signal; a stable non-Gaussian random vibration signal model is constructed, a non-Gaussian random vibration signal is generated through the zero-mean Gaussian random signal and an impact signal, and the impact signal is used for providing a plurality of peaks with different amplitudes for the non-Gaussian random vibration signal; based on a reference condition, through control cooperation iteration of kurtosis and a peak factor, various parameters in the model are solved, and an impact signal is obtained; correcting the power spectral density of the zero-mean Gaussian random signal based on the power spectral density of the impact signal; and synthesizing the zero-mean Gaussian random signal and the impact signal to generate a non-Gaussian random vibration signal. The invention innovatively provides a non-Gaussian random vibration signal generation scheme capable of accurately controlling the kurtosis and the peak factor, and has important practical significance for improving the reliability analysis and test level of an engineering system.

Description

Technical Field

[0001] The present invention belongs to the technical field of dynamic environment testing, and particularly relates to a method and system for generating non-Gaussian random vibration signals by controlling kurtosis and peak factor. Background Art

[0002] In the engineering field, the characteristics of vibration signals have an important impact on the performance and reliability of equipment. Although the traditional Gaussian random vibration signal generation method is widely used, it assumes that the vibration signal follows a normal distribution and cannot accurately simulate the complex non-Gaussian vibration environment in actual engineering. Many actual vibration scenarios, such as mechanical shock and pulse interference of electronic devices, often have significant non-Gaussian characteristics, such as higher kurtosis and different peak factors. Kurtosis is an index to measure the strength of pulse components in a signal, while the peak factor reflects the ratio of the signal peak value to the effective value. Both are important parameters to characterize non-Gaussian characteristics. However, there is currently a lack of a non-Gaussian random vibration signal generation technology that can effectively control kurtosis and peak factor. The absence of this technology makes it difficult to accurately simulate the real working conditions in vibration testing, fault diagnosis, and equipment reliability assessment, thereby affecting the accurate evaluation and optimization of equipment performance. Therefore, developing a non-Gaussian random vibration signal generation technology that can precisely control kurtosis and peak factor has important practical significance for improving the reliability analysis and testing level of engineering systems. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for generating a stationary non-Gaussian vibration signal that simultaneously specifies (controls) kurtosis and peak factor in view of the problems existing in the above-mentioned prior art.

[0004] To achieve the object of the present invention, the technical solution is as follows: On the one hand, a method for generating a non-Gaussian random vibration signal by controlling kurtosis and peak factor is provided. The method includes the following steps:

[0005] Step 1, set the reference conditions for generating non-Gaussian random vibration signals, including the constraints on kurtosis and peak factor;

[0006] Step 2, construct a stationary non-Gaussian random vibration signal model:

[0007]

[0008] where t represents the time at moment t, represents the non-Gaussian random vibration signal, represents the impact signal, which is used to provide m spikes with different amplitudes A for the generated non-Gaussian random vibration signal, represents a zero-mean Gaussian random signal with a standard deviation of ;

[0009] Step 3, based on the reference conditions set in Step 1, by controlling the kurtosis and peak factor and cooperating with the iterative update method, solve each parameter in the model of Step 2 to obtain ;

[0010] Step 4, based on the power spectral density of to correct the power spectral density of and obtain the final

[0011] Step 5, based on the model in Step 2, synthesize obtained in Step 3 and obtained in Step 4, and finally generate a non-Gaussian random vibration signal .

[0012] Furthermore, the reference conditions set in Step 1 include: defining the reference power spectral density , the reference kurtosis and the reference peak factor , as well as the tolerance limits of the kurtosis and peak factor and .

[0013] Furthermore, the impulse signal in Step 2 is specifically:

[0014]

[0015] wherein, is the amplitude of the i-th spike, m is the number of spikes, represents the unit impulse function, represents the moment corresponding to the i-th spike;

[0016] is used to control the positive and negative directions of the spike and satisfies:

[0017]

[0018] wherein, represents the probability distribution, p is the probability value, and the value range is [0,1]; k1 = 1 indicates that the amplitude of the spike is positive, and k1 = -1 indicates that the amplitude of the spike is negative.

[0019] Furthermore, Step 3 specifically includes:

[0020] Step 3-1, set the number of iterations k and initialize it to 0;

[0021] Step 3-2, initialize the parameters:

[0022] ,

[0023] Among them, and respectively represent the initialized parameters m and A, , and are respectively the upper limit frequency and the lower limit frequency of the reference power spectral density, represents the reference power spectral density related to the frequency f;

[0024] Meanwhile, calculate:

[0025]

[0026]

[0027] Among them, N is the length of the non-Gaussian random vibration signal, represents the initialized ;

[0028] Step 3-3, calculate the value of A, specifically including:

[0029] Judge whether the following formula holds:

[0030]

[0031] If it does not hold, then execute Step 3-4;

[0032] If it holds, then repeat the following iterative process until the above formula does not hold, output the final value of A, and then execute Step 3-4;

[0033]

[0034]

[0035]

[0036] Among them, are respectively the A values of the (k + 1)-th iteration and the k-th iteration, is the value of the k-th iteration, is the peak factor of the non-Gaussian random vibration signal of the k-th iteration, is the peak factor convergence coefficient, is the non-Gaussian random vibration signal the quantile of the probability density function of the absolute value, expressed as:

[0037]

[0038] Step 3-4, calculate the value of m, specifically including:

[0039] Determine whether the following formula holds:

[0040]

[0041] If it does not hold, execute steps 3 - 5;

[0042] If it holds, repeat the following iterative process until the above formula does not hold, and output the final m value and value;

[0043] If , execute ;

[0044] If , execute ;

[0045]

[0046]

[0047] Among them, is the kurtosis of the non - Gaussian random vibration signal in the k - th iteration, are the m values in the (k + 1) - th iteration and the k - th iteration respectively, is the q value in the k - th iteration;

[0048] Steps 3 - 5: Based on the A value obtained in step 3 - 3 and the m value obtained in step 3 - 4, obtain the final .

[0049] Furthermore, during the iterative process in step 3 - 4, it also includes executing:

[0050] If , execute and .

[0051] Furthermore, step 4 specifically includes:

[0052] Step 4 - 1: Calculate the power spectral density of obtained in step 3;

[0053] Step 4 - 2: Based on the reference power spectral density , obtain the power spectral density of :

[0054]

[0055] Step 4 - 3: Use the power spectral density to filter , and obtain the final .

[0056] On the other hand, a non-Gaussian random vibration signal generation system for controlling kurtosis and crest factor is provided, and the system includes:

[0057] A first module for setting reference conditions for generating a non-Gaussian random vibration signal, including constraints on kurtosis and crest factor;

[0058] A second module for constructing a stationary non-Gaussian random vibration signal model:

[0059]

[0060] wherein, t represents the moment of t, represents a non-Gaussian random vibration signal, represents an impact signal, and this impact signal is used to provide m spikes with different amplitudes A for the generated non-Gaussian random vibration signal, represents a zero-mean Gaussian random signal, and its standard deviation is ;

[0061] A third module for solving each parameter in the stationary non-Gaussian random vibration signal model based on the reference conditions set by the first module through the control of kurtosis and crest factor in cooperation with the iterative update method to obtain ;

[0062] A fourth module for correcting the power spectral density of based on the power spectral density of to obtain the final ;

[0063] A fifth module for synthesizing obtained by the third module and obtained by the fourth module based on the stationary non-Gaussian random vibration signal model, and finally generating a non-Gaussian random vibration signal .

[0064] On the other hand, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the non-Gaussian random vibration signal generation method for controlling kurtosis and crest factor is implemented.

[0065] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the non-Gaussian random vibration signal generation method for controlling kurtosis and crest factor is implemented.

[0066] Compared with the prior art, the significant advantages of the present invention are:

[0067] (1) The present invention innovatively constructs an impact signal plus stationary Gaussian random signal model for the first time, which can generate stationary non-Gaussian random vibration signals with specified power spectral density, kurtosis, and peak factor.

[0068] (2) The present invention proposes to calculate the peak factor of the signal by using the quantile of the probability density function of the stationary random vibration signal, realizing the approximate calculation of the peak factor by the theoretical formula.

[0069] (3) The present invention proposes to use a fast iterative method to obtain the three synthesis parameters required for the stationary non-Gaussian random vibration signal model, and the iterative process is efficient and easy to control.

[0070] The present invention will be further described in detail below with reference to the accompanying drawings. Description of the Drawings

[0071] Figure 1 It is a flowchart of a method for generating non-Gaussian random vibration signals by controlling kurtosis and peak factor in an embodiment.

[0072] Figure 2 It is the power spectral density diagram of the stationary non-Gaussian random vibration signal generated by the present invention in an embodiment.

[0073] Figure 3 It is the time-domain segment of the stationary non-Gaussian random vibration signal generated by the present invention in an embodiment, where the reference kurtosis of the signal is 5 and the reference peak factor is 6.

[0074] Figure 4 It is the time-domain segment of the stationary non-Gaussian random vibration signal generated by the present invention in an embodiment, where the reference kurtosis of the signal is 5 and the reference peak factor is 8.

[0075] Figure 5 It is the time-domain segment of the stationary non-Gaussian random vibration signal generated by the present invention in an embodiment, where the reference kurtosis of the signal is 5 and the reference peak factor is 10. Detailed Embodiments

[0076] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0077] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, the directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0078] In addition, if there are descriptions such as "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between the various embodiments may be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0079] In one embodiment, in combination with Figure 1 , a method for generating a non-Gaussian random vibration signal for controlling kurtosis and crest factor is provided, which can control both the crest factor and kurtosis of the signal. The method includes the following steps:

[0080] Step 1, set the reference conditions for generating the non-Gaussian random vibration signal, including the constraints on kurtosis and crest factor;

[0081] Step 2, construct a stationary non-Gaussian random vibration signal model:

[0082]

[0083] where t represents the time at t, represents the non-Gaussian random vibration signal, represents the impact signal, which is used to provide m spikes with different amplitudes A for the generated non-Gaussian random vibration signal, represents the zero-mean Gaussian random signal with a standard deviation of ;

[0084] Step 3, based on the reference conditions set in Step 1, through the control cooperation iterative update method for kurtosis and crest factor, solve the parameters in the model of Step 2 to obtain ;

[0085] Step 4, based on the power spectral density of correct the power spectral density of to obtain the final ;

[0086] Step 5, based on the model of Step 2, synthesize obtained in Step 3 and obtained in Step 4, and finally generate the non-Gaussian random vibration signal .

[0087] Furthermore, in one of the embodiments, the reference conditions set in Step 1 include: defining the reference power spectral density , with reference to kurtosis and reference crest factor , as well as the tolerance limits of kurtosis and crest factor and .

[0088] Furthermore, in one of the embodiments, the impact signal in step 2 is specifically:

[0089]

[0090] In the formula, is the amplitude of the i-th spike, m is the number of spikes, represents the unit impulse function, represents the time corresponding to the i-th spike;

[0091] is used to control the positive and negative directions of the spike and satisfies:

[0092]

[0093] Among them, represents the probability distribution, p is the probability value, and the value range is [0, 1]; k1 = 1 indicates that the amplitude of the spike is positive, and k1 = -1 indicates that the amplitude of the spike is negative.

[0094] Preferably here, the probability value p takes 0.5.

[0095] Furthermore, in one of the embodiments, step 3 specifically includes:

[0096] Step 3-1, set the number of iterations k and initialize it to 0;

[0097] Step 3-2, initialize the parameters:

[0098] ,

[0099] Among them, 、 respectively represent the initialized parameters m and A, , and are respectively the upper limit frequency and the lower limit frequency of the reference power spectral density, represents the reference power spectral density related to the frequency f;

[0100] At the same time, calculate:

[0101]

[0102]

[0103] where N is the length of the non-Gaussian random vibration signal, indicating initialization ;

[0104] Step 3-3: Calculate the value of A, specifically including:

[0105] Judge whether the following formula holds:

[0106]

[0107] If not, execute Step 3-4;

[0108] If it holds, repeat the following iterative process until the above formula does not hold, output the final value of A, and then execute Step 3-4;

[0109]

[0110]

[0111]

[0112] where are the values of A in the (k + 1)-th iteration and the k-th iteration respectively, is the value in the k-th iteration, is the peak factor of the non-Gaussian random vibration signal in the k-th iteration, is the peak factor convergence coefficient, is the non-Gaussian random vibration signal the quantile of the probability density function of the absolute value, expressed as:

[0113]

[0114] Step 3-4: Calculate the value of m, specifically including:

[0115] Judge whether the following formula holds:

[0116]

[0117] If not, execute Step 3-5;

[0118] If it holds, repeat the following iterative process until the above formula does not hold, output the final value of m and value;

[0119] If , execute ;

[0120] If , execute ;

[0121]

[0122]

[0123] wherein, is the kurtosis of the non-Gaussian random vibration signal at the k-th iteration, are the m values at the (k + 1)-th iteration and the k-th iteration respectively, is the q value at the k-th iteration;

[0124] Step 3-5: Based on the A value obtained in Step 3-3 and the m value obtained in Step 3-4, obtain the final .

[0125] Preferably, in some embodiments, during the iteration process in Step 3-4, it further includes finally performing:

[0126] If , perform and .

[0127] Furthermore, in one of the embodiments, Step 4 specifically includes:

[0128] Step 4-1: Calculate the power spectral density of obtained in Step 3;

[0129] Step 4-2: Based on the reference power spectral density , obtain the power spectral density of :

[0130]

[0131] Step 4-3: Filter using the power spectral density to obtain the final .

[0132] In one embodiment, a non-Gaussian random vibration signal generation system for controlling kurtosis and peak factor is provided, and the system includes:

[0133] A first module for setting reference conditions for generating a non-Gaussian random vibration signal, including constraints on kurtosis and peak factor;

[0134] A second module for constructing a stationary non-Gaussian random vibration signal model:

[0135]

[0136] In the formula, \(t\) represents the time \(t\), represents a non-Gaussian random vibration signal, represents an impact signal, which is used to provide spikes with \(m\) different amplitudes \(A\) for the generated non-Gaussian random vibration signal, represents a zero-mean Gaussian random signal with a standard deviation of ;

[0137] The third module is used to solve the parameters in the stationary non-Gaussian random vibration signal model through the control of kurtosis and peak factor in cooperation with the iterative update method based on the reference conditions set by the first module, and obtain ;

[0138] The fourth module is used to correct the power spectral density of based on the power spectral density of to obtain the final ;

[0139] The fifth module is used to synthesize the obtained by the third module and the obtained by the fourth module based on the stationary non-Gaussian random vibration signal model, and finally generate the non-Gaussian random vibration signal .

[0140] For the specific limitations of the non-Gaussian random vibration signal generation system for controlling kurtosis and peak factor, reference can be made to the limitations of the non-Gaussian random vibration signal generation method for controlling kurtosis and peak factor in the above text, which will not be elaborated here. Each module in the above non-Gaussian random vibration signal generation system for controlling kurtosis and peak factor can be implemented in whole or in part by software, hardware and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0141] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it realizes:

[0142] Step 1, set the reference conditions for generating non-Gaussian random vibration signals, including the constraints on kurtosis and peak factor;

[0143] Step 2, construct a stationary non-Gaussian random vibration signal model:

[0144]

[0145] In the formula, \(t\) represents the time \(t\), represents a non-Gaussian random vibration signal, represents an impact signal, which is used to provide spikes with m different amplitudes A for the generated non-Gaussian random vibration signal. represents a zero-mean Gaussian random signal with a standard deviation of ;

[0146] Step 3: Based on the reference conditions set in Step 1, by controlling the kurtosis and peak factor and cooperating with the iterative update method, solve each parameter in the model of Step 2 to obtain ;

[0147] Step 4: Based on the power spectral density of, correct the power spectral density of to obtain the final ;

[0148] Step 5: Based on the model of Step 2, synthesize the obtained in Step 3 and the obtained in Step 4, and finally generate a non-Gaussian random vibration signal .

[0149] For the specific limitations of each step, reference can be made to the limitations of the non-Gaussian random vibration signal generation method for controlling kurtosis and peak factor in the above text, which will not be elaborated here.

[0150] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it realizes:

[0151] Step 1: Set the reference conditions for generating a non-Gaussian random vibration signal, including the constraints on kurtosis and peak factor;

[0152] Step 2: Construct a stationary non-Gaussian random vibration signal model:

[0153]

[0154] In the formula, t represents the t-th moment, represents a non-Gaussian random vibration signal, represents an impact signal, which is used to provide spikes with m different amplitudes A for the generated non-Gaussian random vibration signal. represents a zero-mean Gaussian random signal with a standard deviation of ;

[0155] Step 3: Based on the reference conditions set in Step 1, by controlling the kurtosis and peak factor and cooperating with the iterative update method, solve each parameter in the model of Step 2 to obtain ;

[0156] Step 4: Based on the power spectral density of, correct the The power spectral density of, to obtain the final ;

[0157] Step 5, based on the model in Step 2, synthesize the obtained in Step 3 and the obtained in Step 4, and finally generate a non-Gaussian random vibration signal .

[0158] For the specific limitations of each step, reference can be made to the limitations of the non-Gaussian random vibration signal generation method for controlling kurtosis and peak factor in the above text, which will not be elaborated here.

[0159] As a specific example, the present invention is further verified and described.

[0160] In this example, the reference spectrum is set as a trapezoidal spectrum, as shown by the red dashed line in Figure 2 , with the upper and lower limit frequencies being 10 Hz and 1000 Hz respectively. The reference kurtosis is set to 5, and the reference peak factor is set in 3 cases, which are 6, 8, and 10 respectively.

[0161] The effects of the stationary non-Gaussian random vibration signals synthesized by the method of the present invention are respectively as shown in Figures 2 to 5 . Figure 2 The power spectral density of the synthesized non-Gaussian random vibration signal is given, and it can be seen that the power spectral density of the synthesized signal completely fits on the corresponding reference value. Figures 3 to 5 The segments of the stationary non-Gaussian random vibration signals with kurtosis of 5 but peak factors of 6, 8, and 10 respectively are shown. It can be seen that although the power spectral density and kurtosis of the signals are the same, the time-domain characteristics of the generated stationary non-Gaussian random vibration signals are quite different, that is, the larger the peak factor, the sparser the high peaks of the signal but the larger the amplitude.

[0162] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for generating non-Gaussian random vibration signals that control kurtosis and crest factor, characterized in that, The method includes the following steps: Step 1: Set the reference conditions for generating non-Gaussian random vibration signals, including constraints on kurtosis and peak factor; Step 2: Construct a stationary non-Gaussian random vibration signal model: ； where \(t\) represents the time at moment \(t\), represents a non-Gaussian random vibration signal, represents an impact signal, which is used to provide spikes with \(m\) different amplitudes \(A\) for the generated non-Gaussian random vibration signal, represents a zero-mean Gaussian random signal with a standard deviation of ; Step 3, based on the reference conditions set in Step 1, by controlling the kurtosis and peak factor and cooperating with the iterative update method, solve each parameter in the model of Step 2 to obtain ; Step 4, based on the power spectral density correction of the power spectral density, obtain the final ; Step 5, based on the model in Step 2, synthesize the obtained in Step 3 and the obtained in Step 4, and finally generate a non-Gaussian random vibration signal .

2. The method for generating a non-Gaussian random vibration signal for controlling kurtosis and peak factor according to claim 1, wherein The reference conditions set in step 1 include: defining a reference power spectral density , a reference kurtosis and a reference peak factor , as well as tolerance limits for kurtosis and peak factor and .

3. The method for generating a non-Gaussian random vibration signal for controlling kurtosis and crest factor according to claim 1, wherein The impact signal in Step 2 Specifically: ； In the formula, is the amplitude of the i-th peak, m is the number of peaks, represents the unit impulse function, represents the time corresponding to the i-th peak; For controlling the positive and negative directions of the spike and satisfying: ； Among them, represents a probability distribution, p is a probability value, and its value range is [0, 1]; k1 = 1 indicates that the amplitude of the peak is positive, and k1 = -1 indicates that the amplitude of the peak is negative.

4. The method for generating a non-Gaussian random vibration signal for controlling kurtosis and crest factor according to claim 3, characterized in that, The probability value p is taken as 0.

5.

5. The method for generating a non-Gaussian random vibration signal for controlling kurtosis and crest factor according to claim 2 or 3, characterized in that Step 3 specifically includes: Step 3-1: Set the number of iterations k and initialize it to 0; Step 3-2: Initialize the parameters: ， ； Among them, and respectively represent the initialized parameters m and A, , and are respectively the upper limit frequency and the lower limit frequency of the reference power spectral density, represents the reference power spectral density related to the frequency f; Meanwhile, calculate: ； ； where N is the length of the non-Gaussian random vibration signal, indicating initialization ; Step 3-3: Calculate the value of A, specifically including: Judge whether the following formula holds: ； If it does not hold, then execute Step 3-4; If it holds, then repeat the following iterative process until the above formula does not hold, output the final value of A, and then execute Step 3-4; ； ； ； Among them, are the A values of the (k + 1)-th iteration and the k-th iteration respectively, is the value of the k-th iteration, is the peak factor of the non-Gaussian random vibration signal of the k-th iteration, is the peak factor convergence coefficient, is the quantile of the probability density function of the absolute value of the non-Gaussian random vibration signal, expressed as: ； Step 3-4: Calculate the value of m, specifically including: Judge whether the following formula holds: ； If it does not hold, then execute Step 3-5; If it holds, repeat the following iterative process until the above formula does not hold, and output the final value of m and value; If , execute ; If , execute ; ； ； Among them, is the kurtosis of the non-Gaussian random vibration signal at the k-th iteration, are the m values at the (k + 1)-th iteration and the k-th iteration respectively, is the q value at the k-th iteration; Step 3-5: Based on the A value obtained in Step 3-3 and the m value obtained in Step 3-4, obtain the final .

6. The method for generating a non-Gaussian random vibration signal for controlling kurtosis and peak factor according to claim 5, characterized in that In the iterative process of Step 3-4, it also includes executing at the end: If , execute and .

7. The method for generating a non-Gaussian random vibration signal for controlling kurtosis and crest factor according to claim 5, characterized in that, Step 4 specifically includes: Step 4-1, calculate the power spectral density of obtained in Step 3 ; Step 4-2, based on the reference power spectral density , obtain 's power spectral density : ； Step 4-3, using the power spectral density to perform filtering to obtain the final .

8. A non-Gaussian random vibration signal generation system for controlling kurtosis and peak factor based on the method according to any one of claims 1 to 7, characterized in that, The system includes: The first module is used to set the reference conditions for generating non-Gaussian random vibration signals, including constraints on kurtosis and peak factor; The second module is used to construct a stationary non-Gaussian random vibration signal model: ； where \(t\) represents the time at moment \(t\), represents a non-Gaussian random vibration signal, represents an impact signal, which is used to provide \(m\) spikes with different amplitudes \(A\) for the generated non-Gaussian random vibration signal, represents a zero-mean Gaussian random signal with a standard deviation of ; The third module is used to solve each parameter in the stationary non-Gaussian random vibration signal model through the control of kurtosis and peak factor and the iterative update method based on the reference conditions set by the first module, and obtain ; The fourth module is used to obtain the final by correcting the power spectral density of and the power spectral density of ; The fifth module is used to synthesize, based on the stationary non-Gaussian random vibration signal model, what is obtained by the third module and what is obtained by the fourth module , and finally generate a non-Gaussian random vibration signal .

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method described in any one of claims 1 to 7.

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