CN115982625B - Priori information-based long-term working mode analysis method and detection method - Google Patents
Priori information-based long-term working mode analysis method and detection method Download PDFInfo
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Abstract
The application discloses a long-term working mode analysis method and a detection method based on priori information, comprising the steps of obtaining a compression vibration response signal under random sampling of a target structure, and taking the mode frequency and the mode damping ratio calculated by the compression vibration response signal collected last time as the priori information; establishing a modal frequency range based on prior information, and extracting 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; the mode shape is determined based on the target mode frequency and the target mode damping ratio. According to the application, the modal frequency and the modal damping ratio calculated by the last acquired compression vibration response signal are used as priori information, no additional unit is 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 priori information.
Background
Modal parameter identification is a way to study the natural vibration characteristics of a structure, where modal parameters include modal frequencies, modal damping ratios, and 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.
One of the existing modal analysis methods based on compressed sensing is priori sparse decomposition (Prior sparse decomposition, PSD), however, the PSD needs to add an additional module to the sensor for calculating a correlation function, performing base transformation and using a peak extraction algorithm, which consumes the electric quantity of the power supply of the wireless sensor and is not beneficial to long-term monitoring.
There is thus a need for improvements and improvements in the art.
Disclosure of Invention
The application aims to solve the technical problem of providing a long-term working mode analysis method and a detection method based on priori information aiming at the defects of the prior art.
In order to solve the above technical problems, a first aspect of the present application provides a long-term working mode analysis method based on prior information, where the method includes:
acquiring a compression vibration response signal under random sampling of a target structure, and taking the modal frequency and the modal damping ratio calculated by the compression vibration response signal acquired last time as prior information;
establishing a modal frequency range based on the prior information, and extracting target modal frequency from the modal frequency range by adopting an orthogonal matching pursuit 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 pursuit algorithm;
and determining the mode shape corresponding to the target structure based on the target mode frequency and the target mode damping ratio.
In one implementation manner, the mode frequency and the mode damping ratio calculated by the last acquired compression vibration response signal are specifically included as prior information:
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 the modal damping ratio calculated by the compression vibration response signal acquired last time are used as priori information;
when the acquisition time is the first acquisition time, taking the preset modal parameter as prior information.
In one implementation manner, the acquiring process of the preset modal parameter specifically includes:
acquiring an uncompressed vibration response signal of the target structure through a sensor;
and carrying out 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 the target modal frequency from the modal frequency range by using the orthogonal matching pursuit algorithm specifically includes:
establishing a frequency dictionary based on the modal frequency range free vibration function;
and solving the most sparse solution of the frequency dictionary to obtain the target modal frequency.
In one implementation, the extracting the target modal damping ratio from the range of modal damping ratios using an orthogonal matching pursuit algorithm specifically includes:
establishing a damping dictionary based on a free vibration function according to the modal damping ratio range;
and solving the most sparse solution of the damping dictionary to obtain a target modal damping ratio.
In one implementation, 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 merging each row in the target damping dictionary to obtain a sparse matrix;
and based on the sparse matrix, carrying out inversion operation on a sparse model corresponding to the compression vibration response signal to obtain a mode shape corresponding to the target structure.
The second aspect of the embodiment of the application provides a method for detecting structural damage, which applies the long-term working mode analysis method based on priori information, and the method comprises the following steps:
acquiring a compression vibration response signal under random sampling of a target structure, and acquiring working mode parameters of the compression vibration response signal through the mode parameters, wherein the working mode parameters comprise a mode shape, a mode natural frequency and a mode damping ratio;
and determining a fault detection result of the target structure based on the reference modal parameter corresponding to the target structure and the working modal parameter.
A third aspect of the embodiment of the present application provides a long-term working mode analysis method acquisition system based on prior information, where the system includes:
the acquisition module is used for acquiring a compression vibration response signal under random sampling of a target structure and acquiring prior information corresponding to the compression vibration response signal, wherein the prior information comprises modal frequency and modal damping ratio;
the first search module is used for establishing a modal frequency range based on the prior information and extracting target modal frequencies from the modal frequency range by adopting an orthogonal matching pursuit algorithm;
the second search 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 mode shape corresponding to the target structure based on the target mode frequency and the target mode damping ratio.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium storing one or more programs executable by one or more processors to implement steps in a long-term operating mode analysis method based on a priori information as described in any of the above, and/or steps in a method for detecting structural damage as described above.
A fifth aspect of an embodiment 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 steps in a long-term working mode analysis method based on a priori information as described in any of the above, and/or steps in a detection method of structural damage as described above.
The beneficial effects are that: compared with the prior art, the application provides a long-term working mode analysis method and a detection method based on priori information, which comprise the steps of acquiring a compression vibration response signal under random sampling of a target structure, and taking the mode frequency and the mode damping ratio calculated by the compression vibration response signal acquired last time as the priori information; establishing a modal frequency range based on prior information, and extracting 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; the mode shape is determined based on the target mode frequency and the target mode damping ratio. The prior information is determined based on the obtained compression vibration response signal, and an additional unit is not needed to be additionally arranged in the sensor, so that the electric quantity consumption of a wireless sensor power supply is reduced. Meanwhile, the application directly extracts the non-reconstruction working mode parameters from the compressed signals by utilizing the orthogonal matching pursuit algorithm, so that false modes can be effectively removed, the calculation time is reduced, and the recognition accuracy and the robustness of the mode parameters are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without creative effort for a person of ordinary skill in the art.
Fig. 1 is a flowchart of a long-term working mode analysis method based on priori information.
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 original signal before compression of the vibration displacement response signal based on a four degree of freedom mass-spring-damper system measurement.
Fig. 4 is a graph of a spectrum of the original signal before compression based on vibration displacement response data measured by a four degree of freedom mass-spring-damper system.
Fig. 5 is a schematic structural diagram of a long-term working mode analysis method acquisition system based on priori information.
Fig. 6 is a schematic structural diagram of a terminal device provided by the present application.
Detailed Description
The application provides a long-term working mode analysis method and a detection method based on priori information, which are used for making the purposes, the technical scheme and the effects of the application clearer and more definite. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Through researches, the identification of modal parameters is one way to research the inherent vibration characteristics of a structure, and one of the main purposes of the structure detection is to identify the modal parameters of the structure, such as modal frequency, modal damping ratio and modal shape. The modal parameter identification can be applied to the fields of structure state identification, finite element model analysis and correction, vibration control, damage detection and the like.
Compressed sensing (Compressive sensing, CS) is a signal processing method, which is widely focused by related personnel in the vibration field and introduced into research of modal parameter identification with the advantages of undersampling, better performance of an optimization algorithm and the like. One of the existing modal analysis methods based on compressed sensing is priori sparse decomposition (Prior sparse decomposition, PSD), however, the PSD needs to add an additional module to the sensor for calculating a correlation function, performing base transformation and using a peak extraction algorithm, which consumes the electric quantity of the power supply of the wireless sensor and is not beneficial to long-term monitoring.
In order to solve the above problems, in the embodiment of the present application, the method includes acquiring a compression vibration response signal under random sampling of a target structure, and taking a modal frequency and a modal damping ratio calculated by a compression vibration response signal acquired last time as prior information; establishing a modal frequency range based on prior information, and extracting 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; the mode shape is determined based on the target mode frequency and the target mode damping ratio. The prior information is determined based on the obtained compression vibration response signal, and an additional unit is not needed to be additionally arranged in the sensor, so that the electric quantity consumption of a wireless sensor power supply is reduced. Meanwhile, the application directly extracts the non-reconstruction working mode parameters from the compressed signals by utilizing the orthogonal matching pursuit algorithm, so that false modes can be effectively removed, the calculation time is reduced, and the recognition accuracy and the robustness of the mode parameters are improved.
The application will be further described by the description of embodiments with reference to the accompanying drawings.
The embodiment provides a long-term working mode analysis method based on priori information, as shown in fig. 1, the method comprises the following steps:
s10, acquiring a compression vibration response signal of a target structure under random sampling, and taking the modal frequency and the modal damping ratio calculated by the compression vibration response signal acquired last time as prior information.
Specifically, the target structure is a structure with damping, i.e. the target structure is a damped structure, e.g. a four-degree-of-freedom mass-spring-damping structure or the like. The compressive vibration response signal is obtained by randomly sampling the target structure by the sensor. In this embodiment, the compression vibration response signal is a compression time domain vibration response signal.
The signal decomposition of the compression 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 is E R n×n Representing a quality matrix, C.epsilon.R n×n Represents a damping matrix, K.epsilon.R n×n Representing a stiffness matrix, F (t) representing a random excitation, and 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 ]Represents a mode shape matrix, Γ represents a matrix having the element A j Is = [ S ] 1 (t),…,s N (t)]Representing a modal coordinate matrix, ω n,j Represent undamped natural frequency, ω, of the jth order mode d,j Represents the damped natural frequency, ζ, of the jth 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 is j ′＝A j cos(θ j )、A j ″＝A j sin(θ j )。
Based on this, the compression vibration response signal Y can be decomposed into:
wherein Γ= [ Γ' Γ ""]Both Γ' and Γ "are diagonal matrices of N x N; s comprises element pairsAnd->Φ is a compression matrix (mxl).
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 for the first time, the preset modal parameter can be used as the prior information, and when the modal parameter is acquired for the second 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 application, the average value of the modal parameters acquired in the previous two times may be used as prior information, or the modal parameters at intervals may be used as prior information.
In one implementation manner, the mode frequency and the mode damping ratio calculated by the last acquired compression vibration response signal are specifically included as prior information:
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 the modal damping ratio calculated by the compression vibration response signal acquired last time are used as priori information;
when the acquisition time is the first acquisition time, taking the preset modal parameter as prior information.
Specifically, the first acquisition time refers to a time when the compression vibration response signal of the target structure is acquired for the first time, that is, before the mode parameter is acquired by the method provided in this embodiment, the mode parameter is not acquired by the method provided in this embodiment. When the acquisition time is not the first acquisition time, the mode frequency and the mode damping ratio of the target structure are calculated, so that the mode frequency and the mode damping ratio corresponding to the previous acquisition time of the acquisition time can be selected from the mode frequency and the mode damping ratio of the target structure which are calculated as prior information. That is, the prior information includes the acquisition timing of the modal frequency and modal damping ratio adjacent to and before the acquisition timing corresponding to the current compression 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 calculated based on the compression 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 compression vibration response signal.
In one implementation manner, the preset modal parameter is determined based on the uncompressed compression vibration response signal, and correspondingly, the acquiring process of the preset modal parameter specifically includes:
acquiring an uncompressed vibration response signal of the target structure through a sensor;
and carrying out 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 through a sensor and used for determining a preset modal parameter, wherein a modal analysis method, such as a COVariance-driven random subspace identification method (COVariance-driven Stochastic Subspace Identification, SSI-COV), can be preset for performing modal analysis on the uncompressed vibration response signal, a default modal frequency and a default modal damping ratio of the uncompressed vibration response signal are calculated through the preset modal analysis method, and the calculated default modal frequency and the calculated default modal damping ratio are used as the preset modal parameter.
For the compression vibration response signals acquired for the first time, default modal frequency and initial modal damping determined based on the uncompressed vibration response signals are adopted as prior information, for the compression vibration response signals acquired for the first time, the modal frequency and modal damping acquired for the last time are adopted as prior information, and therefore the prior information can be acquired without arranging an additional module on the sensor, the electric energy consumption of the sensor can be reduced, the working time of the sensor can be further prolonged, and the target structure can be monitored for a long time in the mode provided by the embodiment.
S20, establishing a modal frequency range based on the prior information, and extracting target modal frequency from the modal frequency range by adopting an orthogonal matching pursuit algorithm.
Specifically, after the prior information is obtained, a modal frequency range is established based on the modal frequencies in the prior information, wherein the modal frequency range may be ±2.5% of the maximum modal frequency in the prior information, and when the search ranges of two adjacent modal frequencies overlap, the adjacent boundary values take the average value of the two modal frequencies to avoid overlapping. The mode frequency comprises the mode frequencies of a plurality of modes, the maximum mode frequency is the maximum value of the mode frequencies of a plurality of modes, and the two adjacent mode frequencies refer to the mode frequencies of the two adjacent modes.
Orthogonal matching pursuit (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 the modal parameters without signal reconstruction, thereby realizing the identification of the non-reconstruction working modal parameters based on CS. 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 free vibration function of the modal frequency range;
s22, solving the most sparse 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 method comprises the steps of, wherein,the frequency dictionary D can be expressed as:
wherein, the elements of the sub-dictionaries D 'and D' are respectively:
wherein time t= { t 1 ,t 2 ,…,t M }，ω d,l The damped natural frequency of the first order, l= {1, …, p }.
Correspondingly, the compression vibration response signal may be sparsely represented as:wherein γ is used to include ψΓ. According to CS theory, if gamma and D meet constraint equidistant conditions and gamma is sparse in D domain, a sparse coefficient matrix gamma can be obtained through an optimization algorithm, and then modal frequencies are estimated according to the positions of non-zero coefficients. From this, the target modal frequency can be obtained by solving the most sparse solution of the frequency dictionary, wherein the optimization target equation of the modal frequency is:
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 determining the modal damping ratio range, the process of establishing the modal damping ratio range based on the modal damping ratio and the target modal frequency may adopt the existing determination process of the modal damping ratio range according to the modal damping ratio and the target modal frequency in the prior information, which is not specifically described herein. After the modal damping ratio range is obtained, a target modal damping ratio is extracted 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 range of modal damping ratios specifically includes:
establishing a damping dictionary based on a free vibration function according to the modal damping ratio range;
and solving the most sparse solution of the damping dictionary to obtain a target modal damping ratio.
Specifically, the establishing process of the damping dictionary and the solving process of the most sparse solution of the damping dictionary by adopting the orthogonal matching pursuit algorithm are the same as the determining process of the modal frequency, which is not described in detail herein, and specific reference may be made to the determining process of the modal frequency.
And S40, determining the mode shape corresponding to the target structure based on the target mode frequency and the target mode damping ratio.
Specifically, after the target modal frequency and the target modal damping ratio are obtained, determining a damping dictionary corresponding to the target modal frequency and the target modal damping ratio, and then determining a modal shape 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 mode damping ratio specifically includes:
acquiring a target damping dictionary corresponding to the target modal frequency and the target modal damping ratio, and merging each row in the target damping dictionary to obtain a sparse matrix;
and based on the sparse matrix, carrying out inversion operation on a sparse model corresponding to the compression vibration response signal to obtain a mode 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, and after the target damping dictionary is acquired, the target damping dictionary is acquiredFixing elements in each row to add so as to obtain a sparse matrix with one row and multiple columns, and then obtaining a mode vibration mode through inversion operation, wherein the mode vibration mode
Further, in order to evaluate the accuracy of the long-term working mode analysis method based on prior information provided in the present embodiment, the present embodiment further provides a mode parameter accuracy evaluation method, in which the identification accuracy of the mode shape is evaluated using a mode confidence criterion Method (MAC);
wherein,,for the identified j-th order mode shape, { ψ j The j-th order theoretical mode shape is the MAC range between 0 and 1, and the more the MAC value approaches 1, the higher the accuracy of the identified mode shape is;
the accuracy of the modal frequencies adopts relative errorsEvaluation identification, wherein the relative error +.>The expression of (2) may be:
wherein omega j Represents the theoretical jth order natural frequency, omega j ′ Representing the identified jth order natural frequency,the closer to 0 the identified natural frequency accuracy is.
The accuracy of the modal damping ratio adopts relative errorEvaluation identification, wherein the relative error +.>The expression of (2) may be:
wherein, xi j Represents the theoretical jth order natural frequency, ζ j ' represents the identified jth order natural frequency,the closer to 0 the identified natural frequency accuracy is.
In summary, the embodiment provides a long-term working mode analysis method based on prior information, which includes obtaining a compression vibration response signal under random sampling of a target structure, and taking a mode frequency and a mode damping ratio calculated by the compression vibration response signal collected last time as prior information; establishing a modal frequency range based on prior information, and extracting 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; the mode shape is determined based on the target mode frequency and the target mode damping ratio. The prior information is determined based on the obtained compression vibration response signal, and an additional unit is not needed to be additionally arranged in the sensor, so that the electric quantity consumption of a wireless sensor power supply is reduced. Meanwhile, the application directly extracts the non-reconstruction working mode parameters from the compressed signals by utilizing the orthogonal matching pursuit algorithm, so that false modes can be effectively removed, the calculation time is reduced, and the recognition accuracy and the robustness of the mode parameters are improved.
To further illustrate the present embodiment provides a long-term operating modality analysis method (SDPI) based on a priori information, as shown in FIGS. 2-4, with four degrees of freedomA degree mass-spring-damping system is used as target structure, wherein c 1 Representing a mass m 1 Damping with fixed end, c 12 Representing a mass m 1 And m 2 Damping between k 1 Representing a mass m 1 Stiffness, k, between fixed end 12 Representing a mass m 1 And m 2 Rigidity of the middle, f 1 Representing a mass m 1 Force, x 1 Representing a mass m 1 The mass matrix of the target structure is m=diag ([ 1 1 1 1)]) The stiffness matrix is:
the damping matrix is C=0.1M+βK, considering β=0 and 0.0001, excitation F is Gaussian white noise with zero mean value and unit variance, simulation is carried out based on numerical software, vibration response data with 20Hz is sampled 5000 times, compression ratios are 5, 10 and 15, the compressed random signal is used for verifying the performance of an SDPI method, wherein the frequency range of the SDPI method is +/-2.5% of 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 indicate that the SDPI method has better identification accuracy. In addition, the modal damping ratio identification for random vibration requires additional pretreatment, and the pretreatment does not relate to the patent, so the damping ratio result is not shown, but the SDPI method can successfully identify the modal damping ratio under free vibration.
Table 1, modal parameters identified by the SDPI method
Based on the long-term working mode analysis method based on priori information, the embodiment provides a method for detecting structural damage, which is applied to the long-term working mode analysis method based on priori information, and the method comprises the following steps:
acquiring a compression vibration response signal under random sampling of a target structure, and acquiring working mode parameters of the compression vibration response signal through the mode parameters, wherein the working mode parameters comprise a mode shape, a mode natural frequency and a mode damping ratio;
and determining a fault detection result of the target structure based on the reference modal parameter corresponding to the target structure and the working modal parameter.
Specifically, the fault detection result includes whether to send damage, where the reference modal parameter corresponding to the target structure may be a modal parameter when the target structure fails. That is, the acquired operation mode parameter is compared with the mode parameter when no failure occurs, and when the difference between the two reaches a difference threshold (for example, 3%), 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 long-term working mode analysis method based on prior information, the embodiment provides a long-term working mode analysis method acquisition system based on prior information, as shown in fig. 5, the system includes:
the acquisition module 100 is configured to acquire a compression vibration response signal under random sampling of a target structure, and take a modal frequency and a modal damping ratio calculated by a compression vibration response signal acquired last time as prior information;
the first search module 200 is configured to establish a modal frequency range based on the prior information, and extract a target modal frequency from the modal frequency range by adopting an orthogonal matching pursuit algorithm;
the second search module 300 is configured to establish a modal damping ratio range based on the prior information and the target modal frequency, and extract a target modal damping ratio from the modal damping ratio range by adopting an orthogonal matching pursuit algorithm;
a determining module 400, configured to determine a mode shape corresponding to the target structure based on the target mode frequency and the target mode damping ratio.
Based on the above-described long-term operation mode analysis method based on the prior information, the present embodiment provides a computer-readable storage medium storing one or more programs executable by one or more processors to implement the steps in the long-term operation mode analysis method based on the prior information as described in the above-described embodiment.
Based on the long-term working mode analysis method based on priori information, the application also provides a terminal device, as shown in fig. 6, which comprises 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. Wherein the processor 20, the display 21, the memory 22 and the communication interface 23 may communicate with each other via a 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 invoke logic instructions in the memory 22 to perform the methods of the embodiments described above.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (7)
1. A long-term operating mode analysis method based on priori information, the method comprising:
acquiring a compression vibration response signal under random sampling of a target structure, and taking the modal frequency and the modal damping ratio calculated by the compression vibration response signal acquired last time as prior information;
establishing a modal frequency range based on the prior information, and extracting target modal frequency from the modal frequency range by adopting an orthogonal matching pursuit 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 pursuit algorithm;
determining a mode shape corresponding to the target structure based on the target mode frequency and the target mode damping ratio;
the extracting the target modal frequency from the modal frequency range by adopting the orthogonal matching pursuit algorithm specifically comprises the following steps: establishing a frequency dictionary based on the modal frequency range free vibration function; solving the most sparse solution of the frequency dictionary to obtain target modal frequency;
the extracting the target modal damping ratio from the modal damping ratio range by adopting the orthogonal matching pursuit algorithm specifically comprises the following steps: establishing a damping dictionary based on a free vibration function according to the modal damping ratio range; solving the most sparse solution of the damping dictionary to obtain a target modal damping ratio;
the determining the mode shape corresponding to the target structure based on the target mode frequency and the target mode damping ratio specifically includes: acquiring a target damping dictionary corresponding to the target modal frequency and the target modal damping ratio, and merging each row in the target damping dictionary to obtain a sparse matrix; and based on the sparse matrix, carrying out inversion operation on a sparse model corresponding to the compression vibration response signal to obtain a mode shape corresponding to the target structure.
2. The long-term working mode analysis method based on priori information according to claim 1, wherein the step of calculating the mode frequency and the mode damping ratio of the last acquired compression vibration response signal as the priori information specifically includes:
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 the modal damping ratio calculated by the compression vibration response signal acquired last time are used as priori information;
when the acquisition time is the first acquisition time, taking the preset modal parameter as prior information.
3. The long-term working mode analysis method based on priori information according to claim 2, wherein the obtaining process of the preset mode parameters specifically includes:
acquiring an uncompressed vibration response signal of the target structure through a sensor;
and carrying out 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. A method for detecting structural damage, characterized in that it applies a long-term operation mode analysis method based on priori information according to any one of claims 2-3, said method comprising:
acquiring a compression vibration response signal under random sampling of a target structure, and acquiring working mode parameters of the compression vibration response signal through the mode parameters, wherein the working mode parameters comprise a mode shape, a mode natural frequency and a mode damping ratio;
and determining a fault detection result of the target structure based on the reference modal parameter corresponding to the target structure and the working modal parameter.
5. The utility model provides a long-term working mode analysis method acquisition system based on priori information which characterized in that, the system includes:
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 the modal damping ratio calculated by the compression vibration response signal acquired last time as prior information;
the first search module is used for establishing a modal frequency range based on the prior information and extracting target modal frequencies from the modal frequency range by adopting an orthogonal matching pursuit algorithm;
the second search 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;
the determining module is used for determining the mode shape corresponding to the target structure based on the target mode frequency and the target mode damping ratio;
the extracting the target modal frequency from the modal frequency range by adopting the orthogonal matching pursuit algorithm specifically comprises the following steps: establishing a frequency dictionary based on the modal frequency range free vibration function; solving the most sparse solution of the frequency dictionary to obtain target modal frequency;
the extracting the target modal damping ratio from the modal damping ratio range by adopting the orthogonal matching pursuit algorithm specifically comprises the following steps: establishing a damping dictionary based on a free vibration function according to the modal damping ratio range; solving the most sparse solution of the damping dictionary to obtain a target modal damping ratio;
the determining the mode shape corresponding to the target structure based on the target mode frequency and the target mode damping ratio specifically includes: acquiring a target damping dictionary corresponding to the target modal frequency and the target modal damping ratio, and merging each row in the target damping dictionary to obtain a sparse matrix; and based on the sparse matrix, carrying out inversion operation on a sparse model corresponding to the compression vibration response signal to obtain a mode shape corresponding to the target structure.
6. A computer readable storage medium storing one or more programs executable by one or more processors to perform the steps of the a priori information based long term operational mode analysis method of any of claims 1-3, and/or the steps of the structural damage detection method of claim 4.
7. 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 long-term operation mode analysis method based on a priori information according to any of claims 1-3, and/or the steps of the detection method of structural damage according to claim 4.
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