Modal identification method and device based on combined discrete fourier transform

Abstract

The present application relates to the technical field of signal processing, in particular to a modal identification method and device based on combined discrete Fourier transform, wherein the method comprises: estimating the maximum dominant modal order in the vibration field of a to-be-identified geometric structure, and determining the sampling number; designing a combined uniform sampling scheme based on a preset sampling limitation condition, obtaining a plurality of sampling data according to the combined uniform sampling scheme and the sampling number, and collecting the time-domain vibration signals of each sampling data; grouping and combining the plurality of sampling data, constructing a combined uniform sampling aliasing matrix for the grouped and combined sampling data; performing time and space Fourier transform on the time-domain vibration signals to obtain a modal spectrum with aliasing; establishing a matrix equation according to the combined uniform sampling aliasing matrix and the modal spectrum with aliasing, and solving the optimal modal spectrum in the vibration field of the to-be-identified geometric structure according to the matrix equation. The method enhances the sampling flexibility and anti-failure capability, and can reconstruct an accurate modal spectrum by using a non-uniform sampling mode.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, in particular to a modal identification method and device based on combined discrete Fourier transform. BACKGROUND

[0002] Modality is a very important concept in the field of structure, vibration and noise, which is physically embodied as a vibration state with a specific mode, and mathematically refers to the base function of a homogeneous ordinary differential equation. For a linear system, the actual vibration form can be decomposed into a linear combination of infinite order modes, but the main role is often played by a few dominant modes, so the dynamic characteristics of the system are usually evaluated by modal analysis. The ultimate goal of modal analysis is to provide a basis for the vibration characteristics analysis, vibration fault diagnosis and prediction, and optimization design of the dynamic characteristics of the structure system, and it is widely used in the fields of machinery, power, construction, water conservancy, aviation and aerospace.

[0003] Modal identification is to solve the modal coefficients of each order. In practice, it is not easy to identify the modal, which is limited by the measurement equipment. The sensor can only get local vibration information, so it is necessary to get the result as quickly and accurately as possible through the limited measurement point data by the modal identification method. At present, the modal identification method is mainly divided into two categories. One is the direct matrix inversion method, which establishes the transfer matrix between the to-be-solved modal amplitude vector and the measurement point signal, so as to directly convert the problem into a matrix inversion problem. This method is intuitive and simple, but the calculation speed is very slow, and the calculation complexity is O(N 3 ); the second is the modal identification method using discrete Fourier transform method, which is the most widely used method. This method uses the characteristics of the discrete Fourier matrix of uniform sampling to get the modal coefficients of each order within the prior highest modal order through the discrete Fourier transform under the quantitative limitation of the Nyquist sampling theorem. This method has fast calculation speed, low calculation complexity of O(N 2 ), and high accuracy, but due to the requirement of uniform sampling, the measurement cost is high, the anti-failure ability is poor, and it is easily limited in industrial practical application. In view of this problem, some researches have proposed a non-uniform sampling based discrete Fourier transform method, which can get an approximate solution of the modal spectrum, and to a certain extent, improve the flexibility of the discrete Fourier transform method sampling, but at the cost of precision reduction. Even without background noise measurement data, the accurate modal spectrum cannot be identified. SUMMARY

[0004] The application provides a modal identification method and device based on combined discrete Fourier transform, which solves the problem that related modal identification methods cannot simultaneously meet the requirements of fast speed, low complexity, low cost and high failure resistance due to the requirement of uniform sampling in actual application. The proposed non-uniform sampling discrete Fourier transform can simultaneously meet the above requirements, but cannot identify relatively accurate modal spectrum.

[0005] The first aspect embodiment of the application provides a modal identification method based on combined discrete Fourier transform, which comprises the following steps: estimating the maximum dominant modal order in the vibration field of a to-be-identified geometric structure to determine the sampling number; designing a combined uniform sampling scheme based on a preset sampling limitation condition, obtaining a plurality of sampling data according to the combined uniform sampling scheme and the sampling number, and collecting a time-domain vibration signal of at least one sampling data; grouping and combining the plurality of sampling data, constructing a combined uniform sampling aliasing matrix according to the grouped and combined sampling data; performing time and space Fourier transform on the time-domain vibration signal of the at least one sampling data to obtain a modal spectrum with aliasing; establishing a matrix equation according to the combined uniform sampling aliasing matrix and the modal spectrum with aliasing, and solving the optimal modal spectrum in the vibration field of the to-be-identified geometric structure according to the matrix equation.

[0006] Optionally, the relationship between the maximum dominant modal order and the sampling number is 2|M|<N, wherein M is the maximum dominant modal order, and N is the sampling number.

[0007] Optionally, the grouping and combining of the plurality of sampling data and the construction of the combined uniform sampling aliasing matrix according to the grouped and combined sampling data comprise:

[0008] The N sampling data are divided into L groups, and the number of each group is {1, 2, …, N / L}. L}N l is the sampling number of the lth group.

[0009] Based on the uniform arrangement of each group of sampling data in the modal direction, a grouping scheme and a geometric position are determined to construct the combined uniform sampling aliasing matrix.

[0010] Optionally, the time and space Fourier transform on the time-domain vibration signal of the at least one sampling data to obtain the modal spectrum with aliasing comprises:

[0011] Performing fast Fourier transform on the time-domain vibration signal of the at least one sampling data to obtain a frequency-domain signal sequence.

[0012] Selecting a target frequency of interest in the frequency-domain signal sequence to construct a frequency-domain vector.

[0013] Perform a spatial Fourier transform on each frequency domain amplitude vector in the frequency domain vector to obtain the aliased mode spectrum.

[0014] Optionally, the aliased modal spectrum is:

[0015]

[0016] in, For the l-th group of modes with aliasing, Here is the matrix expression for the spatial Fourier transform, and its elements are:

[0017]

[0018] Where p = 1, 2, ..., N l Let q be the row index of the matrix, where q = 1, 2, ..., N. l For the column index of the matrix, To round up, θ lq Let be the geometric position of the q-th sampling point in the l-th group, e be the natural constant, be the base of the natural logarithm function, and i be the imaginary unit.

[0019] Optionally, the matrix equation is:

[0020]

[0021] Where α=α -|M| …0… +|M| ] T For the modal spectrum to be determined, α m The complex amplitude of the m-th order mode. The modal spectrum is characterized by aliasing. This is for combining uniform sampling aliasing matrices.

[0022] Optionally, solving for the optimal modal spectrum in the vibration field of the geometric structure to be identified based on the matrix equation includes:

[0023] Construct an augmented matrix based on the matrix equation;

[0024] Perform row swapping on the augmented matrix to make the diagonal elements non-zero, resulting in the swapped augmented matrix;

[0025] The non-zero elements in the lower triangular part of the augmented matrix after the exchange process are successively transformed into zero elements by Givens transformation to obtain the upper triangular matrix;

[0026] Substitute the upper triangular matrix back and normalize it to transform it into an identity matrix with only diagonal elements equal to 1, thereby obtaining the optimal modal spectrum within the vibration field of the geometric structure to be identified.

[0027] The second aspect embodiment of the present application provides a modal identification device based on combined discrete Fourier transform, comprising: a determination module configured to estimate a maximum dominant modal order in a vibration field of a to-be-identified geometric structure to determine a sampling number; a design module configured to design a combined uniform sampling scheme based on a preset sampling limitation condition, acquire a plurality of sampling data according to the combined uniform sampling scheme and the sampling number, and collect a time-domain vibration signal of at least one sampling data; a grouping and combining module configured to group and combine the plurality of sampling data, and construct a combined uniform sampling aliasing matrix according to the grouped and combined sampling data; a transform module configured to perform time and space Fourier transform on the time-domain vibration signal of the at least one sampling data to obtain a modal spectrum with aliasing; and a solving module configured to establish a matrix equation according to the combined uniform sampling aliasing matrix and the modal spectrum with aliasing, and solve an optimal modal spectrum in the vibration field of the to-be-identified geometric structure according to the matrix equation.

[0028] The third aspect embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the modal identification method based on combined discrete Fourier transform as described in the above embodiments.

[0029] The fourth aspect embodiment of the present application provides a computer readable storage medium, which stores a computer program executable by a processor to implement the modal identification method based on combined discrete Fourier transform as described above.

[0030] The modal identification method and device based on combined discrete Fourier transform of the embodiments of the present application have the following beneficial effects:

[0031] Compared with the traditional discrete Fourier transform method, the sampling flexibility and failure resistance are further enhanced, and the calculation speed is faster;

[0032] Compared with the discrete Fourier transform method based on non-uniform sampling, the precision is higher, and the accurate modal coefficient solution can be identified in the noise-free case.

[0033] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0034] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0035] Figure 1 A flowchart of a modal identification method based on combined discrete Fourier transform according to the embodiments of the present application is provided.

[0036] Figure 2 A flowchart of a modal identification method based on combined discrete Fourier transform according to an embodiment of the present application is provided.

[0037] Figure 3 A flowchart of a QR decomposition algorithm based on Givens transform according to an embodiment of the present application is provided.

[0038] Figure 4 A schematic diagram of a microphone installation position according to an embodiment of the present application is provided.

[0039] Figure 5 A schematic diagram of a circumferential acoustic modal spectrum amplitude identified by a classical DFT method and a modal spectrum amplitude identified by the present application according to an embodiment of the present application is provided.

[0040] Figure 6 A block schematic diagram of a modal identification device based on combined discrete Fourier transform according to an embodiment of the present application is provided.

[0041] Figure 7 A structural schematic diagram of an electronic device according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0042] Embodiments of the present application are described in detail below with reference to the accompanying drawings, in which the same or similar components have the same or similar reference numbers throughout the several views. The embodiments described below are exemplary and are intended to explain the present application, and should not be understood as limiting the present application.

[0043] A modal identification method and device based on combined discrete Fourier transform according to an embodiment of the present application are described below with reference to the accompanying drawings.

[0044] Figure 1 A flowchart of a modal identification method based on combined discrete Fourier transform according to an embodiment of the present application is provided.

[0045] As shown in Figure 1 the modal identification method based on combined discrete Fourier transform includes the following steps:

[0046] In step S101, the maximum dominant modal order in the vibration field of the geometric structure to be identified is estimated to determine the sampling number.

[0047] Specifically, the order of the maximum dominant mode in the annular cavity to be identified is estimated by prior information such as geometric structure and vibration frequency, and the number of samples N is determined. According to the Nyquist sampling theorem, the relationship between the order of the maximum dominant mode and the number of samples is: 2|M| < 2, where M is the order of the maximum dominant mode and N is the number of samples.

[0048] In step S102, a combined uniform sampling scheme is designed based on preset sampling constraints. Multiple sampling data are obtained according to the combined uniform sampling scheme and the number of samples, and the time-domain vibration signal of at least one sampling data is collected.

[0049] Specifically, a combined uniform sampling scheme is designed based on the sampling constraints in practical applications. Multiple sampling data are obtained according to the combined uniform sampling scheme and the number of samples, that is, a certain number of sampling point arrays are obtained. At least one sampling data time-domain vibration signal can be collected using a signal synchronous acquisition instrument.

[0050] In step S103, multiple sampled data are grouped and combined, and a combined uniform sampling aliasing matrix is ​​constructed based on the grouped and combined sampled data.

[0051] Furthermore, in one embodiment of the present invention, multiple sampled data are grouped and combined, and a combined uniform sampling aliasing matrix is ​​constructed based on the grouped and combined sampled data, including:

[0052] Divide the N sampled data into L groups, with each group containing {1, 2, ..., ...} L}, N l The number of samples in the l-th group;

[0053] Based on the uniform arrangement of each group of sampled data in the modal direction, the grouping scheme and geometric position are determined to construct a combined uniform sampling aliasing matrix.

[0054] Specifically, such as Figure 2 As shown, the N sampling points are divided into L groups, with each group containing {1, 2, ..., ...} L}, N l N represents the number of sampling points in the l-th group. Considering data reuse, N... ∑ =1+2+…+ L ≥N; Each group of sampling points is uniformly distributed along the modal direction. Taking a circle as an example, that is... It is uniformly distributed on the circumference, where θ lk This represents the geometric position (angle) of the k-th sampling point in the l-th group. After determining the grouping scheme and geometric positions, a combined uniform sampling aliasing matrix can be constructed. It consists of L aliasing matrices Stacked together, that is In a single aliasing matrix The elements in are:

[0055]

[0056] where p = 1, 2, …, N l is the row index of the matrix; q = 1, 2, …, 2+1 is the column index of the matrix; The symbol represents rounding up; represents a set of integers.

[0057] In step S104, time and spatial Fourier transforms are performed on the time-domain vibration signals of the at least one sampling data to obtain a modal spectrum with aliasing.

[0058] Further, in an embodiment of the present application, time and spatial Fourier transforms are performed on the time-domain vibration signals of the at least one sampling data to obtain a modal spectrum with aliasing, including:

[0059] Performing fast Fourier transform on the time-domain vibration signals of the at least one sampling data to obtain a frequency-domain signal sequence;

[0060] Selecting a target frequency of interest in the frequency-domain signal sequence to construct a frequency-domain vector;

[0061] Performing spatial Fourier transform on each frequency-domain amplitude vector in the frequency-domain vector to obtain a modal spectrum with aliasing.

[0062] Specifically, fast Fourier transform is first performed on the time-domain vibration signals measured at each sampling point in turn to obtain a frequency-domain signal sequence y f = FFT(y t ), where y t is a time-domain signal sequence, FFT is fast Fourier transform, and y f is a frequency-domain signal sequence;

[0063] Selecting a frequency ω of interest in the frequency-domain signal sequence to construct a frequency-domain vector where P lk (ω) is a complex amplitude of the kth sampling point in the lth group at the frequency ω;

[0064] Then, spatial Fourier transform is performed on each obtained frequency-domain amplitude vector p lω to obtain a modal coefficient result with aliasing, i.e., a modal spectrum with aliasing:

[0065]

[0066] where, is the lth group of modal results with aliasing, is a matrix expression of spatial Fourier transform, and the elements thereof are:

[0067]

[0068] where p = 1, 2, …, N l is the row index of the matrix, q = 1, 2, …, N l is the column index of the matrix, is the rounding up, θ lq is the geometric position of the qth sampling point of the lth group, e is a natural constant, is the base of the natural logarithm function, and i is an imaginary unit.

[0069] In step S105, a matrix equation is established according to the combined uniform sampling aliasing matrix and the aliased modal spectrum, and the optimal modal spectrum in the vibration field of the to-be-identified geometric structure is solved according to the matrix equation.

[0070] Further, in an embodiment of the present application, solving the optimal modal spectrum in the vibration field of the to-be-identified geometric structure according to the matrix equation comprises:

[0071] establishing an augmented matrix according to the matrix equation;

[0072] performing row exchange processing on the augmented matrix to make the elements on the diagonal not zero, and obtaining an exchanged augmented matrix;

[0073] making the non-zero elements in the lower triangular part of the exchanged augmented matrix into zero elements in sequence through Givens transformation, and obtaining an upper triangular matrix;

[0074] back-substituting and normalizing the upper triangular matrix to make the matrix into a unit matrix with only the diagonal elements being 1, and obtaining the optimal modal spectrum in the vibration field of the to-be-identified geometric structure.

[0075] Specifically, the matrix equation is:

[0076]

[0077] where α = α -|M| …0… +|M| ] T is the to-be-solved modal spectrum, α m is the complex amplitude of the m-order modal, is the aliased modal spectrum, is the combined uniform sampling aliasing matrix.

[0078] Since the matrix equation is a typical sparse matrix equation, in order to quickly obtain the result, the embodiment of the present application uses the QR decomposition method based on Givens transformation to solve the matrix sparseness, as shown in Figure 3 , and the specific process is as follows:

[0079] establishing an augmented matrix

[0080] The matrix is processed by row exchange to make the elements on the diagonal non-zero, denoted as

[0081] The non-zero elements in the lower triangular part of the matrix are sequentially converted to zero elements by Givens transformation, and finally the matrix is converted to an upper triangular matrix, denoted as

[0082] The upper triangular matrix is back-substituted and normalized to convert the matrix into a unit matrix with only diagonal elements being 1, obtaining [I|a], outputting a, and obtaining the optimal modal spectrum in the vibration field of the to-be-identified geometric structure.

[0083] The method for modal identification based on combined discrete Fourier transform proposed in the embodiments of the present application is further described below by taking the identification of circumferential acoustic modes in an aero-engine as an example.

[0084] With the wide use of turbofan engines in the field of civil aviation, combustion noise and fan noise have become the main sources of aero-engine noise. The main structure of a turbofan engine is composed of a fan, a compressor, a combustion chamber, a turbine and an exhaust nozzle. The combustion noise comes from the fuel combustion in the combustion chamber, and the fan noise comes from the rotation of the blades in the fan, compressor and turbine. The geometric structures of the above-mentioned noise-producing components are all three-dimensional annular cavities, and their acoustic modes include axial, radial and circumferential directions. For a compact aero-engine, the axial and radial distances are relatively narrow, and the circumferential mode becomes the dominant acoustic mode. Therefore, the identification of the circumferential acoustic mode in the annular cavity is of great significance.

[0085] Suppose that the frequency of the thermal-acoustic oscillation in a certain annular combustion chamber is about 400 Hz, and the order of the dominant circumferential acoustic mode is estimated to be within ±2 orders. Based on the Nyquist sampling theorem, the number of circumferential microphones used according to the traditional discrete Fourier transform (DFT) method needs to satisfy N>2|M|, and taking N=12, 11 orders of circumferential acoustic modes from-5 to +5 can be identified, which meets the demand, and the installation angle is determined as {0°, 30°, 60°, …, 330°}.

[0086] Suppose that one of the microphones fails, and suppose that it is the microphone at the 0° installation position, and the installation position cannot be changed. At this time, the remaining 11 normal working microphones are used for circumferential acoustic mode identification based on combined discrete Fourier transform (cDFT), as shown in Figure 4 The remaining 11 microphones can be divided into 4 groups, with the number of microphones in each group being {6, 3, 2, 2}, respectively, θ1={30°, 90°, 150°, 210°, 270°, 330°}, θ2={60°, 180°, 300°}, θ3={60°, 240°} and θ4={120°, 300°}.

[0087] The combined uniform sampling aliasing matrix T can be constructed according to the grouping scheme and the installation angle 13×11 , and the number of sparse elements is 44.

[0088] First, the time-domain sound pressure signals measured by the microphone array are subjected to fast Fourier transform, and a frequency ω=400Hz is selected, and a frequency-domain sound pressure vector p is constructed lω , , wherein P lk (ω) represents the complex sound pressure of the kth microphone in the lth group at the frequency ω; then the frequency-domain sound pressure vectors p lω obtained by each group are subjected to spatial Fourier transform respectively, and a combined aliasing matrix equation is obtained:

[0089]

[0090] , wherein α=[α -5 …α0…α +5 ] T is a to-be-solved circumferential sound modal spectrum; is a circumferential sound modal spectrum with aliasing.

[0091] The matrix equation is solved by using the sparsity of the matrix and the QR decomposition method based on Givens transformation, and an un-aliased circumferential sound modal spectrum α is output, and the output result is as shown in Figure 5 It can be seen that, compared with the traditional discrete Fourier transform method, the modal identification method based on combined discrete Fourier transform can more flexibly obtain an accurate circumferential sound modal spectrum.

[0092] In summary, the modal identification method based on combined discrete Fourier transform according to the embodiment of the application enhances the sampling flexibility and anti-failure ability, and has a relatively fast calculation speed; the non-uniform sampling mode can be used, and in the case of no noise, an accurate modal coefficient solution can be identified; the calculation speed is comparable to or even faster than the discrete Fourier transform method, and the calculation complexity is O(N 2 ).

[0093] Secondly, the modal identification device based on combined discrete Fourier transform according to the embodiment of the application is described with reference to the accompanying drawings.

[0094] Figure 6 is a block schematic diagram of the modal identification device based on combined discrete Fourier transform according to the embodiment of the application.

[0095] As shown in Figure 6 , the modal identification device 60 based on combined discrete Fourier transform includes a determination module 601, a design module 602, a grouping and combining module 603, a transformation module 604, and a solving module 605.

[0096] The determining module 601 is configured to estimate a maximum dominant modal order in a vibration field of a to-be-identified geometric structure to determine a sampling number. The designing module 602 is configured to design a combined uniform sampling scheme based on a preset sampling limitation condition, acquire a plurality of sampling data according to the combined uniform sampling scheme and the sampling number, and collect a time-domain vibration signal of at least one sampling data. The grouping and combining module 603 is configured to group and combine the plurality of sampling data, and construct a combined uniform sampling aliasing matrix according to the grouped and combined sampling data. The transforming module 604 is configured to perform time and space Fourier transform on the time-domain vibration signal of the at least one sampling data to obtain a modal spectrum with aliasing. The solving module 605 is configured to establish a matrix equation according to the combined uniform sampling aliasing matrix and the modal spectrum with aliasing, and solve an optimal modal spectrum in the vibration field of the to-be-identified geometric structure according to the matrix equation.

[0097] Further, in an embodiment of the present application, the relationship between the maximum dominant modal order and the sampling number is: 2|M|<N, where M is the maximum dominant modal order and N is the sampling number.

[0098] Further, in an embodiment of the present application, the grouping and combining module 603 is specifically configured to:

[0099] divide the N sampling data into L groups, and the number of each group is {1, 2, …, L} and N l is the sampling number of the lth group;

[0100] determine a grouping scheme and a geometric position based on uniform arrangement of each group of sampling data in a modal direction to construct the combined uniform sampling aliasing matrix.

[0101] Further, in an embodiment of the present application, the transforming module 604 is specifically configured to:

[0102] perform fast Fourier transform on the time-domain vibration signal of the at least one sampling data to obtain a frequency-domain signal sequence;

[0103] select a target frequency of interest in the frequency-domain signal sequence to construct a frequency-domain vector;

[0104] perform space Fourier transform on each frequency-domain amplitude vector in the frequency-domain vector to obtain the modal spectrum with aliasing.

[0105] Further, in an embodiment of the present application, the modal spectrum with aliasing is:

[0106]

[0107] wherein, is the lth group of the modal spectrum with aliasing, is a matrix expression of the space Fourier transform, and an element of the matrix expression is:

[0108]

[0109] wherein p = 1, 2, …, N l is a row index of the matrix, q = 1, 2, …, N l is a column index of the matrix, is a rounding up, θ lq is a geometric position of the qth sampling point of the lth group, e is a natural constant, is a base of a natural logarithm function, and i is an imaginary unit.

[0110] Further, in an embodiment of the present application, the matrix equation is:

[0111]

[0112] wherein α = α -|M| …0… +|M| ] T is a to-be-solved modal spectrum, α m is a complex amplitude of the mth modal, is a modal spectrum with aliasing, is a combined uniform sampling aliasing matrix.

[0113] Further, in an embodiment of the present application, the solving module 605 solves the optimal modal spectrum in the vibration field of the to-be-identified geometric structure according to the matrix equation, including:

[0114] establishing an augmented matrix according to the matrix equation;

[0115] performing row exchange processing on the augmented matrix to make the elements on the diagonal not be zero, to obtain an exchanged augmented matrix;

[0116] making the non-zero elements in the lower triangular part of the exchanged augmented matrix be zero elements in sequence through Givens transformation, to obtain an upper triangular matrix;

[0117] back-substituting and normalizing the upper triangular matrix, to make the matrix be a unit matrix with only diagonal elements being 1, to obtain the optimal modal spectrum in the vibration field of the to-be-identified geometric structure.

[0118] It should be noted that the foregoing explanation and description of the embodiment of the modal identification method based on the combined discrete Fourier transform also applies to the embodiment of the modal identification device based on the combined discrete Fourier transform, which will not be repeated here.

[0119] The modal identification device based on the combined discrete Fourier transform according to the embodiment of the application enhances sampling flexibility and failure resistance, and has a relatively fast calculation speed; the non-uniform sampling mode can be used to identify accurate modal coefficient solutions in a noise-free case; the calculation speed is comparable to or even faster than that of the discrete Fourier transform method, and the calculation complexity is O(N 2 )。

[0120] Figure 7 The structural schematic diagram of the electronic device according to the embodiment of the application is provided. The electronic device can include:

[0121] The memory 701, the processor 702 and the computer program stored in the memory 701 and executable on the processor 702.

[0122] The processor 702 implements the modal identification method based on the combined discrete Fourier transform provided in the above embodiment when executing the program.

[0123] Further, the electronic device further includes:

[0124] The communication interface 703 is used for communication between the memory 701 and the processor 702.

[0125] The memory 701 is used for storing the computer program executable on the processor 702.

[0126] The memory 701 can include a high-speed RAM memory, and can also include a non-volatile memory such as at least one disk memory.

[0127] If the memory 701, the processor 702 and the communication interface 703 are independently implemented, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 7 Only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0128] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can complete mutual communication through an internal interface.

[0129] The processor 702 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to perform the embodiments of the present application.

[0130] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to implement the modal identification method based on combined discrete Fourier transform.

[0131] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description 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 N embodiments or examples in a suitable manner. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present application and the features of the different embodiments or examples without contradiction.

[0132] In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0133] Any process or method descriptions in flow charts or described elsewhere herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for implementing the specified logic functions (or steps) and / or can be implemented by one or more hardware or software components, by other physical components, by combinations thereof, and / or by means to be understood by those skilled in the art. The various processes described in the specification can be understood as representing a process or method, any of which can be implemented by a computer program, software, or firmware tangibly embodied in a computer-readable storage medium, and / or by one or more hardware components.

[0134] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, a processor-based system, or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or a combination of the above. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus or device. The computer readable medium can be a computer readable storage medium or a computer readable signal medium. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a propagation medium. The computer readable signal medium can include, but is not limited to, a computer readable medium that facilitates transfer of the program from one place to another. A specific example of a computer readable medium is a non-transitory computer-readable storage medium. A specific example of a computer readable signal medium is a source or destination of the computer readable medium. Another specific example of a computer readable signal medium is a computer readable signal travelling through space. Thus, a computer readable medium can take many forms of hardware to carry out the program for use by or in connection with the instruction execution system, apparatus or device.

[0135] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, the hardware can be implemented with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.

[0136] Those of skill in the art would understand that the steps carried out in the above-mentioned embodiments can be implemented by a program instructing the relevant hardware to complete all or part of the steps, and the program can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiments or a combination thereof.

[0137] In addition, each function unit in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module. When the integrated module is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0138] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A modality recognition method based on combined discrete Fourier transform, characterized in that, Includes the following steps: Estimate the maximum dominant mode order in the vibration field of the geometry to be identified in order to determine the number of samples; A combined uniform sampling scheme is designed based on preset sampling constraints. Multiple sampling data are obtained according to the combined uniform sampling scheme and the sampling quantity, and the time-domain vibration signal of at least one sampling data is collected. The multiple sampled data are grouped and combined, and a combined uniform sampling aliasing matrix is ​​constructed based on the grouped and combined sampled data, specifically including: Will Each sampled data is divided into There are groups, with each group containing [number of groups]. , For the first Number of samples per group; Based on the uniform arrangement of each group of sampled data in the modal direction, the grouping scheme and geometric position are determined to construct the combined uniform sampling aliasing matrix; Performing time-space Fourier transforms on the time-domain vibration signal of the at least one sampled data to obtain an aliased modal spectrum specifically includes: Perform a fast Fourier transform on the time-domain vibration signal of the at least one sampled data to obtain a frequency-domain signal sequence; Select the target frequency of interest from the frequency domain signal sequence to construct a frequency domain vector; Performing a spatial Fourier transform on each frequency domain amplitude vector in the frequency domain vector yields the aliased modal spectrum, wherein the aliased modal spectrum is: in, For the first The group has aliased modal results. Here is the matrix expression for the spatial Fourier transform, and its elements are: in, For the row index of the matrix, For the column index of the matrix, To round up, For the first Group 1 Geometric location of each sampling point is a natural constant, and is the base of the natural logarithm function. The imaginary unit; A matrix equation is established based on the combined uniform sampling aliasing matrix and the aliased modal spectrum, and the optimal modal spectrum in the vibration field of the geometric structure to be identified is solved based on the matrix equation.

2. The modal recognition method based on combined discrete Fourier transform according to claim 1, characterized in that, The relationship between the maximum dominant mode order and the number of samples is as follows: ,in, The maximum dominant mode order, This represents the number of samples.

3. The modal recognition method based on combined discrete Fourier transform according to claim 1, characterized in that, The matrix equation is: in, For the modal spectrum to be determined, for Complex amplitude of the first mode, The modal spectrum is characterized by aliasing. This is for combining uniform sampling aliasing matrices.

4. The modal recognition method based on combined discrete Fourier transform according to claim 3, characterized in that, The step of solving for the optimal modal spectrum in the vibration field of the geometric structure to be identified based on the matrix equation includes: Construct an augmented matrix based on the matrix equation; Perform row swapping on the augmented matrix to make the diagonal elements non-zero, resulting in the swapped augmented matrix; The non-zero elements in the lower triangular part of the augmented matrix after the exchange process are successively transformed into zero elements by Givens transformation to obtain the upper triangular matrix; Substitute the upper triangular matrix back and normalize it to transform it into an identity matrix with only diagonal elements equal to 1, thereby obtaining the optimal modal spectrum within the vibration field of the geometric structure to be identified.

5. A modal recognition device based on combined discrete Fourier transform, characterized in that, include: The determination module is used to estimate the maximum dominant mode order in the vibration field of the geometry to be identified, in order to determine the number of samples; The design module is used to design a combined uniform sampling scheme based on preset sampling constraints, acquire multiple sampling data according to the combined uniform sampling scheme and the sampling quantity, and collect the time-domain vibration signal of at least one sampling data. The grouping and combining module is used to group and combine the multiple sampled data, and construct a combined uniform sampling aliasing matrix based on the grouped and combined sampled data, specifically including: Will Each sampled data is divided into There are groups, with each group containing [number of groups]. , For the first Number of samples per group; Based on the uniform arrangement of each group of sampled data in the modal direction, the grouping scheme and geometric position are determined to construct a combined uniform sampling aliasing matrix; The transformation module is used to perform time-space Fourier transform on the time-domain vibration signal of the at least one sampled data to obtain the aliased modal spectrum, specifically including: Perform a fast Fourier transform on the time-domain vibration signal of at least one sampled data to obtain a frequency-domain signal sequence; Select the target frequency of interest from the frequency domain signal sequence to construct a frequency domain vector; Performing a spatial Fourier transform on each frequency domain amplitude vector in the frequency domain vector yields the aliased modal spectrum, where the aliased modal spectrum is: in, For the first The group has aliased modal results. Here is the matrix expression for the spatial Fourier transform, and its elements are: in, For the row index of the matrix, For the column index of the matrix, To round up, For the first Group 1 The geometric location of each sampling point is a natural constant, and is the base of the natural logarithm function. The imaginary unit; The solution module is used to establish a matrix equation based on the combined uniform sampling aliasing matrix and the aliased modal spectrum, and to solve for the optimal modal spectrum in the vibration field of the geometric structure to be identified based on the matrix equation.

6. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the modality recognition method based on combined discrete Fourier transform as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the modality recognition method based on combined discrete Fourier transform as described in any one of claims 1-4.

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