CN115982625A - Long-term working mode analysis method and detection method based on prior information - Google Patents
Long-term working mode analysis method and detection method based on prior information Download PDFInfo
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Abstract
The application discloses a long-term working modal analysis method and a detection method based on prior information, which comprises the steps of obtaining a compressed vibration response signal under random sampling of a target structure, and taking the modal frequency and the modal damping ratio calculated by the compressed vibration response signal collected last time as the prior information; establishing a modal frequency range based on prior information, and extracting a target modal frequency from the modal frequency range; establishing a modal damping ratio range based on the prior information and the target modal frequency, and extracting a target modal damping ratio from the modal damping ratio range; and determining the modal shape based on the target modal frequency and the target modal damping ratio. According to the method and the device, the modal frequency and the modal damping ratio calculated by the last acquired compression vibration response signal are used as prior information, no additional unit is required to be added in the sensor, and the electric quantity consumption of the wireless sensor power supply is reduced.
Description
Technical Field
The application relates to the technical field of vibration, in particular to a long-term working mode analysis method and a detection method based on prior information.
Background
Modal parameter identification is a way to study the natural vibration characteristics of a structure, wherein the modal parameters include modal frequency, modal damping ratio, and modal mode shape. The modal parameter identification can be applied to the fields of structural state identification, finite element model analysis and correction, vibration control, damage detection and the like.
One of the existing modal analysis methods based on compressed sensing is Prior Sparse Decomposition (PSD), however, the PSD needs to add an additional module in the sensor for calculating a correlation function, basis transformation, and using a peak extraction algorithm, which consumes electric quantity of a wireless sensor power supply and is not favorable for long-term monitoring.
Thus, the prior art has yet to be improved and enhanced.
Disclosure of Invention
The technical problem to be solved by the present application is to provide a long-term working mode analysis method and a detection method based on prior information, aiming at the defects of the prior art.
In order to solve the above technical problem, a first aspect of the embodiments of the present application provides a long-term working mode analysis method based on prior information, where the method includes:
acquiring a compressed vibration response signal under random sampling of a target structure, and taking modal frequency and modal damping ratio calculated by the compressed vibration response signal acquired last time as prior information;
establishing a modal frequency range based on the prior information, and extracting a target modal frequency from the modal frequency range by adopting an orthogonal matching tracking algorithm;
establishing a modal damping ratio range based on the prior information and the target modal frequency, and extracting a target modal damping ratio from the modal damping ratio range by adopting an orthogonal matching tracking algorithm;
and determining the modal shape corresponding to the target structure based on the target modal frequency and the target modal damping ratio.
In one implementation, the calculating the modal frequency and the modal damping ratio of the last acquired compressive vibration response signal as prior information specifically includes:
detecting whether the acquisition time of the compressed vibration response signal is the first acquisition time;
when the acquisition time is not the first acquisition time, using the modal frequency and modal damping ratio calculated by the last acquired compressed vibration response signal as prior information;
and when the acquisition time is the first acquisition time, taking the preset modal parameter as prior information.
In an implementation manner, the acquiring process of the preset modal parameter specifically includes:
acquiring an uncompressed vibration response signal of a target structure through a sensor;
and performing modal analysis on the uncompressed vibration response signal to obtain default modal frequency and default modal damping ratio, and taking the default modal frequency and the default modal damping ratio as preset modal parameters.
In one implementation, the extracting, by using an orthogonal matching pursuit algorithm, a target modal frequency from a modal frequency range specifically includes:
establishing a frequency dictionary based on the modal frequency range and taking a free vibration function as a base;
and solving the rarest solution of the frequency dictionary to obtain the target modal frequency.
In one implementation, the extracting, by using an orthogonal matching pursuit algorithm, a target modal damping ratio from the modal damping ratio range specifically includes:
establishing a damping dictionary based on a free vibration function according to the modal damping ratio range;
and solving the rarest solution of the damping dictionary to obtain a target modal damping ratio.
In one implementation manner, the determining, based on the target modal frequency and the target modal damping ratio, a modal shape corresponding to the target structure specifically includes:
acquiring a target damping dictionary corresponding to the target modal frequency and the target modal damping ratio, and combining all rows in the target damping dictionary to obtain a sparse matrix;
and based on the sparse matrix, performing inversion operation on a sparse model corresponding to the compressed vibration response signal to obtain a modal shape corresponding to the target structure.
A second aspect of the embodiments of the present application provides a method for detecting structural damage, which applies the method for analyzing long-term working mode based on prior information as described above, where the method includes:
acquiring a compression vibration response signal under random sampling of a target structure, and acquiring working modal parameters of the compression vibration response signal through the modal parameters, wherein the working modal parameters comprise a modal vibration type, a modal natural frequency and a modal damping ratio;
and determining a fault detection result of the target structure based on the working modal parameter and the reference modal parameter corresponding to the target structure.
A third aspect of the embodiments of the present application provides a system for obtaining a long-term working mode analysis method based on prior information, where the system includes:
the acquisition module is used for acquiring a compressed vibration response signal under random sampling of a target structure and acquiring prior information corresponding to the compressed vibration response signal, wherein the prior information comprises modal frequency and modal damping ratio;
the first searching module is used for establishing a modal frequency range based on the prior information and extracting a target modal frequency from the modal frequency range by adopting an orthogonal matching tracking algorithm;
the second searching module is used for establishing a modal damping ratio range based on the prior information and the target modal frequency and extracting a target modal damping ratio from the modal damping ratio range by adopting an orthogonal matching pursuit algorithm;
and the determining module is used for determining the modal shape corresponding to the target structure based on the target modal frequency and the target modal damping ratio.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium storing one or more programs, which are executable by one or more processors to implement the steps of the method for analyzing long-term working modalities based on a priori information as described above and/or the steps of the method for detecting structural damage as described above.
A fifth aspect of embodiments of the present application provides a terminal device, including: a processor, a memory, and a communication bus; the memory has stored thereon a computer readable program executable by the processor;
the communication bus realizes connection communication between the processor and the memory;
the processor, when executing the computer readable program, implements the steps of the method for long-term working modal analysis based on a priori information as described above, and/or the steps of the method for detecting structural damage as described above.
Has the beneficial effects that: compared with the prior art, the application provides a long-term working modal analysis method and a detection method based on prior information, which comprises the steps of obtaining a compressed vibration response signal under random sampling of a target structure, and taking the modal frequency and the modal damping ratio calculated by the compressed vibration response signal collected last time as the prior information; establishing a modal frequency range based on prior information, and extracting a target modal frequency from the modal frequency range; establishing a modal damping ratio range based on prior information and target modal frequency, and extracting a target modal damping ratio from the modal damping ratio range; and determining the modal shape based on the target modal frequency and the target modal damping ratio. According to the method and the device, the prior information is determined based on the obtained compression vibration response signal, and no additional unit is required to be added in the sensor, so that the electric quantity consumption of the wireless sensor power supply is reduced. Meanwhile, the method and the device directly extract the non-reconstruction working modal parameters from the compressed signals by using the orthogonal matching pursuit algorithm, can effectively remove the false mode, reduce the calculation time and improve the identification precision and the robustness of the modal parameters.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings may be obtained according to the drawings without any inventive work.
Fig. 1 is a flowchart of a long-term working mode analysis method based on prior information according to the present application.
Fig. 2 is a schematic diagram of a four-degree-of-freedom mass-spring-damping structure.
Fig. 3 is a time domain plot of the pre-compression raw signal of the vibration displacement response signal measured based on a four degree of freedom mass-spring-damping system.
Fig. 4 is a graph of the spectrum of the raw signal before compression of vibration displacement response data measured based on a four degree of freedom mass-spring-damper system.
Fig. 5 is a structural schematic diagram of the system for acquiring the long-term working mode analysis method based on the prior information provided by the present application.
Fig. 6 is a schematic structural diagram of a terminal device provided in the present application.
Detailed Description
The present application provides a long-term working mode analysis method and a detection method based on prior information, and in order to make the purpose, technical scheme, and effect of the present application clearer and clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It has been found that the modal parameter identification is a way to study the natural vibration characteristics of the structure, and one of the main purposes of the structure detection is to identify the modal parameters of the structure, such as the modal frequency, the modal damping ratio, and the modal shape. The modal parameter identification can be applied to the fields of structural state identification, finite element model analysis and correction, vibration control, damage detection and the like.
Compressed Sensing (CS) is a signal processing method, which has the advantages of undersampling, better performance of optimized algorithm, etc. and is widely paid attention by people related to the field of vibration and introduced into the research of modal parameter identification. One of the existing modal analysis methods based on compressed sensing is Prior Sparse Decomposition (PSD), however, the PSD needs to add an additional module to the sensor for calculating a correlation function, basis transformation, and using a peak extraction algorithm, which consumes electric power of a wireless sensor power supply and is not favorable for long-term monitoring.
In order to solve the above problem, in the embodiment of the present application, a compressed vibration response signal under random sampling of a target structure is obtained, and a modal frequency and a modal damping ratio calculated from the compressed vibration response signal collected last time are used as prior information; establishing a modal frequency range based on prior information, and extracting a target modal frequency from the modal frequency range; establishing a modal damping ratio range based on prior information and target modal frequency, and extracting a target modal damping ratio from the modal damping ratio range; and determining the modal shape based on the target modal frequency and the target modal damping ratio. According to the method and the device, the priori information is determined based on the acquired compression vibration response signal, and an additional unit is not required to be additionally arranged in the sensor, so that the electric quantity consumption of the power supply of the wireless sensor is reduced. Meanwhile, the method and the device directly extract the non-reconstruction working modal parameters from the compressed signals by using the orthogonal matching pursuit algorithm, can effectively remove the false mode, reduce the calculation time and improve the identification precision and the robustness of the modal parameters.
The following description of the embodiments is provided to further explain the present disclosure by way of example in connection with the appended drawings.
The present embodiment provides a long-term working modality analysis method based on prior information, as shown in fig. 1, the method includes:
s10, obtaining a compressed vibration response signal under random sampling of a target structure, and taking the modal frequency and modal damping ratio calculated by the compressed vibration response signal collected last time as prior information.
In particular, the target structure is a structure with damping, i.e. the target structure is a structure with damping, e.g. a four degree of freedom mass-spring-damping structure or the like. The compressed vibration response signal is obtained by randomly sampling the target structure through the sensor. In this embodiment, the compressed vibration response signal is a compressed time-domain vibration response signal.
The signal decomposition of the compressive vibration response signal based on the free vibration function may be:
in structural dynamics, for a linear time-invariant system of n degrees of freedom, the motion control equation is written as:
wherein M ∈ R n×n Represents a quality matrix, C ∈ R n×n Represents the damping matrix, K ∈ R n×n Representing a stiffness matrix, F (t) representing a random excitation, X being an n-dimensional displacement response matrix. Vibration displacement response X (t) = [ X = 1 (t),…,X n (t)] T Can be decomposed into:
wherein Ψ = [ ψ = 1 ,…,ψ n ]Representing a mode shape matrix, Γ representing a matrix having an element A j S = [ S ] (diagonal coefficient matrix of (c)) 1 (t),…,s N (t)]Representing a matrix of modal coordinates, ω n,j Representing undamped natural frequencies, ω, of the j-th order mode d,j Damped natural frequency, ξ, representing the j-th order mode j Mode damping ratio, θ, representing the j-th order mode j Representing the phase of the j-th order mode. X (t) can be expanded as:
wherein, A j ′＝A j cos(θ j )、A j ″＝A j sin(θ j )。
Based on this, the compressive vibration response signal Y can be decomposed into:
wherein Γ = [ Γ' Γ ″ ], and the like]Γ' and Γ "are both N × N diagonal matrices; s comprises an element pair
And &>
Φ is the compression matrix (M × L).
Further, the prior information includes a modal frequency and a modal damping ratio, the prior information is the modal frequency and the modal damping ratio calculated by the compression vibration response signal acquired last time, when the modal parameter is acquired first time, the preset modal parameter can be used as the prior information, and when the modal parameter is not acquired first time, the modal frequency and the modal damping ratio calculated by the compression vibration response signal acquired last time can be directly used as the prior information. Of course, in practical applications, the mean value of the modality parameters acquired in the previous two times may be used as the prior information, or the modality parameters obtained at one time interval may be used as the prior information.
In one implementation, the calculating the modal frequency and the modal damping ratio of the last acquired compressive vibration response signal as prior information specifically includes:
detecting whether the acquisition time of the compressed vibration response signal is the first acquisition time;
when the acquisition time is not the first acquisition time, using the modal frequency and modal damping ratio calculated by the last acquired compressed vibration response signal as prior information;
and when the acquisition time is the first acquisition time, taking the preset modal parameter as prior information.
Specifically, the first obtaining time refers to a time when the compression vibration response signal of the target structure is obtained for the first time, that is, before the modal parameter is obtained by using the method provided by this embodiment, the modal parameter is not obtained by using the method provided by this embodiment. When the acquisition time is not the first acquisition time, the modal frequency and the modal damping ratio of the target structure are obtained through calculation, so that the modal frequency and the modal damping ratio corresponding to the previous acquisition time of the acquisition time can be selected from the modal frequency and the modal damping ratio of the target structure obtained through calculation as prior information. That is, the prior information includes the modal frequencies and modal damping ratios at the acquisition time adjacent to and before the acquisition time corresponding to the current compressive vibration response signal.
Further, when the acquisition time is the first acquisition time, it is indicated that the modal frequency and the modal damping ratio of the target structure are not obtained based on the calculation of the compressed vibration response signal, and at this time, a preset modal parameter may be used as prior information, where the preset modal parameter may be a preset default modal parameter or may be determined based on the uncompressed compressed vibration response signal.
In one implementation manner, the preset modal parameter is determined based on an uncompressed compressed vibration response signal, and accordingly, the acquiring process of the preset modal parameter specifically includes:
acquiring an uncompressed vibration response signal of a target structure through a sensor;
and performing modal analysis on the uncompressed vibration response signal to obtain default modal frequency and default modal damping ratio, and taking the default modal frequency and the default modal damping ratio as preset modal parameters.
Specifically, the uncompressed vibration response signal is collected by a sensor and used for determining a preset modal parameter, wherein a modal analysis method, such as COVariance-driven random Subspace Identification (SSI-COV) and the like, may be preset by performing modal analysis on the uncompressed vibration response signal, and a default modal frequency and a default modal damping ratio of the uncompressed vibration response signal are calculated by using the preset modal analysis method, and the calculated default modal frequency and default modal damping ratio are used as the preset modal parameter.
For the first-obtained compression vibration response signal, default modal frequency and initial modal damping determined based on the uncompressed vibration response signal are used as prior information, for the non-first-obtained compression vibration response signal, the previous-obtained modal frequency and modal damping are used as prior information, and therefore the prior information can be obtained without arranging an additional module on the sensor, so that the electric energy consumption of the sensor can be reduced, the working time of the sensor can be increased, and the target structure can be monitored for a long time by the method provided by the embodiment.
S20, establishing a modal frequency range based on the prior information, and extracting the target modal frequency from the modal frequency range by adopting an orthogonal matching tracking algorithm.
Specifically, after the prior information is acquired, a modal frequency range is established based on modal frequencies in the prior information, where the modal frequency range may be ± 2.5% of a maximum modal frequency in the prior information, and when search ranges of two adjacent modal frequencies overlap, an adjacent boundary value takes an average value of the two modal frequencies to avoid the overlap. The modal frequencies include modal frequencies of a plurality of orders of modes, the maximum modal frequency is the maximum value of the modal frequencies of the plurality of orders of modes, and two adjacent modal frequencies refer to the modal frequencies of two adjacent orders of modes.
Orthogonal Matching Pursuit (OMP) is a CS algorithm for sparse representation of signals that determines modal frequencies and modal damping ratios by designing a dictionary of modal frequencies and modal damping ratios. The orthogonal matching pursuit algorithm searches modal parameters without signal reconstruction, and then CS-based non-reconstruction working modal parameter identification is realized. Based on this, the extracting the target modal frequency from the modal frequency range by using the orthogonal matching pursuit algorithm specifically includes:
s21, establishing a frequency dictionary based on the modal frequency range and taking a free vibration function as a base;
and S22, solving the rarest solution of the frequency dictionary to obtain the target modal frequency.
Specifically, the frequency dictionary includes a modal coordinate matrix S = [ S ] 1 (t),…,s N (t)]Wherein, the frequency dictionary D can be represented as:
wherein, the elements of the sub-dictionaries D 'and D' are respectively:
wherein, time t = { t = { t } 1 ,t 2 ,…,t M }，ω d,l Representing the damped natural frequency of order i, = {1, …, p }.
Correspondingly, the compressive vibration response signal may be sparsely represented as:
where γ is used to include Ψ Γ. According to the CS theory, if γ and D satisfy the constraint isometry condition, and γ is sparse in the D domain, the sparse coefficient matrix γ may be obtained through an optimization algorithm, and then the modal frequency is estimated according to the location of the non-zero coefficient. Therefore, the target modal frequency can be obtained by solving the rarest solution of the frequency dictionary, wherein the optimization target equation of the modal frequency is as follows:
s30, establishing a modal damping ratio range based on the prior information and the target modal frequency, and extracting a target modal damping ratio from the modal damping ratio range by adopting an orthogonal matching pursuit algorithm.
Specifically, when the modal damping ratio range is determined, according to the modal damping ratio and the target modal frequency in the prior information, the process of establishing the modal damping ratio range based on the modal damping ratio and the target modal frequency may adopt an existing determination process of the modal damping ratio range, which is not specifically described herein. After the modal damping ratio range is obtained, extracting a target modal damping ratio from the modal damping ratio range by adopting an orthogonal matching pursuit algorithm.
In one implementation, the extracting the target modal damping ratio from the modal damping ratio range specifically includes:
establishing a damping dictionary based on a free vibration function according to the modal damping ratio range;
and solving the rarest solution of the damping dictionary to obtain a target modal damping ratio.
Specifically, the process of establishing the damping dictionary and the process of solving the rarest solution of the damping dictionary by using the orthogonal matching pursuit algorithm are the same as the process of determining the modal frequency, which is not repeated herein, and the process of determining the modal frequency may be referred to specifically.
And S40, determining a modal shape corresponding to the target structure based on the target modal frequency and the target modal damping ratio.
Specifically, after target modal frequency and a target modal damping ratio are obtained, a damping dictionary corresponding to the target modal frequency and the target modal damping ratio is determined, and then a modal shape is determined based on the damping dictionary corresponding to the target modal frequency and the target modal damping ratio, wherein the modal shape is obtained by performing inversion operation on a sparse model corresponding to the compression vibration response signal based on a sparse matrix formed by the damping dictionary corresponding to the target modal damping ratio. Correspondingly, the determining the mode shape corresponding to the target structure based on the target modal damping ratio specifically includes:
acquiring a target damping dictionary corresponding to the target modal frequency and the target modal damping ratio, and combining all rows in the target damping dictionary to obtain a sparse matrix;
and based on the sparse matrix, performing inversion operation on a sparse model corresponding to the compressed vibration response signal to obtain a modal shape corresponding to the target structure.
Specifically, the target damping dictionary is a damping dictionary corresponding to the target modal frequency and the target modal damping ratio, after the target damping dictionary is obtained, elements in fixed rows in the target damping dictionary are added to obtain a sparse matrix with one row and multiple columns, and then the modal shape is obtained through inversion operation, wherein the modal shape is
Further, in order to evaluate the accuracy of the long-term working modal analysis method based on prior information provided by the present embodiment, the present embodiment further provides a modal parameter accuracy evaluation method, wherein a modal confidence criterion Method (MAC) is used to evaluate the identification accuracy of the modal shape;
wherein the content of the first and second substances,
for the identified j-th order mode shape, { psi j The j order theoretical mode shape is adopted, the MAC range is between 0 and 1, and the more the MAC value approaches to 1, the higher the identified mode shape precision is;
accuracy of modal frequency using relative error
Evaluation recognition, wherein the relative error>
The expression of (c) may be:
wherein, omega' j Representing the theoretical j-th natural frequency, ω k Representing the identified j-th order natural frequency,
the closer to 0 the higher the accuracy of the identified natural frequency.
Relative error is adopted as the accuracy of modal damping ratio
Evaluation recognition, wherein the relative error>
May be:
wherein, ξ' j Representing the theoretical j-th natural frequency, ξ k Representing the identified j-th order natural frequency,
the closer to 0, the higher the accuracy of the identified natural frequency.
In summary, the present embodiment provides a long-term working mode analysis method based on prior information, including obtaining a compressed vibration response signal under random sampling of a target structure, and using a modal frequency and a modal damping ratio calculated from the compressed vibration response signal collected last time as prior information; establishing a modal frequency range based on prior information, and extracting a target modal frequency from the modal frequency range; establishing a modal damping ratio range based on the prior information and the target modal frequency, and extracting a target modal damping ratio from the modal damping ratio range; and determining the modal shape based on the target modal frequency and the target modal damping ratio. According to the method and the device, the prior information is determined based on the obtained compression vibration response signal, and no additional unit is required to be added in the sensor, so that the electric quantity consumption of the wireless sensor power supply is reduced. Meanwhile, the method and the device directly extract the non-reconstruction working modal parameters from the compressed signals by using the orthogonal matching pursuit algorithm, can effectively remove false modes, reduce the calculation time and improve the identification precision and the robustness of the modal parameters.
To further illustrate that the present embodiment provides a long-term operation modal analysis method (SDPI) based on a priori information, as shown in FIGS. 2-4, a four-DOF mass-spring-damper system is used as a target structure, where c 1 Representing mass m 1 Damping from the fixed end, c 12 Representing mass m 1 And m 2 Damping between, k 1 Representing mass m 1 Rigidity to the fixed end, k 12 Representing a mass m 1 And m 2 Rigidity of (f) between 1 Representing mass m 1 Force applied, x 1 Representing mass m 1 Is M = diag ([ 1 1 1 1 1 1) for the mass matrix of the target structure]) The stiffness matrix is:
the damping matrix is C =0.1M + beta K, beta =0 and 0.0001 are considered, excitation F is Gaussian white noise with zero mean and unit variance, simulation is carried out on the basis of numerical software, vibration response data with the sampling frequency of 20Hz are sampled for 5000 times, the compression ratio is 5, 10 and 15, compressed random signals are used for verifying the performance of the SDPI method, the frequency range of the SDPI method is +/-2.5% of the maximum prior modal frequency, and the final search interval is 0.005Hz. The modal parameters identified by the SDPI method are shown in table 1, and the results in table 1 show that the SDPI method has better identification accuracy. In addition, modal damping ratio identification for random vibrations requires additional pre-processing, and the pre-processing does not relate to this patent, so the damping ratio results are not shown, but the SDPI method can successfully identify modal damping ratios under free vibrations.
TABLE 1 Modal parameters identified by SDPI method
Based on the method for analyzing long-term working mode based on prior information, the present embodiment provides a method for detecting structural damage, which applies the method for analyzing long-term working mode based on prior information described in the above embodiment, and the method includes:
acquiring a compression vibration response signal under random sampling of a target structure, and acquiring working modal parameters of the compression vibration response signal through the modal parameters, wherein the working modal parameters comprise a modal vibration type, a modal natural frequency and a modal damping ratio;
and determining a fault detection result of the target structure based on the working modal parameters and the reference modal parameters corresponding to the target structure.
Specifically, the fault detection result includes whether to send the damage, where the reference modal parameter corresponding to the target structure may be a modal parameter when the target structure is not in fault. That is, the acquired operation modal parameters are compared with the modal parameters when no failure occurs, and when the difference between the two parameters reaches a difference threshold (for example, 3%, etc.), it is determined that the target structure is damaged. In addition, when the target structure is damaged, the damage position of the target structure can be determined according to the acquired mode shape.
Based on the foregoing long-term working mode analysis method based on prior information, this embodiment provides a long-term working mode analysis method acquisition system based on prior information, and as shown in fig. 5, the system includes:
the acquisition module 100 is configured to acquire a compressed vibration response signal under random sampling of a target structure, and use a modal frequency and a modal damping ratio calculated from the compressed vibration response signal acquired last time as prior information;
a first search module 200, configured to establish a modal frequency range based on the prior information, and extract a target modal frequency from the modal frequency range by using an orthogonal matching pursuit algorithm;
a second searching module 300, configured to establish a modal damping ratio range based on the priori information and the target modal frequency, and extract a target modal damping ratio from the modal damping ratio range by using an orthogonal matching pursuit algorithm;
a determining module 400, configured to determine a modal shape corresponding to the target structure based on the target modal frequency and the target modal damping ratio.
Based on the method for analyzing long-term working modality based on prior information, the present embodiment provides a computer-readable storage medium, where one or more programs are stored, and the one or more programs are executable by one or more processors to implement the steps in the method for analyzing long-term working modality based on prior information according to the foregoing embodiment.
Based on the foregoing long-term working mode analysis method based on prior information, the present application further provides a terminal device, as shown in fig. 6, including at least one processor (processor) 20; a display screen 21; and a memory (memory) 22, which may also include a communication interface 23 and a bus 24. The processor 20, the display 21, the memory 22 and the communication interface 23 can communicate with each other through the bus 24. The display screen 21 is configured to display a user guidance interface preset in the initial setting mode. The communication interface 23 may transmit information. The processor 20 may call logic instructions in the memory 22 to perform the methods in the embodiments described above.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present application.
Claims (10)
1. A long-term working mode analysis method based on prior information is characterized by comprising the following steps:
acquiring a compressed vibration response signal under random sampling of a target structure, and taking modal frequency and modal damping ratio calculated by the compressed vibration response signal acquired last time as prior information;
establishing a modal frequency range based on the prior information, and extracting a target modal frequency from the modal frequency range by adopting an orthogonal matching tracking algorithm;
establishing a modal damping ratio range based on the prior information and the target modal frequency, and extracting a target modal damping ratio from the modal damping ratio range by adopting an orthogonal matching tracking algorithm;
and determining the mode shape corresponding to the target structure based on the target modal frequency and the target modal damping ratio.
2. The method for analyzing long-term working mode based on prior information according to claim 1, wherein the calculating the modal frequency and modal damping ratio of the last acquired compressed vibration response signal as the prior information specifically comprises:
detecting whether the acquisition time of the compression vibration response signal is the first acquisition time or not;
when the acquisition time is not the first acquisition time, the modal frequency and modal damping ratio calculated by the last acquired compressed vibration response signal are used as prior information;
and when the acquisition time is the first acquisition time, taking the preset modal parameter as prior information.
3. The method for analyzing long-term working modality based on a priori information of claim 2, wherein the obtaining process of the preset modality parameters specifically comprises:
acquiring an uncompressed vibration response signal of a target structure through a sensor;
and performing modal analysis on the uncompressed vibration response signal to obtain default modal frequency and default modal damping ratio, and taking the default modal frequency and the default modal damping ratio as preset modal parameters.
4. The method for analyzing long-term working modal analysis based on prior information according to claim 1, wherein the extracting the target modal frequency from the modal frequency range by using the orthogonal matching pursuit algorithm specifically comprises:
establishing a frequency dictionary based on the modal frequency range and taking a free vibration function as a base;
and solving the rarest solution of the frequency dictionary to obtain the target modal frequency.
5. The method for analyzing long-term working mode based on prior information according to claim 1, wherein the extracting a target modal damping ratio from the modal damping ratio range by using an orthogonal matching pursuit algorithm specifically comprises:
establishing a damping dictionary based on a free vibration function according to the modal damping ratio range;
and solving the rarest solution of the damping dictionary to obtain a target modal damping ratio.
6. The method for analyzing long-term working mode based on prior information according to claim 1, wherein the determining the mode shape corresponding to the target structure based on the target modal frequency and the target modal damping ratio specifically comprises:
acquiring a target damping dictionary corresponding to the target modal frequency and the target modal damping ratio, and combining all rows in the target damping dictionary to obtain a sparse matrix;
and on the basis of the sparse matrix, performing inversion operation on a sparse model corresponding to the compressed vibration response signal to obtain a modal shape corresponding to the target structure.
7. A method for detecting structural damage, which is characterized by applying the method for analyzing long-term working mode based on prior information according to any one of claims 1 to 6, wherein the method comprises the following steps:
acquiring a compression vibration response signal under random sampling of a target structure, and acquiring working modal parameters of the compression vibration response signal through the modal parameters, wherein the working modal parameters comprise a modal shape, a modal natural frequency and a modal damping ratio;
and determining a fault detection result of the target structure based on the working modal parameter and the reference modal parameter corresponding to the target structure.
8. A long-term working mode analysis method acquisition system based on prior information is characterized by comprising the following steps:
the acquisition module is used for acquiring a compression vibration response signal under random sampling of a target structure and taking the modal frequency and modal damping ratio calculated by the compression vibration response signal acquired last time as prior information;
the first searching module is used for establishing a modal frequency range based on the prior information and extracting a target modal frequency from the modal frequency range by adopting an orthogonal matching tracking algorithm;
the second searching module is used for establishing a modal damping ratio range based on the prior information and the target modal frequency and extracting a target modal damping ratio from the modal damping ratio range by adopting an orthogonal matching pursuit algorithm;
and the determining module is used for determining the modal shape corresponding to the target structure based on the target modal frequency and the target modal damping ratio.
9. A computer readable storage medium, storing one or more programs, which are executable by one or more processors to perform the steps of the method for long-term-operation modal analysis based on a priori information according to any one of claims 1 to 6 and/or the method for structural damage detection according to claim 7.
10. A terminal device, comprising: a processor, a memory, and a communication bus; the memory has stored thereon a computer readable program executable by the processor;
the communication bus realizes connection communication between the processor and the memory;
the processor, when executing the computer readable program, implements the steps of the method for long-term working modal analysis based on prior information according to any one of claims 1 to 6, and/or the steps of the method for detecting structural damage according to claim 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1838109A (en) * | 2006-04-10 | 2006-09-27 | 西安交通大学 | Mode parameter recognition method based on experience mode decomposition and Laplace wavelet |
| KR101529690B1 (en) * | 2015-04-14 | 2015-06-16 | 주식회사 이제이텍 | System for monitoring building shake using time domain decomposition |
| US20200033226A1 (en) * | 2018-03-12 | 2020-01-30 | Dalian University Of Technology | An automatic method for tracking structural modal parameters |
| US20200089733A1 (en) * | 2018-06-04 | 2020-03-19 | Dalian University Of Technology | Sensor placement method for reducing uncertainty of structural modal identification |
| CN111652154A (en) * | 2020-06-04 | 2020-09-11 | 河北工业大学 | Underdetermined system mode identification method based on automatic frequency band segmentation |
| CN112329274A (en) * | 2020-12-29 | 2021-02-05 | 星河动力(北京)空间科技有限公司 | Rocket modal parameter determination method, device, equipment and storage medium |
| CN112329855A (en) * | 2020-11-05 | 2021-02-05 | 华侨大学 | Underdetermined working modal parameter identification method and detection method based on adaptive dictionary |
| CN112507606A (en) * | 2020-11-05 | 2021-03-16 | 华侨大学 | Method for identifying underdetermined working modal parameters based on RBF network and detection method |
| CN112834243A (en) * | 2021-01-18 | 2021-05-25 | 中国铁道科学研究院集团有限公司 | Abnormal vibration monitoring method and device for vehicle body |
| WO2021158198A2 (en) * | 2020-02-07 | 2021-08-12 | Orta Doğu Tekni̇k Üni̇versi̇tesi̇ | A novel experimental modal analysis method based on response control approach for nonlinear engineering structures |
| CN114565003A (en) * | 2021-11-11 | 2022-05-31 | 哈尔滨工业大学（深圳） | Underdetermined working mode analysis method based on compression sampling and dictionary sparse decomposition |
| CN114912547A (en) * | 2022-06-17 | 2022-08-16 | 华侨大学 | Underdetermined working mode parameter identification method and system of linear time-varying structure |
| CN115169396A (en) * | 2022-06-30 | 2022-10-11 | 西安交通大学 | Bearing weak fault feature extraction method based on parameter dictionary and OMP algorithm |
| CN115204016A (en) * | 2022-07-18 | 2022-10-18 | 北京航空航天大学 | Verification method for measurement result of small-size cylinder space vibration mode |
-
2023
- 2023-01-06 CN CN202310019314.5A patent/CN115982625B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1838109A (en) * | 2006-04-10 | 2006-09-27 | 西安交通大学 | Mode parameter recognition method based on experience mode decomposition and Laplace wavelet |
| KR101529690B1 (en) * | 2015-04-14 | 2015-06-16 | 주식회사 이제이텍 | System for monitoring building shake using time domain decomposition |
| US20200033226A1 (en) * | 2018-03-12 | 2020-01-30 | Dalian University Of Technology | An automatic method for tracking structural modal parameters |
| US20200089733A1 (en) * | 2018-06-04 | 2020-03-19 | Dalian University Of Technology | Sensor placement method for reducing uncertainty of structural modal identification |
| WO2021158198A2 (en) * | 2020-02-07 | 2021-08-12 | Orta Doğu Tekni̇k Üni̇versi̇tesi̇ | A novel experimental modal analysis method based on response control approach for nonlinear engineering structures |
| CN111652154A (en) * | 2020-06-04 | 2020-09-11 | 河北工业大学 | Underdetermined system mode identification method based on automatic frequency band segmentation |
| CN112329855A (en) * | 2020-11-05 | 2021-02-05 | 华侨大学 | Underdetermined working modal parameter identification method and detection method based on adaptive dictionary |
| CN112507606A (en) * | 2020-11-05 | 2021-03-16 | 华侨大学 | Method for identifying underdetermined working modal parameters based on RBF network and detection method |
| CN112329274A (en) * | 2020-12-29 | 2021-02-05 | 星河动力(北京)空间科技有限公司 | Rocket modal parameter determination method, device, equipment and storage medium |
| CN112834243A (en) * | 2021-01-18 | 2021-05-25 | 中国铁道科学研究院集团有限公司 | Abnormal vibration monitoring method and device for vehicle body |
| CN114565003A (en) * | 2021-11-11 | 2022-05-31 | 哈尔滨工业大学（深圳） | Underdetermined working mode analysis method based on compression sampling and dictionary sparse decomposition |
| CN114912547A (en) * | 2022-06-17 | 2022-08-16 | 华侨大学 | Underdetermined working mode parameter identification method and system of linear time-varying structure |
| CN115169396A (en) * | 2022-06-30 | 2022-10-11 | 西安交通大学 | Bearing weak fault feature extraction method based on parameter dictionary and OMP algorithm |
| CN115204016A (en) * | 2022-07-18 | 2022-10-18 | 北京航空航天大学 | Verification method for measurement result of small-size cylinder space vibration mode |
Non-Patent Citations (6)
| Title |
|---|
| BOHAO XU等: "A Novel Regularized Adaptive Matching Pursuit for Moving Force Identification Using Multiple Criteria and Prior Knowledge", INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS, pages 1 - 10 * |
| XIN ZHANG等: "Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time–frequency atom dictionary", MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 107, pages 29 - 42, XP085351171, DOI: 10.1016/j.ymssp.2018.01.027 * |
| YONGJIAN LI等: "A secondary selection-based orthogonal matching pursuit method for rolling element bearing diagnosis", MEASUREMENT, vol. 176, pages 1 - 11 * |
| 康杰: "基于压缩感知的结构振动模态参数识别方法研究", 中国博士学位论文全文数据库 工程科技Ⅱ辑, pages 028 - 4 * |
| 李玉刚;叶庆卫;周宇;王晓东;: "基于模态参数识别的ITD算法改进", 计算机工程, no. 04, pages 304 - 309 * |
| 王海明;刘永强;廖英英;: "基于快速谱峭度和正交匹配追踪算法的轴承故障诊断方法", 振动与冲击, no. 19, pages 85 - 90 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116680662A (en) * | 2023-04-19 | 2023-09-01 | 哈尔滨工业大学（深圳） | Automatic working mode analysis method based on compressed sensing |
| CN116680662B (en) * | 2023-04-19 | 2024-03-08 | 哈尔滨工业大学（深圳） | Automatic working mode analysis method based on compressed sensing |
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