CN115165270B - Method and device for processing multi-channel flutter test signals - Google Patents
Method and device for processing multi-channel flutter test signals Download PDFInfo
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M7/00—Vibration-testing of structures; Shock-testing of structures
- G01M7/02—Vibration-testing by means of a shake table
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- B64F5/60—Testing or inspecting aircraft components or systems
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- G—PHYSICS
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M9/00—Aerodynamic testing; Arrangements in or on wind tunnels
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Abstract
本发明实施例公开了一种信号处理的方法和装置。该信号处理的方法包括：依据获取到的多通道紊流响应信号计算功率谱密度函数矩阵；依据功率谱密度函数矩阵获取最大奇异值曲线；依据最大奇异值曲线获取单自由度系统的自功率谱密度函数；依据自功率谱密度函数得到单模态系统模态参数。通过本发明，解决相关技术中由于现有技术在颤振试飞试验中紊流响应信号分析问题，达到了提高模态参数参数估计的精度，同时避免由于随机子空间方法带来的计算实时性不足的技术效果。
The embodiment of the present invention discloses a method and device for signal processing. The signal processing method includes: calculating a power spectrum density function matrix based on the acquired multi-channel turbulence response signal; obtaining a maximum singular value curve based on the power spectrum density function matrix; obtaining the self-power spectrum density function of the single-degree-of-freedom system based on the maximum singular value curve; and obtaining the modal parameters of the single-mode system based on the self-power spectrum density function. Through the present invention, the problem of turbulence response signal analysis in flutter flight tests in the related technology due to the existing technology is solved, and the accuracy of modal parameter estimation is improved, while avoiding the technical effect of insufficient calculation real-time performance caused by the random subspace method.
Description
Technical Field
The present invention relates to the field of aeronautical technology, and in particular, to a method and apparatus for signal processing.
Background
The flutter test is used as the verification of flutter design, is an unavoidable important link in the development process of the aircraft, and is an important task for accurately and effectively carrying out characteristic analysis on flutter response signals and further processing test data. Generally, the artificial excitation source (such as control surface sweep frequency, eccentric wheel excitation, rudder pedaling, small rocket excitation and the like) is implemented in the test, so that the quality of the flutter response signal can be effectively improved, the reliability of subsequent data processing conclusion is improved, however, the active excitation mode is often severely limited by the condition of the flutter test itself, such as the wind tunnel test is limited by the size and structure of a model, the active excitation is inconvenient to implement, and the aircraft needs to be modified by installing excitation equipment in test flight, so that the risk of test flight subjects such as dive, low-altitude large gauge speed and the like is further increased. Meanwhile, due to the general narrow-band characteristic of the active excitation signal, high-frequency mode information in the structure is not easy to sufficiently excite, and the analysis effect of a higher-frequency mode is further affected.
The turbulent flow excitation is an unavoidable excitation form in the flutter test flight/wind tunnel test, meanwhile, no additional modification is needed to be carried out on an aircraft/aeroelastic model, and the turbulent flow excitation belongs to broadband noise, so that the turbulent flow excitation becomes a necessary and effective excitation mode in high-risk test flight subjects, wind tunnel tests and high-frequency modal analysis. Because the atmospheric turbulence energy is dispersed and not measurable, although the turbulence response signal contains abundant and precious flutter test information, the randomness and low quality of the data often cause difficulty in modal analysis, thereby influencing the accurate acquisition of test conclusion.
Disclosure of Invention
The embodiment of the invention provides a signal processing method and device, which at least solve the problem of turbulent flow response signal analysis in a flutter test flight test due to the prior art in the related art.
According to one aspect of the embodiment of the invention, a signal processing method is provided, which comprises the steps of calculating a power spectrum density function matrix according to an acquired multi-channel turbulence response signal, acquiring a maximum singular value curve according to the power spectrum density function matrix, acquiring a self-power spectrum density function of a single-degree-of-freedom system according to the maximum singular value curve, and acquiring modal parameters of the single-mode system according to the self-power spectrum density function.
Optionally, obtaining the power spectrum density function matrix according to the obtained multi-channel turbulence response signal comprises performing power spectrum analysis on the multi-channel turbulence response signal to obtain a power spectrum density function matrix corresponding to each signal in the multi-channel turbulence response signal, wherein the power spectrum density function matrix is an autocorrelation power spectrum density function matrix or a cross correlation power spectrum density function matrix corresponding to each signal.
Further, optionally, obtaining the maximum singular value curve according to the power spectral density function matrix includes performing singular value decomposition on the power spectral density function matrix at each frequency point based on frequency domain orthogonality to obtain the maximum singular value curve of each frequency band.
Optionally, the method further comprises the step of carrying out singular value decomposition on the power spectrum density function matrix at each frequency point based on frequency domain orthogonality to obtain a left singular value vector corresponding to each frequency point, wherein the left singular value vector represents the mode shape of the system.
Further, optionally, obtaining the self-power spectral density function of the single degree of freedom system according to the maximum singular value curve comprises analyzing the left singular value vector based on a modal shape coherence criterion to obtain the self-power spectral density function of the single degree of freedom system of the corresponding frequency.
Optionally, obtaining the modal parameters of the single-mode system according to the self-power spectral density function comprises performing frequency domain fitting on the self-power spectral density function through an orthogonal polynomial, and performing modal parameter estimation on the single-degree-of-freedom system through polynomial fitting to obtain the modal parameters of the single-mode system, wherein the modal parameters of the single-mode system comprise frequency and damping of the single-degree-of-freedom system.
According to another aspect of the embodiment of the invention, a signal processing device is provided, which comprises a signal processing module, a first acquisition module, a second acquisition module and a third acquisition module, wherein the signal processing module is used for calculating a power spectrum density function matrix according to an acquired multi-channel turbulence response signal, the first acquisition module is used for acquiring a maximum singular value curve according to the power spectrum density function matrix, the second acquisition module is used for acquiring a self-power spectrum density function of a single-degree-of-freedom system according to the maximum singular value curve, and the third acquisition module is used for acquiring modal parameters of the single-mode system according to the self-power spectrum density function.
Optionally, the signal processing module comprises a signal processing unit, a power spectrum analysis unit and a power spectrum analysis unit, wherein the signal processing unit is used for carrying out power spectrum analysis on the multi-channel turbulence response signals to obtain a power spectrum density function matrix corresponding to each signal in the multi-channel turbulence response signals, and the power spectrum density function matrix is an autocorrelation power spectrum density function matrix or a cross correlation power spectrum density function matrix corresponding to each signal.
Further, the first acquisition module comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for carrying out singular value decomposition on the power spectrum density function matrix at each frequency point based on frequency domain orthogonality to obtain a maximum singular value curve of each frequency band.
Optionally, the first acquisition module further comprises a decomposition unit, which is used for carrying out singular value decomposition on the power spectrum density function matrix at each frequency point based on frequency domain orthogonality to obtain a left singular value vector corresponding to each frequency point, wherein the left singular value vector represents the mode shape of the system.
According to the embodiment of the invention, a power spectrum density function matrix is calculated according to the acquired multi-channel turbulence response signals, a maximum singular value curve is acquired according to the power spectrum density function matrix, a self-power spectrum density function of a single-degree-of-freedom system is acquired according to the maximum singular value curve, and a modal parameter of the single-mode system is acquired according to the self-power spectrum density function. That is, the embodiment of the invention can solve the problem of turbulent flow response signal analysis in the flutter test flight test in the related technology, achieves the technical effects of improving the accuracy of modal parameter estimation and avoiding the defect of calculation instantaneity caused by a random subspace method.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 is a flow chart of a signal processing method according to an embodiment of the present invention;
FIG. 2 is a flowchart of another signal processing method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a signal processing apparatus according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and in the drawings are used for distinguishing between different objects and not for limiting a particular order.
In the related art, for the frequency domain decomposition method and the processing of the turbulent flow response signal of the flutter test, the following signal processing methods mainly exist:
According to a traditional frequency domain decomposition method, a self-power spectrum density function of a single-degree-of-freedom system is calculated by carrying out frequency domain decomposition based on a modal coherence criterion, a single-degree-of-freedom time domain impulse response is calculated by interpolation and zero padding and then carrying out inverse Fourier transformation, and modal parameter estimation is carried out;
Aiming at the turbulent flow response signal analysis of the flutter test, generally, in the wind tunnel test, the turbulent flow response signal is processed through a random decrement technology because the test speed is closer to the flutter boundary, so as to obtain free attenuation response, and then time domain modal parameter estimation is carried out;
aiming at the situation that test data in flutter test flight is far lower than flutter speed and the interference of atmospheric turbulence is large, the modal parameter estimation of the turbulence response signal is carried out by a random subspace method.
However, the above signal processing schemes have better effects under certain technical conditions, but have certain technical defects as well:
in the traditional frequency domain decomposition method, the effect of calculating the time domain impulse response signal by the inverse Fourier transform is poor aiming at the inverse Fourier transform of the self-power spectrum of the single-degree-of-freedom system due to the problem of spectral line density, and the influence on the fitting result is larger;
aiming at the problems of random decrement technology commonly used in flutter wind tunnel test, such as atmospheric turbulence, principle flutter boundary and the like in flutter test flight, the error of the free attenuation response signal calculated by the technology is larger, and the effect of estimating the modal parameters of the flutter test flight is poorer;
The random subspace method has better performance on the modal parameter estimation of the flutter test flight, but in order to obtain a better estimation result, the subspace method obtains a large number of modal parameters through the modal parameter estimation of different orders, and the steady-state pattern method is based on the steady-state pattern method for identifying the steady mode, and the signal processing method has higher time requirements and is a larger defect in practical engineering application.
Therefore, the signal processing method provided by the embodiment of the application aims at the turbulent flow response signal of the flutter test, improves the traditional frequency domain decomposition method, directly carries out the modal parameter estimation of the orthogonal polynomial in the frequency domain, directly carries out the modal parameter estimation in the frequency domain, can better avoid estimation errors caused by insufficient spectral line density, improves the result of the modal parameter estimation, and only needs to obtain the self-power spectral density function of the system according to the technical route of the modal parameter estimation of the turbulent flow response signal of the flutter test, thereby obtaining the modal parameter result of the system by fitting in the frequency domain based on the scheme.
Specifically, according to an aspect of the embodiment of the present application, a signal processing method is provided, and fig. 1 is a schematic flow chart of the signal processing method provided by the embodiment of the present application. As shown in fig. 1, the signal processing method provided by the embodiment of the application includes:
step S102, calculating a power spectral density function matrix according to the acquired multi-channel turbulence response signals;
Optionally, the step S102 of calculating the power spectrum density function matrix according to the acquired multi-channel turbulence response signals includes performing power spectrum analysis on the multi-channel turbulence response signals to obtain a power spectrum density function matrix corresponding to each signal in the multi-channel turbulence response signals, where the power spectrum density function matrix is an autocorrelation power spectrum density function matrix or a cross correlation power spectrum density function matrix corresponding to each signal.
Specifically, fig. 2 is a schematic flow chart of another signal processing method provided by the embodiment of the present application, as shown in fig. 2, in the embodiment of the present application, power spectrum analysis is performed on a multi-channel turbulence response signal to obtain a self (mutual) correlation power spectrum density function matrix of the corresponding signal, and the multi-channel turbulence response signal obtains the power spectrum density function matrix through a periodic chart method.
Step S104, obtaining a maximum singular value curve according to a power spectrum density function matrix;
Optionally, the step S104 of obtaining the maximum singular value curve according to the power spectrum density function matrix includes performing singular value decomposition on the power spectrum density function matrix at each frequency point based on frequency domain orthogonality to obtain the maximum singular value curve of each frequency band.
Optionally, the signal processing method provided by the embodiment of the application further comprises the step of carrying out singular value decomposition on the power spectrum density function matrix at each frequency point based on frequency domain orthogonality to obtain a left singular value vector corresponding to each frequency point, wherein the left singular value vector represents the mode shape of the system.
Specifically, as shown in fig. 2, based on frequency domain orthogonality, singular value decomposition is performed on the power spectrum density function matrix at each frequency point to obtain a maximum singular value curve of each frequency band and a left singular value vector corresponding to each frequency point, and based on the power spectrum density function matrix obtained in step S102, the maximum singular value curve is obtained through singular value decomposition (Singular Value Decomposition).
Step S106, obtaining a self-power spectral density function of the single degree of freedom system according to the maximum singular value curve;
Optionally, the step S106 of obtaining the self-power spectral density function of the single degree of freedom system according to the maximum singular value curve comprises analyzing the left singular value vector based on the mode shape coherence criterion to obtain the self-power spectral density function of the single degree of freedom system with corresponding frequency.
Specifically, as shown in fig. 2, the left singular value vector in step S104 represents the mode shape of the system, the left singular value vector is analyzed based on the mode shape coherence criterion to obtain a self-power spectral density function of the single degree of freedom system with corresponding frequency, and the maximum singular value curve obtained in step S104 is based on the mode frequency to be analyzed to obtain the self-power spectral density function of the single degree of freedom system according to the mode amplitude coherence condition.
Step S108, obtaining the modal parameters of the single-mode system according to the self-power spectral density function.
Optionally, obtaining the modal parameters of the single-mode system according to the self-power spectral density function in step S108 includes performing frequency domain fitting on the self-power spectral density function through an orthogonal polynomial, and performing modal parameter estimation on the single-degree-of-freedom system through polynomial fitting to obtain the modal parameters of the single-mode system, wherein the modal parameters of the single-mode system include frequency and damping of the single-degree-of-freedom system.
Specifically, as shown in fig. 2, the frequency domain fitting is performed on the self-power spectral density function of the single-degree-of-freedom system obtained in step S106 through an orthogonal polynomial, and the modal parameter estimation is directly performed on the single-degree-of-freedom system through polynomial fitting, so that the frequency and the damping of the single-degree-of-freedom system are finally obtained.
In summary, the signal processing method provided by the embodiment of the application aims at the turbulent flow response signal analysis in the flutter test flight, can improve the accuracy of modal parameter estimation, and simultaneously avoids the problem of insufficient calculation instantaneity caused by a random subspace method.
According to the embodiment of the invention, a power spectrum density function matrix is calculated according to the acquired multi-channel turbulence response signals, a maximum singular value curve is acquired according to the power spectrum density function matrix, a self-power spectrum density function of a single-degree-of-freedom system is acquired according to the maximum singular value curve, and a modal parameter of the single-mode system is acquired according to the self-power spectrum density function. That is, the embodiment of the invention can solve the problem of turbulent flow response signal analysis in the flutter test flight test in the related technology, achieves the technical effects of improving the accuracy of modal parameter estimation and avoiding the defect of calculation instantaneity caused by a random subspace method.
According to another aspect of the embodiment of the present invention, a signal processing apparatus is provided, and fig. 3 is a schematic diagram of the signal processing apparatus provided in the embodiment of the present invention. As shown in fig. 3, the system comprises a signal processing module 32 for calculating a power spectrum density function matrix according to the acquired multi-channel turbulence response signal, a first acquisition module 34 for acquiring a maximum singular value curve according to the power spectrum density function matrix, a second acquisition module 36 for acquiring a self-power spectrum density function of the single-degree-of-freedom system according to the maximum singular value curve, and a third acquisition module 38 for obtaining a modal parameter of the single-mode system according to the self-power spectrum density function.
Optionally, the signal processing module 32 includes a signal processing unit, configured to perform power spectrum analysis on the multi-channel turbulence response signal, to obtain a power spectrum density function matrix corresponding to each signal in the multi-channel turbulence response signal, where the power spectrum density function matrix is an autocorrelation power spectrum density function matrix or a cross correlation power spectrum density function matrix corresponding to each signal.
Further, the first obtaining module 34 may optionally include a first obtaining unit, configured to perform singular value decomposition on the power spectrum density function matrix at each frequency point based on the orthogonality of the frequency domains, so as to obtain a maximum singular value curve of each frequency band.
Optionally, the first obtaining module 34 further includes a decomposition unit, configured to perform singular value decomposition on the power spectrum density function matrix at each frequency point based on the frequency domain orthogonality, to obtain a left singular value vector corresponding to each frequency point, where the left singular value vector represents a mode shape of the system.
Further, optionally, obtaining the self-power spectral density function of the single degree of freedom system according to the maximum singular value curve comprises analyzing the left singular value vector based on a modal shape coherence criterion to obtain the self-power spectral density function of the single degree of freedom system of the corresponding frequency.
Optionally, obtaining the modal parameters of the single-mode system according to the self-power spectral density function comprises performing frequency domain fitting on the self-power spectral density function through an orthogonal polynomial, and performing modal parameter estimation on the single-degree-of-freedom system through polynomial fitting to obtain the modal parameters of the single-mode system, wherein the modal parameters of the single-mode system comprise frequency and damping of the single-degree-of-freedom system.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention.
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| CN107133195B (en) * | 2017-04-14 | 2019-08-09 | 大连理工大学 | A Model Ordering Method for Modal Identification of Engineering Structures |
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| CN114354170B (en) * | 2022-01-07 | 2022-10-25 | 大连理工大学 | A Structural Damping Ratio Identification Method Based on Unknown Impulse Response |
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