Method and device for obtaining modal parameters

Abstract

The embodiment of the present invention discloses a method, device, electronic device and computer-readable storage medium for obtaining modal parameters. The method includes: determining the power spectral density function of multiple channel turbulence response signals; processing the matrix corresponding to the power spectral density function based on frequency domain decomposition to obtain the maximum singular value curve; fitting the maximum singular value curve within the target frequency range based on orthogonal polynomials to calculate the modal parameters. Through the present invention, the problem that the modal parameter estimation method in the related art has a relatively complex processing process and large errors is solved, and the technical effect of effectively improving the performance and accuracy of modal parameter estimation is achieved.

Description

Technical Field

[0001] The present invention relates to the field of turbulence response signal processing in aircraft flutter test flight, and in particular to a method, device, electronic equipment and computer-readable storage medium for acquiring modal parameters. Background Art

[0002] Flutter design is an important design requirement in the overall design of new aircraft. The flutter boundary provides a basis for the safe flight of the aircraft. Flutter test flight is a key link in verifying flutter design. The test signal collected in the flutter test flight is an important basis for calculating the natural frequency domain and damping of dynamic aeroelastic structures at different speeds. However, due to factors such as test measurement noise, missing excitation signals and excitation noise, and the limited number of data acquisition sensors. Noise-contaminated data leads to false mode problems in modal parameter identification, which in turn leads to extreme value selection problems in the modal parameter identification process. Ultimately, too many extreme points make mode selection difficult.

[0003] Regarding the frequency domain decomposition method and the processing of the turbulence response signal of the flutter test, the following methods are usually used in the prior art:

[0004] 1) An important step in the frequency domain decomposition and its improved method is to perform modal coherence analysis on the calculated maximum singular value curve, and finally calculate the auto-power spectral density function of the single degree of freedom system;

[0005] 2) In view of the fact that the test data in the flutter test flight is far lower than the flutter speed and the atmospheric turbulence interference is large, the modal parameters of the turbulence response signal are estimated by using the random subspace method.

[0006] In general, the above signal processing solutions have good effects under certain technical conditions, but they also have the following technical defects:

[0007] 1) Regarding the separation problem of single-degree-of-freedom systems in frequency domain decomposition, in the actual processing process, the low spectral line density leads to a decrease in the accuracy of the calculated single-degree-of-freedom system autopower spectrum analysis, and the final modal parameter estimation result has a large error;

[0008] 2) The random subspace method has good performance in estimating the modal parameters of flutter test flights. However, in order to obtain better estimation results, the subspace method obtains a large number of modal parameters through modal parameter estimation of different orders, and identifies stable modes based on the steady-state diagram method. This signal processing method has high time requirements, which is a major defect in actual engineering applications.

[0009] The modal parameter estimation methods in related technologies have the problems of complex processing and large errors, and no effective solution has been proposed yet. Summary of the invention

[0010] Embodiments of the present invention provide a method, device, electronic device and computer-readable storage medium for acquiring modal parameters, so as to at least solve the technical problem that the modal parameter estimation method in the related art has a relatively complex processing process and large errors.

[0011] According to one aspect of an embodiment of the present invention, a method for acquiring modal parameters is provided, comprising: determining a power spectral density function of a plurality of channel turbulence response signals; processing a matrix corresponding to the power spectral density function based on frequency domain decomposition to obtain a maximum singular value curve; fitting the maximum singular value curve within a target frequency range based on an orthogonal polynomial to calculate the modal parameters.

[0012] Optionally, determining the power spectral density function of multiple channel turbulence response signals includes: acquiring the multiple channel turbulence response signals collected in an aircraft flutter test flight; and processing the multiple channel turbulence response signals using a periodogram method to obtain the power spectral density function.

[0013] Optionally, after the multiple channel turbulence response signals are processed by using a periodogram method to obtain the power spectral density function, the method further includes: determining a relationship between the power spectral density function and a transfer function of the system.

[0014] Optionally, the matrix corresponding to the power spectral density function is processed based on frequency domain decomposition, including: performing singular value decomposition on the matrix corresponding to the power spectral density function at each frequency point according to the orthogonality of frequencies of each frequency point.

[0015] Optionally, the maximum singular value curve within the target frequency range is fitted based on an orthogonal polynomial to calculate the modal parameters, including: fitting the maximum singular value curve within the target frequency range using an orthogonal polynomial to obtain a transfer function of the system; and identifying the modal parameters based on the transfer function of the system to obtain the modal parameters.

[0016] According to another aspect of an embodiment of the present invention, a device for acquiring modal parameters is also provided, including: a determination module for determining a power spectral density function of multiple channel turbulence response signals; an acquisition module for processing a matrix corresponding to the power spectral density function based on frequency domain decomposition to obtain a maximum singular value curve; and a calculation module for fitting the maximum singular value curve within a target frequency range based on an orthogonal polynomial to calculate the modal parameters.

[0017] Optionally, the determination module includes: an acquisition unit, used to acquire the multiple channel turbulence response signals collected during the aircraft flutter test flight; and a processing unit, used to process the multiple channel turbulence response signals using a periodogram method to obtain the power spectral density function.

[0018] Optionally, the acquisition module includes: a decomposition unit, configured to perform singular value decomposition on a matrix corresponding to the power spectrum density function at each frequency point according to the orthogonality of frequencies of each frequency point.

[0019] An embodiment of the present invention provides an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the steps of any one of the above-mentioned methods for acquiring modal parameters.

[0020] An embodiment of the present invention provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the steps of any one of the above-mentioned methods for obtaining modal parameters are implemented.

[0021] In an embodiment of the present invention, the power spectral density function of the turbulence response signals of multiple channels is determined; the matrix corresponding to the power spectral density function is processed based on frequency domain decomposition to obtain the maximum singular value curve; the maximum singular value curve within the target frequency range is fitted based on orthogonal polynomials to calculate the modal parameters. In other words, the embodiment of the present invention no longer requires the modal separation calculation of the single-degree-of-freedom system of traditional frequency domain decomposition, but directly analyzes the target frequency band to achieve modal parameter estimation, thereby solving the problem that the modal parameter estimation method in the related art has a relatively complex processing process and large errors, and achieves the technical effect of effectively improving the performance and accuracy of modal parameter estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 A flowchart of a method for obtaining modal parameters provided by an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a device for acquiring modal parameters provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second" and the like in the specification, claims and drawings of the present invention are used to distinguish different objects rather than to limit a specific order.

[0027] An embodiment of the present invention provides a method for obtaining modal parameters. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] Figure 1 A flow chart of a method for obtaining modal parameters provided by an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0029] Step S102, determining the power spectral density function of the turbulence response signals of the multiple channels;

[0030] The above-mentioned multiple channel turbulence response signals are the turbulence response signals of multiple channels in the aircraft flutter test flight; the above-mentioned power spectrum density function is used to reflect the distribution characteristics of turbulence energy in the frequency domain, that is, the distribution density of the power of random vibration with respect to frequency; due to the randomness and determinism of the turbulence response signal in the aircraft flutter test flight, the corresponding power spectrum density function can be calculated using the multiple channel turbulence response signals; in the specific implementation process, the power spectrum density function of the turbulence response signals of multiple channels can be determined by a variety of methods such as the periodogram method, the autocorrelation function method and the improved periodogram method, and any of these methods can be used to calculate the power spectrum density function corresponding to the turbulence response signal. The above-mentioned autocorrelation function method is to perform Fourier transform on the autocorrelation power spectrum density function to obtain a power spectrum estimate, thereby calculating the power spectrum density function.

[0031] Step S104, processing the matrix corresponding to the power spectrum density function based on frequency domain decomposition to obtain a maximum singular value curve;

[0032] Optionally, a matrix corresponding to the power spectrum density function of the turbulence response signals of multiple channels may be calculated based on frequency domain decomposition, and a singular value decomposition may be performed on the power spectrum density function matrix at each frequency point to obtain a maximum singular value curve.

[0033] Step S106, fitting the maximum singular value curve within the target frequency range based on the orthogonal polynomial to calculate and obtain the modal parameters.

[0034] The above maximum singular value curve and the autocorrelation power spectral density function of the system are mathematically the product of the transfer function of the system and its conjugate transpose. Therefore, the product of the transfer function of the system and its conjugate transpose can be fitted through the maximum singular value curve. The maximum singular value curve is fitted by orthogonal polynomials, and the modal parameters are further calculated. The above autocorrelation power spectral density function is the dependency of the turbulence response signal at one moment and another.

[0035] In an embodiment of the present invention, the method adopts the method of determining the power spectral density function of multiple channel turbulence response signals; then processing the matrix corresponding to the power spectral density function based on frequency domain decomposition to obtain the maximum singular value curve; and then fitting the maximum singular value curve within the target frequency range based on orthogonal polynomials to calculate the modal parameters. In other words, the embodiment of the present invention no longer requires the modal separation calculation of the single-degree-of-freedom system by traditional frequency domain decomposition, but directly analyzes the target frequency band to achieve modal parameter estimation, thereby solving the problem that the modal parameter estimation method in the related art has a relatively complex processing process and large errors, and achieves the technical effect of effectively improving the performance and accuracy of modal parameter estimation.

[0036] In an optional embodiment, determining the power spectral density function of multiple channel turbulence response signals includes: acquiring multiple channel turbulence response signals collected during an aircraft flutter test flight; and processing the multiple channel turbulence response signals using a periodogram method to obtain a power spectral density function.

[0037] Optionally, the above periodogram method mainly treats the N-point turbulence response signal as a signal with limited energy, directly performs Fourier transform on it, then takes the average value of its modulus value and divides it by N, so as to obtain the true power spectral density function of the turbulence response signal.

[0038] In the specific implementation process, not only can the rectangular window periodogram method be used to realize power spectrum estimation and calculate the power spectrum density function, but the fast Fourier transform (FFT) periodogram method can also be used to realize power spectrum estimation and calculate the power spectrum density function; in addition, improved periodogram methods can also be used, such as the average periodogram method, the window function method, the modified periodogram averaging method and the weighted overlapping averaging method.

[0039] The above average periodogram method is to obtain L groups of independent data when the turbulence response signal is obtained, each group of data is M long, and the power spectrum density is calculated for each group, and then the L means are added together to calculate the average. The variance of the mean obtained in this way is 1 / L of the original. Since the length of each FFT segment becomes M, the resolution is lower (Fs / M). Therefore, the average periodogram method reduces the estimated variance at the expense of reduced resolution.

[0040] The above window function method multiplies the turbulence response signal of length N by a data window w of the same length. After the data is windowed, the mathematical expectation of the spectrum estimate is equal to the square convolution of the true spectrum value and the window spectrum function, so it is a biased estimate. After the data is windowed, the variance of the spectrum estimate is greater than or equal to the square of the spectrum estimate, and does not decrease with the increase of data length. Data windowing can reduce the sidelobes of the spectrum estimate, but it reduces the resolution of the spectrum estimate.

[0041] The modified periodogram averaging method is to divide the turbulence response signal of length N into L segments, each segment of data is M, then N = LM; then add the window function w to each segment of data, find the periodogram of each segment, and then evaluate each periodogram. This method adds a window to each data segment before calculating the periodogram, which reduces the estimated variance of the average periodogram method, but reduces the resolution.

[0042] The weighted overlapping average method is an improvement on the modified periodogram average method. First, two adjacent segments can overlap when segmenting, and second, the window function uses a Hanning window or a Hamming window. Through the improvement, the variance can be further reduced.

[0043] In a specific implementation process, any one method or a combination of any several methods in the periodogram method can be selected according to application requirements to obtain the power spectral density function.

[0044] In the above embodiment, by processing the turbulence response signals of multiple channels using the periodogram method, a more accurate power spectral density function can be obtained.

[0045] In an optional implementation, after processing the turbulence response signals of multiple channels by using the periodogram method to obtain the power spectral density function, the method further includes: determining the relationship between the power spectral density function and the transfer function of the system.

[0046] The transfer function of the above system refers to the ratio of the Laplace transform (or z transform) of the linear system response (i.e. output) to the Laplace transform of the excitation (i.e. input) under zero initial conditions. The transfer function of the above system corresponds to the differential equation that describes its motion law. In the specific implementation process, the transfer function of the overall system can be derived based on the transfer functions of each unit of the aircraft system and the connection relationship between them, and it can be used to analyze the dynamic characteristics and stability of the system.

[0047] Optionally, based on the randomness and determinism of the turbulence response signal in the aircraft flutter test flight, the power spectrum density function of the turbulence response signals of multiple channels can be calculated by the periodogram method, and the relationship between the turbulence response power spectrum density function and the transfer function of the system can be explained to associate the power spectrum density function with the transfer function of the system. For example, by outputting / inputting the power spectrum density and then performing an inverse Fourier transform, the corresponding transfer function of the system can be obtained.

[0048] In an optional implementation, the matrix corresponding to the power spectrum density function is processed based on frequency domain decomposition, including: performing singular value decomposition on the matrix corresponding to the power spectrum density function at each frequency point according to the orthogonality of the frequencies of each frequency point.

[0049] The above singular value decomposition (SVD) is a generalization of eigendecomposition on an arbitrary matrix, specifically a generalization of spectral analysis theory on an arbitrary matrix. Optionally, according to the orthogonality of the frequencies of each frequency point, the matrix corresponding to the power spectral density function of the multi-channel turbulence response signal is subjected to singular value decomposition at each frequency point, and the maximum singular value curve is obtained based on the singular value decomposition result.

[0050] It should be noted that the above implementation method is an improvement on the original frequency domain decomposition method, which directly estimates the modal parameters of orthogonal polynomials in the frequency domain. Compared with the existing frequency domain decomposition method, the important link is the separation of the self-power spectral density function of the single-degree-of-freedom system. This implementation method can directly fit the entire analysis frequency band based on the relationship between the maximum singular value curve and the system's self-power spectral density function. In actual engineering, it only needs to fit the system's self-power spectral density function to achieve the modal parameter estimation of the turbulence response signal of the flutter test.

[0051] In an optional embodiment, the maximum singular value curve in the target frequency range is fitted based on the orthogonal polynomial to calculate the modal parameters, including: fitting the maximum singular value curve in the target frequency range using the orthogonal polynomial to obtain the transfer function of the system; identifying the modal parameters based on the transfer function of the system to obtain the modal parameters.

[0052] Optionally, the maximum singular value curve is analyzed based on the relationship between the maximum singular value curve and the transfer function of the system and its conjugate transpose, the maximum singular value curve is fitted by an orthogonal polynomial within the target frequency range to obtain the transfer function of the system, and the modal parameters are identified based on the transfer function of the system to obtain the modal parameters.

[0053] It should be noted that the above target frequency range is a frequency band to be analyzed or a specified frequency band, which can be set according to the needs of the application scenario.

[0054] In addition, since the maximum singular value curve and the power spectral density function representing the autocorrelation of the system are mathematically the product of the transfer function of the system and its conjugate transpose, the product of the system's autopower spectral density function and its conjugate transpose is equal to the maximum singular value curve. On this basis, the entire frequency band is directly analyzed in the frequency domain, and the specified frequency band is fitted without the need for single-degree-of-freedom system modal separation. Furthermore, the specified frequency band is analyzed based on orthogonal polynomials, and the maximum singular value curve of the specified frequency band is directly fitted, and finally the estimation results of the modal parameters are calculated.

[0055] According to another aspect of the embodiments of the present invention, a device for acquiring modal parameters is also provided. Figure 2 A schematic diagram of a device for acquiring modal parameters provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the device for acquiring modal parameters includes: a determination module 22, an acquisition module 24 and a calculation module 26. The device for acquiring modal parameters is described in detail below.

[0056] A determination module 22 is used to determine the power spectral density function of multiple channel turbulence response signals; an acquisition module 24 is connected to the above-mentioned determination module 22, and is used to process the matrix corresponding to the power spectral density function based on frequency domain decomposition to obtain the maximum singular value curve; a calculation module 26 is connected to the above-mentioned acquisition module 24, and is used to fit the maximum singular value curve within the target frequency range based on an orthogonal polynomial to calculate the modal parameters.

[0057] The above-mentioned multiple channel turbulence response signals are the turbulence response signals of multiple channels in the aircraft flutter test flight; the above-mentioned power spectrum density function is used to reflect the distribution characteristics of turbulence energy in the frequency domain, that is, the distribution density of the power of random vibration with respect to frequency; due to the randomness and determinism of the turbulence response signal in the aircraft flutter test flight, the corresponding power spectrum density function can be calculated using the multiple channel turbulence response signals; in the specific implementation process, the power spectrum density function of the turbulence response signals of multiple channels can be determined by a variety of methods such as the periodogram method, the autocorrelation function method and the improved periodogram method, and any of these methods can be used to calculate the power spectrum density function corresponding to the turbulence response signal. The above-mentioned autocorrelation function method is to perform Fourier transform on the autocorrelation power spectrum density function to obtain a power spectrum estimate, thereby calculating the power spectrum density function.

[0058] Optionally, a matrix corresponding to the power spectrum density function of the turbulence response signals of multiple channels may be calculated based on frequency domain decomposition, and a singular value decomposition may be performed on the power spectrum density function matrix at each frequency point to obtain a maximum singular value curve.

[0059] The above maximum singular value curve and the autocorrelation power spectral density function of the system are mathematically the product of the transfer function of the system and its conjugate transpose. Therefore, the product of the transfer function of the system and its conjugate transpose can be fitted through the maximum singular value curve. The maximum singular value curve is fitted by orthogonal polynomials, and the modal parameters are further calculated. The above autocorrelation power spectral density function is the dependency of the turbulence response signal at one moment and another.

[0060] It should be noted that the above-mentioned modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above-mentioned modules can be located in the same processor; and / or the above-mentioned modules are located in different processors in any combination.

[0061] In the above embodiment, the device for acquiring the modal parameters can determine the power spectral density function of the turbulence response signals of multiple channels; then process the matrix corresponding to the power spectral density function based on frequency domain decomposition to obtain the maximum singular value curve; then fit the maximum singular value curve within the target frequency range based on orthogonal polynomials to calculate the modal parameters. In other words, the embodiment of the present invention no longer requires the modal separation calculation of the single-degree-of-freedom system by traditional frequency domain decomposition, but directly analyzes the target frequency band to achieve modal parameter estimation, thereby solving the problem that the modal parameter estimation method in the related art has a relatively complex processing process and large errors, and achieves the technical effect of effectively improving the performance and accuracy of modal parameter estimation.

[0062] It should be noted here that the above-mentioned determination module 22, acquisition module 24 and calculation module 26 correspond to steps S102 to S106 in the method embodiment, and the examples and application scenarios implemented by the above-mentioned modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned method embodiment.

[0063] Optionally, the determination module 22 includes: an acquisition unit for acquiring multiple channel turbulence response signals collected during an aircraft flutter test flight; and a processing unit for processing the multiple channel turbulence response signals using a periodogram method to obtain a power spectral density function.

[0064] Optionally, the above periodogram method mainly treats the N-point turbulence response signal as a signal with limited energy, directly performs Fourier transform on it, then takes the average value of its modulus value and divides it by N, so as to obtain the true power spectral density function of the turbulence response signal.

[0065] In the specific implementation process, not only can the rectangular window periodogram method be used to realize power spectrum estimation and calculate the power spectrum density function, but the fast Fourier transform (FFT) periodogram method can also be used to realize power spectrum estimation and calculate the power spectrum density function; in addition, improved periodogram methods can also be used, such as the average periodogram method, the window function method, the modified periodogram averaging method and the weighted overlapping averaging method.

[0066] The above average periodogram method is to obtain L groups of independent data when the turbulence response signal is obtained, each group of data is M long, and the power spectrum density is calculated for each group, and then the L means are added together to calculate the average. The variance of the mean obtained in this way is 1 / L of the original. Since the length of each FFT segment becomes M, the resolution is lower (Fs / M). Therefore, the average periodogram method reduces the estimated variance at the expense of reduced resolution.

[0067] The above window function method multiplies the turbulence response signal of length N by a data window w of the same length. After the data is windowed, the mathematical expectation of the spectrum estimate is equal to the square convolution of the true spectrum value and the window spectrum function, so it is a biased estimate. After the data is windowed, the variance of the spectrum estimate is greater than or equal to the square of the spectrum estimate, and does not decrease with the increase of data length. Data windowing can reduce the sidelobes of the spectrum estimate, but it reduces the resolution of the spectrum estimate.

[0068] The modified periodogram averaging method is to divide the turbulence response signal of length N into L segments, each segment of data is M, then N = LM; then add the window function w to each segment of data, find the periodogram of each segment, and then evaluate each periodogram. This method adds a window to each data segment before calculating the periodogram, which reduces the estimated variance of the average periodogram method, but reduces the resolution.

[0069] The weighted overlapping average method is an improvement on the modified periodogram average method. First, two adjacent segments can overlap when segmenting, and second, the window function uses a Hanning window or a Hamming window. Through the improvement, the variance can be further reduced.

[0070] In a specific implementation process, any one method or a combination of any several methods in the periodogram method can be selected according to application requirements to obtain the power spectral density function.

[0071] In the above embodiment, by processing the turbulence response signals of multiple channels using the periodogram method, a more accurate power spectral density function can be obtained.

[0072] Optionally, the determination module further includes: a determination unit configured to determine the relationship between the power spectrum density function and the transfer function of the system after processing the turbulence response signals of the multiple channels by using the periodogram method to obtain the power spectrum density function.

[0073] The transfer function of the above system refers to the ratio of the Laplace transform (or z transform) of the linear system response (i.e. output) to the Laplace transform of the excitation (i.e. input) under zero initial conditions. The transfer function of the above system corresponds to the differential equation that describes its motion law. In the specific implementation process, the transfer function of the overall system can be derived based on the transfer functions of each unit of the aircraft system and the connection relationship between them, and it can be used to analyze the dynamic characteristics and stability of the system.

[0074] Optionally, based on the randomness and determinism of the turbulence response signal in the aircraft flutter test flight, the power spectrum density function of the turbulence response signals of multiple channels can be calculated by the periodogram method, and the relationship between the turbulence response power spectrum density function and the transfer function of the system can be explained to associate the power spectrum density function with the transfer function of the system. For example, by outputting / inputting the power spectrum density and then performing an inverse Fourier transform, the corresponding transfer function of the system can be obtained.

[0075] Optionally, the acquisition module 24 includes: a decomposition unit, configured to perform singular value decomposition on a matrix corresponding to the power spectrum density function at each frequency point according to the orthogonality of the frequencies of each frequency point.

[0076] The above singular value decomposition (SVD) is a generalization of eigendecomposition on an arbitrary matrix, specifically a generalization of spectral analysis theory on an arbitrary matrix. Optionally, according to the orthogonality of the frequencies of each frequency point, the matrix corresponding to the power spectral density function of the multi-channel turbulence response signal is subjected to singular value decomposition at each frequency point, and the maximum singular value curve is obtained based on the singular value decomposition result.

[0077] It should be noted that the above implementation method is an improvement on the original frequency domain decomposition method, which directly estimates the modal parameters of orthogonal polynomials in the frequency domain. Compared with the existing frequency domain decomposition method, the important link is the separation of the self-power spectral density function of the single-degree-of-freedom system. This implementation method can directly fit the entire analysis frequency band based on the relationship between the maximum singular value curve and the system's self-power spectral density function. In actual engineering, it only needs to fit the system's self-power spectral density function to achieve the modal parameter estimation of the turbulence response signal of the flutter test.

[0078] Optionally, the calculation module 26 includes: fitting a maximum singular value curve within a target frequency range using an orthogonal polynomial to obtain a transfer function of the system; and identifying modal parameters based on the transfer function of the system to obtain the modal parameters.

[0079] Optionally, the maximum singular value curve is analyzed based on the relationship between the maximum singular value curve and the transfer function of the system and its conjugate transpose, the maximum singular value curve is fitted by an orthogonal polynomial within the target frequency range to obtain the transfer function of the system, and the modal parameters are identified based on the transfer function of the system to obtain the modal parameters.

[0080] It should be noted that the above target frequency range is a frequency band to be analyzed or a specified frequency band, which can be set according to the needs of the application scenario.

[0081] In addition, since the maximum singular value curve and the power spectral density function representing the autocorrelation of the system are mathematically the product of the transfer function of the system and its conjugate transpose, the product of the system's autopower spectral density function and its conjugate transpose is equal to the maximum singular value curve. On this basis, the entire frequency band is directly analyzed in the frequency domain, and the specified frequency band is fitted without the need for single-degree-of-freedom system modal separation. Furthermore, the specified frequency band is analyzed based on orthogonal polynomials, and the maximum singular value curve of the specified frequency band is directly fitted, and finally the estimation results of the modal parameters are calculated.

[0082] An embodiment of the present invention provides an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the steps of any one of the above-mentioned methods for acquiring modal parameters.

[0083] Optionally, an embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the program: determining a power spectral density function of turbulence response signals of multiple channels; processing a matrix corresponding to the power spectral density function based on frequency domain decomposition to obtain a maximum singular value curve; fitting the maximum singular value curve within the target frequency range based on an orthogonal polynomial to calculate modal parameters.

[0084] Optionally, determining the power spectral density function of multiple channel turbulence response signals includes: acquiring multiple channel turbulence response signals collected during an aircraft flutter test flight; and processing the multiple channel turbulence response signals using a periodogram method to obtain a power spectral density function.

[0085] Optionally, after processing the turbulence response signals of multiple channels by using the periodogram method to obtain the power spectrum density function, the method further includes: determining the relationship between the power spectrum density function and the transfer function of the system.

[0086] Optionally, the matrix corresponding to the power spectrum density function is processed based on frequency domain decomposition, including: performing singular value decomposition on the matrix corresponding to the power spectrum density function at each frequency point according to the orthogonality of the frequencies of each frequency point.

[0087] Optionally, the maximum singular value curve within the target frequency range is fitted based on the orthogonal polynomial to calculate the modal parameters, including: fitting the maximum singular value curve within the target frequency range using the orthogonal polynomial to obtain the transfer function of the system; identifying the modal parameters based on the transfer function of the system to obtain the modal parameters.

[0088] An embodiment of the present invention provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the steps of any one of the above-mentioned methods for obtaining modal parameters are implemented.

[0089] In an embodiment of the present invention, the computer-readable storage medium may be located in any computer terminal in a computer terminal group in a computer network and / or in any mobile terminal in a mobile terminal group, and the computer-readable storage medium includes a stored program.

[0090] Optionally, when the program is running, the device where the computer-readable storage medium is located is controlled to perform the following functions: determine the power spectral density function of the turbulence response signals of multiple channels; process the matrix corresponding to the power spectral density function based on frequency domain decomposition to obtain the maximum singular value curve; fit the maximum singular value curve within the target frequency range based on orthogonal polynomials to calculate the modal parameters.

[0091] Optionally, determining the power spectral density function of multiple channel turbulence response signals includes: acquiring multiple channel turbulence response signals collected during an aircraft flutter test flight; and processing the multiple channel turbulence response signals using a periodogram method to obtain a power spectral density function.

[0092] Optionally, after processing the turbulence response signals of multiple channels by using the periodogram method to obtain the power spectrum density function, the method further includes: determining the relationship between the power spectrum density function and the transfer function of the system.

[0093] Optionally, the matrix corresponding to the power spectrum density function is processed based on frequency domain decomposition, including: performing singular value decomposition on the matrix corresponding to the power spectrum density function at each frequency point according to the orthogonality of the frequencies of each frequency point.

[0094] Optionally, the maximum singular value curve within the target frequency range is fitted based on the orthogonal polynomial to calculate the modal parameters, including: fitting the maximum singular value curve within the target frequency range using the orthogonal polynomial to obtain the transfer function of the system; identifying the modal parameters based on the transfer function of the system to obtain the modal parameters.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A method for obtaining modal parameters, characterized in that: The power spectrum density function is used to reflect the distribution characteristics of turbulent energy in the frequency domain, that is, the distribution density of random vibration power with respect to frequency; including: Determine the power spectral density function of multiple channel turbulence response signals; including: Acquiring the plurality of channel turbulence response signals collected during an aircraft flutter test flight; Processing the plurality of channel turbulence response signals using a periodogram method to obtain the power spectral density function; The above periodogram method treats the N-point turbulence response signal as a signal with limited energy, directly performs Fourier transform on it, then takes the average value of its modulus value and divides it by N, thereby obtaining the true power spectrum density function of the turbulence response signal; determining a relationship between the power spectral density function and a transfer function of the system; Processing the matrix corresponding to the power spectrum density function based on frequency domain decomposition to obtain a maximum singular value curve; comprising: According to the orthogonality of the frequencies of each frequency point, performing singular value decomposition on the matrix corresponding to the power spectrum density function at each frequency point; Fitting the maximum singular value curve within the target frequency range based on an orthogonal polynomial to calculate modal parameters; including: Fitting the maximum singular value curve within the target frequency range using an orthogonal polynomial to obtain a transfer function of the system; The modal parameters are identified based on the transfer function of the system to obtain the modal parameters.

2. A device for acquiring modal parameters, characterized in that: The power spectrum density function is used to reflect the distribution characteristics of turbulent energy in the frequency domain, that is, the distribution density of random vibration power with respect to frequency; including: A determination module is used to determine the power spectral density function of the turbulence response signals of multiple channels; the determination module comprises: An acquisition unit, configured to acquire the plurality of channel turbulence response signals collected during a flutter test flight of the aircraft; A processing unit, used to process the turbulence response signals of the multiple channels by using a periodogram method to obtain the power spectrum density function; the periodogram method takes the N-point turbulence response signals as signals with limited energy, directly performs Fourier transform on them, then takes the average value of their modulus values ​​and divides it by N, thereby obtaining the true power spectrum density function of the turbulence response signals; An acquisition module is used to process the matrix corresponding to the power spectrum density function based on frequency domain decomposition to obtain a maximum singular value curve; the acquisition module includes: A decomposition unit, used for performing singular value decomposition on the matrix corresponding to the power spectrum density function at each frequency point according to the orthogonality of the frequencies of each frequency point; The calculation module is used to fit the maximum singular value curve within the target frequency range based on an orthogonal polynomial to calculate the modal parameters.

3. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to execute the steps of the method for acquiring modal parameters described in claim 1.

4. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the method for obtaining modal parameters described in claim 1 are implemented.

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