Method and device for calculating characteristic frequency of dynamic response data of railway vehicle

Abstract

The invention discloses a characteristic frequency calculation method and device for dynamic response data of a railway vehicle. The method comprises the following steps: acquiring the dynamic response data of the railway vehicle; decomposing the railway vehicle dynamic response data by adopting an adaptive noise complete set empirical mode decomposition (CEEMDAN) method to obtain a plurality of signal components; calculating a root mean square value of each signal component; determining the signal component with the maximum root-mean-square value as a feature component; performing time-frequency analysis on the characteristic components by adopting a Wigner-Wiener distribution WVD method, and calculating a marginal spectrum according to a time-frequency analysis result; and determining the frequency corresponding to the maximum marginal spectrum value as the characteristic frequency of the dynamic response data of the railway vehicle. According to the method, the characteristic frequency of the dynamic response data of the railway vehicle can be efficiently and accurately calculated.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed railway maintenance engineering, and in particular to a method and device for calculating characteristic frequency of dynamic response data of railway vehicles. Background Art

[0002] With the development and maturity of technologies such as 5G, big data, and the Internet, dynamic detection methods based on high-speed comprehensive inspection trains are playing an increasingly important role in railway infrastructure status monitoring. Mining the characteristics of massive dynamic detection data is of great significance for understanding the vibration laws of high-speed EMUs and conducting auxiliary evaluation of track status based on vehicle dynamic response. It can be foreseen that dynamic detection will play an irreplaceable and important role in the automated, intelligent, efficient, and precise detection and monitoring of high-speed railway infrastructure in the future. At present, the frequency domain characteristics of vibration at various locations of railway vehicles have not been effectively evaluated, and there is a lack of effective solutions for calculating the characteristic frequencies of dynamic response data of railway vehicles. Summary of the invention

[0003] An embodiment of the present invention provides a method for calculating characteristic frequency of railway vehicle dynamic response data, which is used to efficiently and accurately calculate the characteristic frequency of railway vehicle dynamic response data. The method includes:

[0004] Obtaining dynamic response data of railway vehicles;

[0005] The CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) method is used to decompose the dynamic response data of railway vehicles to obtain multiple signal components.

[0006] Calculate the RMS value of each signal component;

[0007] The signal component with the largest RMS value is determined as the characteristic component;

[0008] The WVD (Wigner-Ville Distribution) method is used to perform time-frequency analysis on the characteristic components, and the marginal spectrum is calculated based on the time-frequency analysis results;

[0009] The frequency corresponding to the maximum value of the marginal spectrum is determined as the characteristic frequency of the dynamic response data of the railway vehicle.

[0010] In another embodiment of the present invention, a device for calculating characteristic frequency of dynamic response data of a railway vehicle is provided, which is used to efficiently and accurately calculate the characteristic frequency of the dynamic response data of the railway vehicle, and the device comprises:

[0011] A data acquisition module, used to acquire dynamic response data of railway vehicles;

[0012] A signal decomposition module is used to decompose the dynamic response data of railway vehicles using the CEEMDAN method to obtain multiple signal components;

[0013] A signal analysis module for calculating the root mean square value of each signal component;

[0014] A characteristic component extraction module, used for determining the signal component with the largest root mean square value as the characteristic component;

[0015] The time-frequency analysis module is used to perform time-frequency analysis on the characteristic components using the WVD method and calculate the marginal spectrum based on the time-frequency analysis results;

[0016] The characteristic frequency extraction module is used to determine the frequency corresponding to the maximum value of the marginal spectrum as the characteristic frequency of the dynamic response data of the railway vehicle.

[0017] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the characteristic frequency calculation method of the dynamic response data of the railway vehicle when executing the computer program.

[0018] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the characteristic frequency calculation method of the dynamic response data of a railway vehicle is implemented.

[0019] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the characteristic frequency calculation method of the dynamic response data of a railway vehicle is implemented.

[0020] In an embodiment of the present invention, the dynamic response data of a railway vehicle is acquired; the dynamic response data of the railway vehicle is decomposed by using the CEEMDAN method to obtain a plurality of signal components; the root mean square value of each signal component is calculated; the signal component with the largest root mean square value is determined as a characteristic component; the WVD method is used to perform time-frequency analysis on the characteristic component, and the marginal spectrum is calculated according to the time-frequency analysis result; the frequency corresponding to the maximum value of the marginal spectrum is determined as the characteristic frequency of the dynamic response data of the railway vehicle, so that the characteristic frequency of the dynamic response data of the railway vehicle can be calculated efficiently and accurately, thereby evaluating the frequency domain characteristics of the vibration of the railway vehicle, which is helpful to improve the dynamic detection efficiency of the railway vehicle and has low dependence on manual experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0022] Figure 1 Schematic diagram of a method for calculating characteristic frequency of dynamic response data of a railway vehicle in an embodiment of the present invention;

[0023] Figure 2 This is an example diagram of the dynamic response data of a railway vehicle in an embodiment of the present invention being a periodic vehicle body lateral acceleration sequence;

[0024] Figure 3 This is an example diagram of decomposing railway vehicle dynamic response data into multiple signal components in an embodiment of the present invention;

[0025] Figure 4 This is an example diagram of a time-frequency distribution diagram obtained by performing WVD analysis on a characteristic component in an embodiment of the present invention;

[0026] Figure 5 This is an example diagram of calculating the marginal spectrum according to the time-frequency analysis results in an embodiment of the present invention;

[0027] Figure 6 Schematic diagram of a device for calculating characteristic frequency of dynamic response data of a railway vehicle in an embodiment of the present invention;

[0028] Figure 7 Schematic diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0030] In order to effectively evaluate the frequency domain characteristics of railway vehicle vibration, an embodiment of the present invention provides a method for calculating the characteristic frequency of railway vehicle dynamic response data. Figure 1 FIG. 1 is a schematic diagram of a method for calculating characteristic frequency of dynamic response data of a railway vehicle according to an embodiment of the present invention. Figure 1 As shown, the method may include:

[0031] Step 101, obtaining dynamic response data of railway vehicles;

[0032] Step 102, using the CEEMDAN method to decompose the dynamic response data of the railway vehicle to obtain multiple signal components;

[0033] Step 103, calculating the root mean square value of each signal component;

[0034] Step 104: determine the signal component with the largest RMS value as the characteristic component;

[0035] Step 105: using the WVD method to perform time-frequency analysis on the characteristic components, and calculating the marginal spectrum according to the time-frequency analysis results;

[0036] Step 106: Determine the frequency corresponding to the maximum value of the marginal spectrum as the characteristic frequency of the dynamic response data of the railway vehicle.

[0037] Depend on Figure 1 It can be seen from the shown process that the characteristic frequency calculation method of the railway vehicle dynamic response data in the embodiment of the present invention is to calculate the characteristic frequency of the railway vehicle dynamic response data by combining two advanced time-frequency methods, CEEMDAN and WVD, so as to provide an effective solution for the frequency domain characteristic analysis of vibration at positions such as axle boxes, frames, and car bodies, thereby efficiently and accurately evaluating the frequency domain characteristics of vibration at various positions of the railway vehicle.

[0038] In the specific implementation, the dynamic response data of the railway vehicle is first obtained. In one embodiment, the dynamic response data of the railway vehicle can be: the dynamic response data at a certain periodic axle box, frame, car body, etc. For example, the original waveform A(x) of the periodic car body lateral acceleration signal, where x is the distance between the starting point of the sensor on the high-speed comprehensive detection train and the current position of the sensor. Figure 2 This is an example diagram of the railway vehicle dynamic response data being a periodic vehicle body lateral acceleration sequence in an embodiment of the present invention.

[0039] After obtaining the dynamic response data of railway vehicles, the CEEMDAN method is used to decompose the dynamic response data of railway vehicles to obtain multiple signal components. Figure 3 FIG. 1 is an example diagram of decomposing the dynamic response data of a railway vehicle into multiple signal components in an embodiment of the present invention. Figure 3 As shown, Figure 2 The periodic vehicle lateral acceleration sequence is decomposed to obtain the CEEMDAN result of the periodic vehicle lateral acceleration sequence.

[0040] In the embodiment of the present invention, the CEEMDAN method is used to decompose the dynamic response data of railway vehicles, considering that: the adaptive decomposition time-frequency analysis method mainly includes: EMD (Empirical Mode Decomposition), EEMD (Ensemble Empirical Mode Decomposition), CEEMD (Complete Ensemble Empirical Mode Decomposition), and CEEMDAN. EMD can effectively decompose complex signals into a finite number of IMFs (Intrinsic Mode Functions), and each decomposed IMF contains local characteristic signals of different time scales of the original signal. However, EMD has the problem of modal aliasing. In order to solve this problem, EEMD achieves stable decomposition of the signal by adding a series of white noises to the original signal. However, EEMD still has the problem that the white noise cannot be completely offset after reconstruction, and the noise is too large after addition. CEEMD is an extension of EEMD, which further improves the stability and accuracy of decomposition by using positive and negative paired noises. CEEMDAN is an improved method of CEEMD, which uses adaptive noise to further improve the decomposition accuracy. Therefore, CEEMDAN with the highest calculation accuracy is selected to perform frequency adaptive decomposition of the dynamic response data of railway vehicles to obtain signal components with different frequency components.

[0041] In one embodiment, using the CEEMDAN method to decompose the dynamic response data of the railway vehicle to obtain multiple signal components may include: using the CEEMDAN method to decompose the dynamic response data of the railway vehicle according to a preset total number of noise additions and a signal-to-noise ratio to obtain multiple signal components. Specifically, for example, a white noise sequence that obeys a standard normal distribution may be added to the dynamic response data of the railway vehicle according to a preset total number of noise additions and a signal-to-noise ratio.

[0042] For example, the steps of CEEMDAN for the dynamic response data A(x) of a railway vehicle are as follows:

[0043] (1) Set the total number of noise additions to N;

[0044] (2) Using the EMD algorithm, we obtain K IMFs for each of the N added original white noise sequences;

[0045] (3) As shown in formula (1), construct the pth new signal for solving the first signal component of the original data A(x) Where p = 1, 2, ..., N, and N new signals are obtained by adding a white noise sequence that obeys a standard normal distribution to the original data.

[0046]

[0047] Where A(x) is the original railway vehicle dynamic response data; w p (x) is the p-th added white noise sequence that obeys the standard normal distribution; ε0 is the signal-to-noise ratio, which is a constant; E1(.) is the first IMF generated by EMD.

[0048] (4) Based on the newly generated N signals According to formulas (2) and (3), the first signal component C1(x) and the residual term r1 of the original data A(x) are obtained;

[0049]

[0050] r1(x)=A(x)-C1(x) (3)

[0051] Where, M(.) is the local mean function generated by the EMD algorithm; N is the total number of times noise is added; is the pth new signal generated when a white noise sequence is added to A(x); C1(x) is the first signal component of A(x); r1(x) is the first decomposition residual.

[0052] (5) According to formula (4), construct the pth new signal for solving the mth signal component of A(x) Where m = 2, 3, …, K, and eventually N new signals are obtained.

[0053]

[0054] In the formula, r m-1 (x) is the m-1th decomposition residual; E m () is the mth IMF generated by the EMD algorithm.

[0055] (6) Based on the newly generated N signals According to formulas (5) and (6), calculate the mth signal component C of A(x): m (x) and the residual term r m ;

[0056]

[0057] r m (x) = r m-1 (x)-C m (x) (6)

[0058] In the formula, is the pth new signal generated by adding a white noise sequence to the m-1th decomposition residual; r m-1 (x) is the m-1th decomposition residual; C m (x) is the mth signal component of A(x); r m (x) is the residual term of the mth decomposition.

[0059] (7) The original railway vehicle dynamic response data is finally automatically decomposed into K signal components and a residual term.

[0060]

[0061] In the formula, C n (x) is the nth signal component; R(x) is the residual term; K is the total number of signal components.

[0062] It should be noted that, in the embodiment, before using CEEMDAN to decompose the dynamic response data of the railway vehicle, such as the original waveform A(x) of the periodic vehicle body lateral acceleration signal, the total number of noise additions is preset to N and the signal-to-noise ratio ε0, which is generally N≥50, 0≤ε0≤1. The number of processing times and the signal-to-noise ratio will affect the result accuracy and calculation efficiency, and N=100 and ε0=0.2 can be taken.

[0063] In the embodiment, after obtaining the multiple signal components, the root mean square value of each signal component is calculated.

[0064] For example, through CEEMDAN, the dynamic response data of railway vehicles will be decomposed into a finite number of signal components according to the frequency components; the root mean square value of each signal component is calculated. j is the RMS value of the jth signal component, calculated according to formula ().

[0065]

[0066] Where S is the total length of the signal; a ji is the amplitude corresponding to the i-th position of the j-th sub-signal. Table 1 shows an example of the RMS value of each signal component.

[0067] Table 1 RMS values ​​of various signal components

[0068] signal component RMS value IMF1 0.00007 IMF2 0.00012 IMF3 0.00027 IMF4 0.00119 IMF5 0.00204 IMF6 0.03158 IMF7 0.00551 IMF8 0.00285

[0069] In one embodiment, the signal component with the largest RMS value is recorded as the characteristic component. According to Table 1, the characteristic component of the dynamic response data of the railway vehicle in this example is IMF6.

[0070] After determining the characteristic components, the characteristic components are subjected to time-frequency analysis. Considering that the dynamic response data of railway vehicles is a non-steady-state, nonlinear signal, the decomposed signal still retains certain non-steady-state, nonlinear characteristics. Directly applying the frequency domain analysis method Fourier transform will result in a large error. Therefore, in order to obtain more accurate analysis results, the advanced time-frequency analysis method WVD is used to analyze the time-frequency domain characteristics of the characteristic components.

[0071] In one embodiment, the WVD method is used to perform time-frequency analysis on the characteristic components, and the marginal spectrum is calculated based on the time-frequency analysis results, which may include: using the WVD method to perform time-frequency analysis on the characteristic components to obtain a time-frequency distribution diagram of the characteristic components; and calculating the marginal spectrum based on the time-frequency distribution diagram.

[0072] Figure 4 This is an example diagram of a time-frequency distribution diagram obtained by performing WVD analysis on a characteristic component in an embodiment of the present invention.

[0073] For example, for the characteristic component IMF6, the WVD analysis is performed according to formula (9) to obtain the time-frequency distribution diagram. The result is as follows: Figure 4 shown.

[0074]

[0075] In the formula, WVD x (t,f) is the calculated time-frequency distribution of x(t); x(t) is the time domain signal; x * (t) is the complex conjugate of x(t); t is time; τ is the time delay; f is the frequency; i is the imaginary unit.

[0076] Figure 5 This is an example diagram of calculating the marginal spectrum based on the time-frequency analysis results in an embodiment of the present invention.

[0077] For example, based on the time-frequency distribution diagram of IMF6, the marginal spectrum is calculated according to formula (10), and the result is as follows: Figure 5 shown.

[0078]

[0079] Where MW(f) is the marginal spectrum at frequency f after WVD transformation; T is the total length of the time series, and WVD(.) is the time-frequency distribution obtained after WVD transformation.

[0080] In one embodiment, the frequency corresponding to the maximum value of the marginal spectrum is determined as the characteristic frequency of the dynamic response data of the railway vehicle.

[0081] For example, see Figure 5 The results show that the characteristic frequency of the railway vehicle dynamic response data is 1.12Hz, which is consistent with the measured frequency.

[0082] In summary: the characteristic frequency calculation method of the railway vehicle dynamic response data proposed in the present invention can decompose the original railway vehicle dynamic response data according to the frequency components, and extract the characteristic components that best reflect the frequency characteristics of the original data, thereby accurately and reliably calculating the characteristic frequency of the railway vehicle dynamic response data.

[0083] The embodiment of the present invention also provides a device for calculating the characteristic frequency of the dynamic response data of a railway vehicle, as described in the following embodiment. Since the principle of solving the problem by the device is similar to the method for calculating the characteristic frequency of the dynamic response data of a railway vehicle, the implementation of the device can refer to the implementation of the method for calculating the characteristic frequency of the dynamic response data of a railway vehicle, and the repeated parts will not be repeated.

[0084] Figure 6 FIG. 1 is a schematic diagram of a device for calculating characteristic frequency of dynamic response data of a railway vehicle according to an embodiment of the present invention. Figure 6 As shown, the device comprises:

[0085] The data acquisition module 601 is used to acquire the dynamic response data of the railway vehicle;

[0086] The signal decomposition module 602 is used to decompose the dynamic response data of the railway vehicle using the CEEMDAN method to obtain multiple signal components;

[0087] A signal analysis module 603, used to calculate the root mean square value of each signal component;

[0088] A characteristic component extraction module 604, configured to determine the signal component with the largest RMS value as the characteristic component;

[0089] The time-frequency analysis module 605 is used to perform time-frequency analysis on the characteristic components using the WVD method, and calculate the marginal spectrum according to the time-frequency analysis results;

[0090] The characteristic frequency extraction module 606 is used to determine the frequency corresponding to the maximum value of the marginal spectrum as the characteristic frequency of the dynamic response data of the railway vehicle.

[0091] In one embodiment, the signal decomposition module 602 may be specifically used for:

[0092] The CEEMDAN method is used to decompose the dynamic response data of railway vehicles according to the preset total number of noise additions and signal-to-noise ratio to obtain multiple signal components.

[0093] In one embodiment, the signal decomposition module 602 may be specifically used for:

[0094] According to the preset total number of noise additions and signal-to-noise ratio, a white noise sequence obeying the standard normal distribution is added to the dynamic response data of railway vehicles.

[0095] In one embodiment, the time-frequency analysis module 605 may be specifically used for:

[0096] The WVD method is used to perform time-frequency analysis on the characteristic components and obtain the time-frequency distribution diagram of the characteristic components;

[0097] According to the time-frequency distribution diagram, the marginal spectrum is calculated.

[0098] Figure 7 Schematic diagram of a computer device in an embodiment of the present invention. Figure 7 As shown, an embodiment of the present invention further provides a computer device 700, including a processor 701, a memory 702, and a computer program 703 stored in the memory 702 and executable on the processor 701, wherein the processor 701 implements the characteristic frequency calculation method of the dynamic response data of the railway vehicle when executing the computer program 703.

[0099] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the characteristic frequency calculation method of the dynamic response data of a railway vehicle is implemented.

[0100] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the characteristic frequency calculation method of the dynamic response data of a railway vehicle is implemented.

[0101] In summary, in an embodiment of the present invention, the dynamic response data of a railway vehicle is obtained; the CEEMDAN method is used to decompose the dynamic response data of the railway vehicle to obtain multiple signal components; the root mean square value of each signal component is calculated; the signal component with the largest root mean square value is determined as a characteristic component; the WVD method is used to perform time-frequency analysis on the characteristic component, and the marginal spectrum is calculated according to the time-frequency analysis results; the frequency corresponding to the maximum value of the marginal spectrum is determined as the characteristic frequency of the dynamic response data of the railway vehicle, which can efficiently and accurately calculate the characteristic frequency of the dynamic response data of the railway vehicle, thereby evaluating the frequency domain characteristics of the vibration of the railway vehicle, which is helpful to improve the dynamic detection efficiency of the railway vehicle and has low dependence on manual experience.

[0102] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0106] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for calculating characteristic frequencies of dynamic response data of railway vehicles, characterized in that: include: Obtaining dynamic response data of railway vehicles; Adopting the adaptive noise complete ensemble empirical mode decomposition (CEEMDAN) method, the dynamic response data of railway vehicles are decomposed to obtain multiple signal components. Calculate the RMS value of each signal component; The signal component with the largest RMS value is determined as the characteristic component; The Wigner-Vera distribution (WVD) method is used to perform time-frequency analysis on the characteristic components, and the marginal spectrum is calculated based on the time-frequency analysis results. The frequency corresponding to the maximum value of the marginal spectrum is determined as the characteristic frequency of the dynamic response data of the railway vehicle.

2. The method according to claim 1, characterized in that The CEEMDAN method is used to decompose the dynamic response data of railway vehicles and obtain multiple signal components, including: The CEEMDAN method is used to decompose the dynamic response data of railway vehicles according to the preset total number of noise additions and signal-to-noise ratio to obtain multiple signal components.

3. The method according to claim 2, characterized in that The CEEMDAN method is used to decompose the dynamic response data of railway vehicles according to the preset total number of noise additions and signal-to-noise ratio to obtain multiple signal components, including: According to the preset total number of noise additions and signal-to-noise ratio, a white noise sequence obeying the standard normal distribution is added to the dynamic response data of railway vehicles.

4. The method according to claim 1, characterized in that The WVD method is used to perform time-frequency analysis on the characteristic components, and the marginal spectrum is calculated based on the time-frequency analysis results, including: The WVD method is used to perform time-frequency analysis on the characteristic components and obtain the time-frequency distribution diagram of the characteristic components; According to the time-frequency distribution diagram, the marginal spectrum is calculated.

5. A characteristic frequency calculation device for dynamic response data of railway vehicles, characterized in that: include: A data acquisition module, used to acquire dynamic response data of railway vehicles; A signal decomposition module is used to decompose the dynamic response data of railway vehicles using the CEEMDAN method to obtain multiple signal components; A signal analysis module for calculating the root mean square value of each signal component; A characteristic component extraction module, used for determining the signal component with the largest root mean square value as the characteristic component; The time-frequency analysis module is used to perform time-frequency analysis on the characteristic components using the WVD method and calculate the marginal spectrum based on the time-frequency analysis results; The characteristic frequency extraction module is used to determine the frequency corresponding to the maximum value of the marginal spectrum as the characteristic frequency of the dynamic response data of the railway vehicle.

6. The device according to claim 5, characterized in that The signal decomposition module is specifically used for: The CEEMDAN method is used to decompose the dynamic response data of railway vehicles according to the preset total number of noise additions and signal-to-noise ratio to obtain multiple signal components.

7. The device according to claim 6, characterized in that The signal decomposition module is specifically used for: According to the preset total number of noise additions and signal-to-noise ratio, a white noise sequence obeying the standard normal distribution is added to the dynamic response data of railway vehicles.

8. The device according to claim 5, characterized in that The time-frequency analysis module is specifically used for: The WVD method is used to perform time-frequency analysis on the characteristic components and obtain the time-frequency distribution diagram of the characteristic components; According to the time-frequency distribution diagram, the marginal spectrum is calculated.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

11. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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