Improved local mean decomposition mode identification method and system for flutter test flight

Abstract

The invention discloses an improved local mean decomposition modal recognition method and system for flutter test flight, and relates to the technical field of modal recognition. The method comprises the following steps: receiving an acceleration response signal a (t) generated after a structural member is subjected to pulse excitation as an original signal, and calculating from the original signal to obtain a local mean function mij (t) and a frequency modulation-like signal hij (t); and an envelope estimation function rij (t) is calculated based on the frequency-modulated signal hij (t), and the envelope estimation function rij (t) is used to replace the envelope estimation function in the original signal to demodulate the frequency-modulated signal hij (t). According to the method, for the requirement of flight flutter characteristic evaluation for real-time evaluation, the modal parameter identification speed is increased, the requirement of real-time evaluation is met, and for the problem of iteration divergence after demodulation of a frequency-modulated signal in a traditional method, a new envelope estimation function demodulation method is adopted, the number of needed iterations is reduced, and the accuracy of the real-time evaluation is improved. And the calculation speed is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of modal identification, and in particular to an improved local mean decomposition modal identification method and system for flutter flight testing. Background Art

[0002] Aircraft flutter is a self-excited vibration phenomenon caused by aerodynamic excitation caused by the vibration of the aircraft itself after being disturbed. Flutter often causes rapid structural damage within a few seconds, seriously affecting the safety of the aircraft. Therefore, flutter has always been a key aeroelastic problem in aircraft design. The basic data for aircraft flutter analysis are modal parameters such as aircraft frequency / vibration shape. The obtained structural vibration modal parameters are the main parameters that determine the structural dynamic characteristics, which can provide reference for model modification, structural damage diagnosis, external dynamic load identification, etc. In the field of aircraft flutter research, based on the needs of flutter boundary prediction and real-time flutter evaluation, there is a strong demand for rapid calculation.

[0003] However, for modal parameter identification, frequency domain identification method and time domain identification method are generally used at present. However, both methods require that the response signal and the excitation signal are stable, but not all actual engineering signals can meet this requirement, such as flutter test flight data. Therefore, a time-frequency joint method is needed to analyze non-stationary signals. Local mean decomposition, as an adaptive time-frequency analysis method, can decompose multi-component signals into the sum of multiple PF components with clear physical meanings, and combine the instantaneous amplitudes and instantaneous frequencies of all components to obtain the complete time-frequency distribution of the original signal. However, it itself has the problem that there is a probability that the iteration cannot converge after demodulation and the calculation speed is slow. For this reason, the present invention proposes an improved local mean decomposition modal identification method and system for flutter test flight. Summary of the invention

[0004] The purpose of the present invention is to provide an improved local mean decomposition modal identification method and system for flutter test flight, which improves the modal parameter identification speed and meets the demand for real-time evaluation of flight flutter characteristics evaluation.

[0005] According to a first aspect of the present invention, in order to achieve the above-mentioned purpose, the present invention provides the following technical solution: an improved local mean decomposition modal identification method for flutter test flight is applied to aircraft structure flutter analysis, comprising the following steps:

[0006] The acceleration response signal a(t) generated by the receiving structure after the pulse excitation is taken as the original signal, and the local mean function m is calculated from the original signal. ij (t) and quasi-FM signal h ij (t);

[0007] Based on the quasi-FM signal h ij(t) Calculate the envelope estimation function r ij (t), using the envelope estimation function r ij (t) Replace the envelope estimation function in the original signal for the FM signal h ij (t) is demodulated, and then the envelope estimation function r is modified ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t);

[0008] Based on the modified envelope estimation function e ij (t) Obtain the envelope signal e i (t), and with the pure FM signal s in (t) are multiplied to obtain the PF components. After multiple iterations, all PF components are obtained, and then the modal parameters are obtained.

[0009] Furthermore, the acceleration response signal a(t) generated by the receiving structure after the pulse excitation is taken as the original signal, and the local mean function m is calculated from the original signal. ij (t) and quasi-FM signal h ij (t), as follows:

[0010] (21) Apply pulse excitation to the structural component and collect the acceleration response signal a(t) of the structure;

[0011] (22) Find every local extreme point n of a(t) i , calculate the average value m i , fitting to obtain the local mean function m ij (t);

[0012]

[0013] In the formula, m i is the ith average value; n i is the ith local extreme point.

[0014] (23) Separate the quasi-FM signal h from the original signal a(t) ij (t):

[0015] h ij (t) = a(t) - m ij (t) (2)

[0016] In the formula, m ij (t) is the local mean function of the i-th PF component in the j-th iteration; h ij (t) is the quasi-FM signal in the jth iteration of the i-th PF component.

[0017] Furthermore, based on the quasi-FM signal hij (t) Calculate the envelope estimation function r ij (t), using the envelope estimation function r ij (t) Replace the envelope estimation function in the original signal for the FM signal h ij (t) is demodulated, and then the envelope estimation function r is modified ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t), as follows:

[0018] (31) Find h ij (t) Every local extreme point n hi , calculate the local amplitude r i , for all local amplitudes r i Fitting is performed to obtain the envelope estimation function r ij (t):

[0019]

[0020] Where n hi is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local extreme point of (t); r i is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local amplitude of (t); r ij (t) is h ij (t) envelope estimation function;

[0021] (32) Using the envelope estimation function r obtained in (31) ij (t) Demodulate the quasi-FM signal, replace the traditional demodulation method, and obtain a quasi-pure FM signal s ij (t):

[0022]

[0023] In the formula, s ij (t) is the quasi-pure FM signal in the jth iteration of the i-th PF component;

[0024] (33) Modify the envelope estimation function r obtained in (31) ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t):

[0025]

[0026] In the formula, e ij(t) is the modified envelope estimation function of the i-th PF component in the j-th iteration; r i is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local amplitude of (t); m ij (t) is the local mean function of the i-th PF component in the j-th iteration.

[0027] Furthermore, based on the modified envelope estimation function e ij (t) Obtain the envelope signal e i (t), and with the pure FM signal s in (t) are multiplied to obtain the PF components. After multiple iterations, all PF components are obtained, and then the modal parameters are obtained, as follows:

[0028] (41) The function e is estimated by all envelopes ij (t) multiplied to get the envelope signal e i (t):

[0029]

[0030] (42) The envelope signal e i (t) and pure FM signal s in (t) multiply to obtain a PF component;

[0031] PF i =e i (t)gs in (t) (7);

[0032] (43) When the residual satisfies the monotonic condition, the iteration is terminated, all PF components are obtained, and then the modal parameters are obtained.

[0033] According to a second aspect of the present invention, the present invention provides an improved local mean decomposition modal identification system for flutter flight testing, which is used to implement the above-mentioned improved local mean decomposition modal identification method for flutter flight testing, comprising:

[0034] The first calculation module is used to receive the acceleration response signal a(t) generated by the structural component after the pulse excitation as the original signal, and calculate the local mean function m from the original signal ij (t) and quasi-FM signal h ij (t);

[0035] The second calculation module is used to calculate the frequency modulation signal h based on the ij (t) Calculate the envelope estimation function r ij (t), using the envelope estimation function r ij (t) Replace the envelope estimation function in the original signal for the FM signal h ij(t) is demodulated, and then the envelope estimation function r is modified ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t);

[0036] Iterative output module for estimating the function e based on the modified envelope ij (t) Obtain the envelope signal e i (t), and with the pure FM signal s in (t) are multiplied to obtain the PF components. After multiple iterations, all PF components are obtained, and then the modal parameters are obtained.

[0037] According to a third aspect of the present invention, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned improved local mean decomposition modal identification method for flutter flight testing is adopted.

[0038] According to a fourth aspect of the present invention, the present invention provides a storage medium comprising computer executable instructions, wherein the computer executable instructions are used to execute the above-mentioned improved local mean decomposition modal identification method for flutter flight testing when executed by a computer processor.

[0039] According to a fifth aspect of the present invention, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to load and execute the above-mentioned improved local mean decomposition modal identification method for flutter flight testing.

[0040] The present invention has at least the following beneficial effects:

[0041] In view of the problem of iterative divergence after demodulating quasi-FM signals in traditional methods, the present invention proposes a demodulation method using an envelope estimation function, which improves the demodulation speed, reduces the number of required iterations, and further improves the calculation speed, which has important engineering significance in modal identification.

[0042] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the process of the identification method described in the embodiment of the present invention;

[0044] Figure 2 Schematic diagram of the operating principle of the identification method described in an embodiment of the present invention;

[0045] Figure 3A three-degree-of-freedom system response diagram under input pulse excitation according to an embodiment of the present invention;

[0046] Figure 4 This is the first PF component result diagram in an embodiment of the present invention;

[0047] Figure 5 is a second PF component result diagram in an embodiment of the present invention;

[0048] Figure 6 This is a third PF component result diagram in an embodiment of the present invention;

[0049] Figure 7 This is a diagram showing the modal frequency result corresponding to the first PF component in an embodiment of the present invention;

[0050] Figure 8 This is a diagram showing the modal frequency results corresponding to the second PF component in an embodiment of the present invention;

[0051] Fig. 9 This is a diagram showing the modal frequency results corresponding to the third PF component in an embodiment of the present invention;

[0052] Fig.10 Result diagram of modal damping ratio corresponding to the first PF component in an embodiment of the present invention;

[0053] Fig.11 is a result diagram of the modal damping ratio corresponding to the second PF component in an embodiment of the present invention;

[0054] Fig.12 This is a diagram showing the modal damping ratio results corresponding to the third PF component in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0056] The dynamic measurement and analysis system is an analytical instrument used in the basic disciplines of physics, engineering and technology science. It was put into use on November 1, 2019. It has high imaging accuracy and high-speed transmission. The imaging accuracy can reach 500 details per second, the time accuracy is 1us-1s, and the maximum dynamic range is not less than 60dB. The system supports external and internal triggering methods, and has real-time image processing functions such as histograms and other real-time image processing functions. It is also equipped with dustproof devices and pattern marking tools, which are suitable for complex motion tracking environments and vibration spectrum measurements.

[0057] See also Figure 1 and Figure 2 The present invention provides a technical solution: an improved local mean decomposition modal identification method for flutter test flight, which is applied to aircraft structure flutter analysis, comprising the following steps:

[0058] S1. Receive the acceleration response signal a(t) generated by the structural component after the pulse excitation as the original signal, and calculate the local mean function m from the original signal ij (t) and quasi-FM signal h ij (t), as follows:

[0059] (S11) applying pulse excitation to the structural member, and collecting the acceleration response signal a(t) of the structure through a dynamic measurement and analysis system;

[0060] (S12) Find every local extreme point n of a(t) i , calculate the average value m i , fitting to obtain the local mean function m ij (t);

[0061]

[0062] (S13) Separate the quasi-FM signal h from the original signal a(t) ij (t):

[0063] h ij (t) = a(t) - m ij (t) (2)

[0064] In the formula, m ij (t) is the local mean function of the i-th PF component in the j-th iteration; h ij (t) is the quasi-FM signal in the jth iteration of the i-th PF component;

[0065] S2. Based on quasi-FM signal h ij (t) Calculate the envelope estimation function r ij (t), using the envelope estimation function r ij (t) Replace the envelope estimation function in the original signal for the FM signal h ij (t) is demodulated, and then the envelope estimation function r is modified ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t), as follows:

[0066] (S21) Find h ij (t) Every local extreme point n hi , calculate the local amplitude r i, for all local amplitudes r i Fitting is performed to obtain the envelope estimation function r ij (t):

[0067]

[0068] Where n hi is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local extreme point of (t); r i is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local amplitude of (t); r ij (t) is h ij (t) envelope estimation function;

[0069] (S22) Using the envelope estimation function r obtained in (S21) ij (t) Demodulate the quasi-FM signal, replace the traditional demodulation method, and obtain a quasi-pure FM signal s ij (t):

[0070]

[0071] In the formula, s ij (t) is the quasi-pure FM signal in the jth iteration of the i-th PF component;

[0072] (S23) Modify the envelope estimation function r obtained in (S21) ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t):

[0073]

[0074] In the formula, e ij (t) is the modified envelope estimation function of the i-th PF component in the j-th iteration; r i is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local amplitude of (t); m ij (t) is the local mean function of the i-th PF component in the j-th iteration;

[0075] S3. Estimation function based on the modified envelope e ij (t) Obtain the envelope signal e i (t), and with the pure FM signal s in (t) are multiplied to obtain the PF components. After multiple iterations, all PF components are obtained, and then the modal parameters are obtained, as follows:

[0076] (S31) Estimating function e from all envelopes ij (t) multiplied to get the envelope signal e i (t):

[0077]

[0078] (S32) The envelope signal e i (t) and pure FM signal s in (t) multiply to obtain a PF component;

[0079] PF i =e i (t)gs in (t) (7);

[0080] (S33) When the residual satisfies the monotonic condition, the iteration is terminated, all PF components are obtained, and then the modal parameters are obtained.

[0081] Next, we will further explain the technical solution:

[0082] Figure 3 This is the response diagram of the three-degree-of-freedom system under input pulse excitation. The following identification simulation is based on the three-degree-of-freedom system. The specific steps are as follows:

[0083] (1) Apply pulse excitation to the structural component to obtain the acceleration response signal a(t) of the structural component;

[0084] (2) Find every local extreme point n of a(t) i , calculate the average value m i , fitting to obtain the local mean function m ij (t);

[0085] (3) Separate the quasi-FM signal h from the original signal a(t) ij (t):

[0086] h ij (t) = a(t) - m ij (t) (1)

[0087] In the formula, m ij (t) is the local mean function of the i-th PF component in the j-th iteration; h ij (t) is the quasi-FM signal in the jth iteration of the i-th PF component;

[0088] (4) Find h ij (t) Every local extreme point n hi , calculate the local amplitude r i , for all local amplitudes r i Fitting to get the envelope estimation function r ij(t), replacing the envelope estimation function used in traditional methods:

[0089]

[0090] Where n hi is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local extreme point of (t); r i is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local amplitude of (t); r ij (t) is h ij (t) is the envelope estimation function, i and j are synonymous with it;

[0091] (5) Use the envelope estimation function r in (4) ij (t) Demodulate the quasi-FM signal, replace the traditional demodulation method, and obtain a quasi-pure FM signal s ij (t):

[0092]

[0093] In the formula, s ij (t) is the quasi-pure FM signal in the jth iteration of the i-th PF component.

[0094] (6) Modify the envelope estimation function r in (4) ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t):

[0095]

[0096] In the formula, e ij (t) is the modified envelope estimation function of the i-th PF component in the j-th iteration;

[0097] (7) The function e is estimated by all envelopes ij (t) multiplied to get the envelope signal e i (t):

[0098]

[0099] (8) The envelope signal e i (t) and pure FM signal s in (t) multiply to obtain a PF component;

[0100] (9) After multiple iterations, all PF components are obtained, such as Figure 4 , 5 , as shown in 6;

[0101] (10) Then the modal parameters are obtained, and the results are as follows Figure 7-Figure 12 shown.

[0102] In summary, this embodiment meets the demand for real-time evaluation of flight flutter characteristics, improves the speed of modal parameter identification, and meets the demand for real-time evaluation. In addition, in view of the problem of iterative divergence after demodulating quasi-FM signals in traditional methods, this embodiment proposes a method of demodulating using an envelope estimation function, which improves the demodulation speed, reduces the number of required iterations, and further improves the calculation speed, which has important engineering significance in modal identification.

[0103] Embodiment 2:

[0104] The present invention provides an improved local mean decomposition modal identification system for flutter flight testing, which is used to implement the improved local mean decomposition modal identification method for flutter flight testing described in the first embodiment, including:

[0105] The first calculation module is used to receive the acceleration response signal a(t) generated by the structural component after the pulse excitation as the original signal, and calculate the local mean function m from the original signal ij (t) and quasi-FM signal h ij (t);

[0106] The second calculation module is used to calculate the frequency modulation signal h based on the ij (t) Calculate the envelope estimation function r ij (t), using the envelope estimation function r ij (t) Replace the envelope estimation function in the original signal for the FM signal h ij (t) is demodulated, and then the envelope estimation function r is modified ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t);

[0107] Iterative output module for estimating the function e based on the modified envelope ij (t) Obtain the envelope signal e i (t), and with the pure FM signal s in (t) are multiplied to obtain the PF components. After multiple iterations, all PF components are obtained, and then the modal parameters are obtained.

[0108] Specifically, the first calculation module, the second calculation module and the iterative output module can be embedded in a computer processing system. The computer calls the modules to complete the task of improving the speed of modal parameter identification based on the improved local mean decomposition modal identification method for flutter flight testing provided above. The first calculation module, the second calculation module and the iterative output module can perform operations according to the specific steps given in the improved local mean decomposition modal identification method for flutter flight testing.

[0109] It should be noted that it should be understood that the division of the various modules of the above system is only the division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated, and these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. For example, the first computing module can be a separately established processing element, or it can be integrated in a certain chip of the above-mentioned device. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called and executed by a certain processing element of the above-mentioned device. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by the hardware integrated logic circuit in the processor element or the instruction in the form of software.

[0110] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital singnal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0111] Embodiment three:

[0112] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned improved local mean decomposition modal identification method for flutter flight testing is adopted.

[0113] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device can also include input and output devices, network access devices and buses.

[0114] Furthermore, the processor may adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be adopted. The general-purpose processor may adopt a microprocessor or any conventional processor, etc., and the present application does not impose any restrictions on this.

[0115] Embodiment 4:

[0116] The present invention provides a storage medium containing computer executable instructions, and the computer executable instructions are used to execute the above-mentioned improved local mean decomposition modal identification method for flutter test flight when executed by a computer processor.

[0117] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer-readable medium includes but is not limited to the above-mentioned components.

[0118] Embodiment five:

[0119] The present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to load and execute the above-mentioned improved local mean decomposition modal identification method for flutter flight testing.

[0120] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0121] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can also be a centered element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a centered element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are only for illustrative purposes and are not intended to be the only implementation method.

[0122] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0123] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

Claims

1. An improved local mean decomposition modal identification method for flutter test flight is applied to aircraft structure flutter analysis, which is characterized by: The following steps are involved: The acceleration response signal a(t) generated by the receiving structure after the pulse excitation is taken as the original signal, and the local mean function m is calculated from the original signal. ij (t) and quasi-FM signal h ij (t); Based on the quasi-FM signal h ij (t) Calculate the envelope estimation function r ij (t), using the envelope estimation function r ij (t) Replace the envelope estimation function in the original signal for the FM signal h ij (t) is demodulated, and then the envelope estimation function r is modified ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t); Based on the modified envelope estimation function e ij (t) Get the envelope signal e i (t), and with the pure FM signal s in (t) are multiplied to obtain the PF components. After multiple iterations, all PF components are obtained, and then the modal parameters are obtained.

2. The improved local mean decomposition modal identification method for flutter flight testing according to claim 1, characterized in that: The acceleration response signal a(t) generated by the receiving structure after the pulse excitation is taken as the original signal, and the local mean function m is calculated from the original signal. ij (t) and quasi-FM signal h ij (t), as follows: (21) Apply pulse excitation to the structural component and collect the acceleration response signal a(t) of the structure; (22) Find every local extreme point n of a(t) i , calculate the average value m i , fitting to obtain the local mean function m ij (t); In the formula, m i is the ith average value; n i is the i-th local extreme point; (23) Separate the quasi-FM signal h from the original signal a(t) ij (t): h ij (t)=a(t)-m ij (t) (2) In the formula, m ij (t) is the local mean function of the i-th PF component in the j-th iteration; h ij (t) is the quasi-FM signal in the jth iteration of the i-th PF component.

3. The improved local mean decomposition modal identification method for flutter flight testing according to claim 1, characterized in that: Based on the quasi-FM signal h ij (t) Calculate the envelope estimation function r ij (t), using the envelope estimation function r ij (t) Replace the envelope estimation function in the original signal for the FM signal h ij (t) is demodulated, and then the envelope estimation function r is modified ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t), as follows: (31) Find h ij (t) Every local extreme point n hi , calculate the local amplitude r i , for all local amplitudes r i Fitting is performed to obtain the envelope estimation function r ij (t): Where n hi is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local extreme point of (t); r i is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local amplitude of (t); r ij (t) is h ij (t) envelope estimation function; (32) Using the envelope estimation function r obtained in (31) ij (t) Demodulate the quasi-FM signal, replace the traditional demodulation method, and obtain a quasi-pure FM signal s ij (t): In the formula, s ij (t) is the quasi-pure FM signal in the jth iteration of the i-th PF component; (33) Modify the envelope estimation function r obtained in (31) ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t): In the formula, e ij (t) is the modified envelope estimation function of the i-th PF component in the j-th iteration; r i is the quasi-FM signal h in the jth iteration of the i-th PF component ij The i-th local amplitude of (t); m ij (t) is the local mean function of the i-th PF component in the j-th iteration.

4. The improved local mean decomposition modal identification method for flutter flight testing according to claim 1, characterized in that: Based on the modified envelope estimation function e ij (t) Get the envelope signal e i (t), and with the pure FM signal s in (t) are multiplied to obtain the PF components. After multiple iterations, all PF components are obtained, and then the modal parameters are obtained, as follows: (41) The function e is estimated by all envelopes ij (t) multiplied to get the envelope signal e i (t): (42) The envelope signal e i (t) and pure FM signal s in (t) multiply to obtain a PF component; PF i =e i (t)gs in (t) (7)? (43) When the residual satisfies the monotonic condition, the iteration is terminated, all PF components are obtained, and then the modal parameters are obtained.

5. An improved local mean decomposition modal identification system for flutter flight testing, used to implement the improved local mean decomposition modal identification method for flutter flight testing as claimed in any one of claims 1 to 4, characterized in that: include: The first calculation module is used to receive the acceleration response signal a(t) generated by the structural component after the pulse excitation as the original signal, and calculate the local mean function m from the original signal ij (t) and quasi-FM signal h ij (t); The second calculation module is used to calculate the frequency modulation signal h ij (t) Calculate the envelope estimation function r ij (t), using the envelope estimation function r ij (t) Replace the envelope estimation function in the original signal for the FM signal h ij (t) is demodulated, and then the envelope estimation function r is modified ij The difference between (t) and the original signal envelope estimation function is used to obtain the modified envelope estimation function e ij (t); Iterative output module for estimating the function e based on the modified envelope ij (t) Get the envelope signal e i (t), and with the pure FM signal s in (t) are multiplied to obtain the PF components. After multiple iterations, all PF components are obtained, and then the modal parameters are obtained.

6. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the improved local mean decomposition modal identification method for flutter flight testing according to any one of claims 1 to 4 is adopted.

7. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, the computer executable instructions are used to perform the improved local mean decomposition modal identification method for flutter flight testing according to any one of claims 1 to 4.

8. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, is used to load and execute the improved local mean decomposition modal identification method for flutter flight testing according to any one of claims 1 to 4.

Images