A method for identifying operating mode damping, a damage detection method, a system, and equipment.

Abstract

This invention discloses a method for identifying operating mode damping, a damage detection method, a system, and equipment. It involves acquiring the time-domain response signal of the structure under test in the form of random subsamples to obtain multiple sets of random subsample signals. The cross-correlation function of each set of random subsample signals is calculated using natural excitation technology to obtain free vibration signals. The sparse coefficient matrix of each free vibration signal is calculated using an orthogonal matching pursuit algorithm. The modal damping ratio is determined based on the sparse coefficient matrix, thereby identifying the operating mode damping of the structure under test. The operating mode damping identification method disclosed in this embodiment uses a random subsample signal acquisition method, which meets the requirements of natural excitation technology, enabling the identification of modal damping under operating excitation. Furthermore, the identification framework used in this embodiment is frequency-mode shape-damping, which improves the identification accuracy and provides technical support for the accurate identification of structural damage.

Description

Technical Field

[0001] This application relates to the field of structural condition monitoring technology, and in particular to a method for identifying operating mode damping, a damage detection method, a system, and equipment. Background Technology

[0002] Modal damping is one of the modal parameters, and modal parameters are important parameters for structural health monitoring. Modal damping is commonly used in structural condition assessment, damage identification, and structural control.

[0003] Traditional modal identification methods require compliance with the Nyquist sampling theorem, meaning the sampling frequency must be at least twice the maximum frequency of the identified modal. For long-term monitoring, sampling at the Nyquist rate generates a large amount of data, leading to excessively low computational efficiency. Compressed sensing (CS) is a mathematical method for compressed sampling. Based on the assumption that structural vibration signals have a certain sparsity, CS can acquire signals at a lower rate than the Nyquist rate and theoretically can accurately recover the signal. Therefore, CS has been introduced into the field of modal identification to extract modal parameters from compressed signals.

[0004] Currently, modal recognition methods based on compressed sensing have not yet studied modal damping recognition under operating excitation, resulting in a decline in the quality of modal recognition and failing to meet the requirement for accurate identification of operating modal damping.

[0005] Therefore, existing technologies need to be improved. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide users with a method for identifying operating mode damping, a method for detecting damage, a system and equipment, overcoming the defect of the prior art that cannot accurately identify the operating mode damping of the structure to be detected.

[0007] The technical solution adopted by this invention to solve the technical problem is as follows:

[0008] Firstly, this embodiment provides a method for identifying operational mode damping based on compressed sensing, wherein:

[0009] The time-domain response signal of the structure to be detected is acquired in the form of random subsamples to obtain multiple sets of random subsample signals;

[0010] The cross-correlation function of each group of random sub-sample signals is calculated using natural excitation techniques to obtain the free vibration signal;

[0011] The sparse coefficient matrix of each free vibration signal is calculated using the orthogonal matching pursuit algorithm;

[0012] The modal damping ratio is determined based on the sparse coefficient matrix, thereby identifying the operating modal damping of the structure to be detected.

[0013] Optionally, the step of acquiring the time-domain response signal of the structure to be detected in the form of random subsamples includes:

[0014] Multiple sensors are used to collect compression vibration signals of the structure to be tested according to a preset subsample length and a preset number of subsamples; the compression vibration signals include one or more of the following: displacement vibration signals, velocity vibration signals and acceleration vibration signals.

[0015] Optionally, the step of calculating the sparse coefficient matrix of each free vibration signal using the orthogonal matching pursuit algorithm includes:

[0016] Based on the preset damping search range of each sub-sample, a damping dictionary is established using the free vibration function as the basis function;

[0017] Construct a search formula corresponding to the sparse representation of the damping dictionary;

[0018] The optimal solution to the search formula is obtained by solving the orthogonal matching pursuit algorithm, resulting in a sparse coefficient matrix.

[0019] Optionally, the step of constructing a search formula corresponding to the sparse representation of the damping dictionary includes:

[0020] The modal frequencies and mode shapes of the structure under test are determined based on the time-domain response signal.

[0021] Using the modal frequencies and mode shapes as basic information, a search formula is constructed.

[0022] Optionally, the step of determining the modal damping ratio based on the sparse coefficient matrix includes:

[0023] Based on the damping setting range under different modes, the damping ratio under the operating mode is obtained from the sparse coefficient matrix.

[0024] Secondly, this embodiment discloses a method for detecting structural damage, wherein it is applied to the aforementioned compression sensing-based operational mode damping identification method, the method comprising:

[0025] The temporal response signal of the structure to be detected is acquired in the form of random subsamples.

[0026] The damage detection result of the structure to be detected is determined based on the time-domain response signal.

[0027] Optionally, the step of determining the damage detection result of the structure to be detected based on the time-domain response signal includes:

[0028] Determine multiple sets of operating mode damping corresponding to the structure under test based on the time-domain response signal;

[0029] Based on the damping of multiple operating modes and the original modal damping of the structure under test, determine whether the structure under test is in a damaged state.

[0030] If the structure to be tested is in a damaged state, the location of the damage to the structure to be tested is determined based on the operating mode damping.

[0031] Thirdly, this embodiment discloses an operational mode damping identification system based on compressed sensing, which includes:

[0032] The signal acquisition module is used to acquire the time-domain response signal of the structure to be detected in the form of random subsamples, and obtain multiple sets of random subsample signals.

[0033] The first signal processing module is used to calculate the cross-correlation function of each random sub-sample signal using natural excitation technology to obtain the free vibration signal;

[0034] The second signal processing module is used to calculate the sparse coefficient matrix of each free vibration signal using the orthogonal matching pursuit algorithm;

[0035] A damping module is determined to obtain the modal damping ratio from the sparse coefficient matrix according to the damping setting range of each mode, thereby identifying the operating modal damping of the structure to be detected.

[0036] Fourthly, this embodiment provides a terminal device, wherein the terminal device includes a memory and one or more processors; the memory stores one or more programs; the programs include instructions for executing the compressed sensing-based operating mode damping identification method, and / or instructions for executing the structural damage detection method; the processor is used to execute the programs.

[0037] Fifthly, this embodiment provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the compressed sensing-based operating mode damping identification method and / or the steps in the structural damage detection method.

[0038] This invention discloses a method for identifying operating mode damping, a damage detection method, a system, and equipment. It involves acquiring the time-domain response signal of the structure under test in the form of random subsamples to obtain multiple sets of random subsample signals. The cross-correlation function of each set of random subsample signals is calculated using natural excitation technology to obtain free vibration signals. The sparse coefficient matrix of each free vibration signal is calculated using an orthogonal matched pursuit algorithm. The modal damping ratio is determined based on the sparse coefficient matrix, thereby identifying the operating mode damping of the structure under test. The operating mode damping identification method disclosed in this embodiment uses a random subsample signal acquisition method, which meets the requirements of natural excitation technology, enabling the identification of modal damping under operating excitation. Furthermore, the identification framework used in this embodiment is frequency-mode shape-damping, which improves the identification accuracy and provides technical support for the accurate identification of structural damage. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the steps of the modal damping identification method in an embodiment of the present invention.

[0040] Figure 2a This is a schematic diagram showing the location distribution of the original signal;

[0041] Figure 2b This is a schematic diagram illustrating the principle of random sampling in existing technologies;

[0042] Figure 2c This is a schematic diagram illustrating the signal principle of random subsample sampling in an embodiment of the present invention;

[0043] Figure 3 This is a flowchart of the steps of the structural damage detection method in an embodiment of the present invention;

[0044] Figure 4 This is a structural principle block diagram of the operating modal damping identification system in an embodiment of the present invention;

[0045] Figure 5 This is a schematic diagram of the four-degree-of-freedom mass-spring-damping structure in an embodiment of the present invention;

[0046] Figure 6 This is a time-domain plot of the original signal before compression of the vibration displacement response signal measured based on a four-degree-of-freedom mass-spring-damping system when β = 0.0005 in this embodiment of the invention;

[0047] Figure 7 This is the spectrum of the original signal before compression of the vibration displacement response data measured based on a four-degree-of-freedom mass-spring-damping system when β = 0.0005 in this embodiment of the invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] Compressed sensing, also known as compressed sampling or sparse sampling, is a technique for finding sparse solutions to underdetermined linear systems. It is applied in signal processing to acquire and reconstruct sparse or compressible signals. In practical applications, compressed sensing focuses on utilizing the inherent sparsity of the signal to recover the original signal from a subset of observations. Compressed sensing is generally considered to consist of two stages: compressed measurement and reconstruction. Compressed measurement focuses on processing the original signal to obtain a sparse sample representation, while reconstruction focuses on reconstructing the signal from a small number of observations based on its sparsity.

[0050] Currently, in the technology of pattern recognition based on compressed sensing, it is possible to identify the operating modal frequency and mode shape by using compressed vibration signal as input data. However, the existing technology cannot identify the operating modal damping, which leads to certain limitations in practical applications and reduces the quality of modal recognition.

[0051] To address the aforementioned issues, this embodiment discloses a method for identifying operational modal damping, a damage detection method, a system, and equipment. Based on compressed sensing, it identifies operational modal damping. Specifically, this method first acquires compressed time-domain response signals in the form of random subsamples. It then uses natural excitation technology to calculate the cross-correlation function of the time-domain response signals corresponding to each acquired random subsample. Based on the cross-correlation function between signals, it obtains the free vibration signal. Next, it uses an orthogonal matching pursuit algorithm to obtain the sparse representation of the time-domain response signal, finds the sparse coefficient matrix corresponding to the signal, and obtains the operational modal damping of the structure under test based on the sparse coefficient matrix. The method provided in this embodiment can identify modal damping under working excitation, improve the accuracy of modal parameter identification during structural inspection, and enhance the accuracy of structural damage location.

[0052] The embodiments disclosed in this invention will now be described in more detail with reference to the accompanying drawings.

[0053] This embodiment discloses a method for identifying operating mode damping based on compressed sensing, such as... Figure 1 As shown, it includes:

[0054] Step S1: Collect the time-domain response signal of the structure to be detected in the form of random subsamples to obtain multiple sets of random subsample signals.

[0055] The structure to be tested is a damped structure, meaning it contains damping, such as a four-degree-of-freedom mass-spring-damped structure. Sensors can be used to acquire the time-domain response signal of the structure under test. Specifically, in this step, the time-domain response signal of the structure under test is acquired in the form of random subsamples. These random subsamples are constructed by first creating subsamples based on existing samples, and then acquiring signals based on these constructed subsamples.

[0056] Specifically, in combination Figures 2a to 2b As shown, Figure 2a The original signal sample is used as an example. Taking the occupancy rate of the original signal sample as the entire sample, the original sampling is sequential sampling. The signals can be set to be uniformly distributed or sampled at a fixed preset interval. Figure 2b This is a schematic diagram illustrating the principle of random sampling. When performing random sampling, multiple signals can be randomly acquired based on the set number of signals to be collected, in addition to the original total signals. The location of the acquired signals is random, and signals can be acquired at intervals of one or more signals.

[0057] like Figure 2c As shown, the signal acquisition method disclosed in this embodiment of the invention is random subsample acquisition. The length and number of subsamples are first set according to the size of the structure to be detected. In one embodiment, the number of subsamples can be set to the compressed signal length divided by the subsample length; that is, if the compressed signal length is 9 and the subsample length is 3, then the number of subsamples can be set to 3. Figure 2c The subsample length is set to 3. In practice, the time interval between samples within a subsample is equal, but the time interval between adjacent subsamples is random. Therefore, this sampling method is called random subsample sampling. To avoid excessively large time intervals between subsamples, each subsample can be divided into regions of equal size, with each region's size equal to the original sample length divided by the number of subsamples. For example... Figure 2c As shown, the area between two adjacent boundaries is a region, and each region contains a subsample. Each subsample includes multiple samples. Figure 2c A subsample contains 3 samples.

[0058] In practical applications, this step includes: using multiple sensors to collect compression vibration signals of the structure to be tested according to a preset sub-sample length and a preset number of sub-samples; the compression vibration signals include one or more of displacement vibration signals, velocity vibration signals, and acceleration vibration signals. That is, first, based on the size of the structure to be tested or the required recognition accuracy, the sub-sample length and number of sub-samples are set, and then the sensors are used to collect the compression vibration signals of the structure to be tested according to the set sub-sample length, number, and the position of each sub-sample.

[0059] It is conceivable that compression vibration signals are not limited to displacement vibration signals; they can also be velocity vibration signals or acceleration vibration signals. In this step, we will take displacement vibration signals as an example to represent the acquired compression signals.

[0060] In structural dynamics theory, for a linear time-invariant system with N degrees of freedom, the vibration equation is:

[0061]

[0062] Where M∈R n×n It is the mass matrix, C∈R n×n It is the damping matrix, K∈R n×n Here, F(t) is the stiffness matrix, F(t) is the environmental excitation, and x is the system displacement response at time t. Compression signals are acquired by randomly sampling subsamples, and the compression vibration displacement response Y can be expressed as:

[0063] Y N×L =X N×M Φ M×L ;

[0064] Where Φ is the compression matrix (M×L);

[0065] Step S2: Calculate the cross-correlation function of each group of random subsample signals using natural excitation techniques to obtain the free vibration signal.

[0066] The natural excitation technique demonstrates that for a linearly invariant damped system with n degrees of freedom, under the condition that the excitation approximately satisfies Gaussian white noise, the cross-correlation function between the responses at two points in the structure has a similar expression to the impulse response function. Since the environmental vibration signal is a broadband signal, we can assume that the white noise is the excitation source of the structure under test under environmental vibration. Therefore, in this step, the natural excitation technique NExT can be used to calculate the cross-correlation function R of each subsample in the random signal Y, thereby obtaining the free vibration signal.

[0067] The cross-correlation function R can be expressed as:

[0068]

[0069] Where ψ is the mode shape, A is the coefficient, T is the time interval, and ω is the frequency response time. n,j ω d,j ξ j θ j These represent the undamped natural frequency, damped natural frequency, modal damping ratio, and phase of the j-th mode, respectively. According to the theory of natural excitation techniques, the cross-correlation function R can be considered as the free vibration response of the system, and R can also be expressed as:

[0070]

[0071] Where Γ=[Γ′Γ″] is the coefficient matrix, and Γ′ and Γ″ are matrices with elements A j The diagonal coefficient matrix S is a matrix containing elements s′ j (T)=exp(-ξ j ω n,j T)sin(ω d,j T) and s″ j (T)=exp(-ξ j ω n,j T)cos(ω d,j Modal coordinate matrix of T)

[0072] Step S3: Calculate the sparse coefficient matrix of each free vibration signal using the orthogonal matching pursuit algorithm.

[0073] Since the form of the cross-correlation function R is consistent with the compressed sensing formula, compressed sensing is represented in the form of the cross-correlation function R in this embodiment:

[0074] R = ΥD;

[0075] Where Υ represents the sparse coefficient matrix, and D represents the damping dictionary corresponding to the damping search range.

[0076] Specifically, this step includes:

[0077] Step S31: Set the damping search range for each subsample and establish a damping dictionary using the free vibration function as the basis function.

[0078] The damping dictionary D is used to include S, and the elements of the damping dictionary can be represented as:

[0079]

[0080] Substitute the damping dictionary into the search formula to construct a sparse model corresponding to the free vibration signal.

[0081] The expression for the sparse representation of the cross-correlation function R is:

[0082]

[0083] Among them, Υ is used to include ΨΓ.

[0084] That is, the sparse model corresponding to the free vibration signal is a sparse representation of the cross-correlation function R.

[0085] Step S32: Construct a search formula to obtain the sparse representation of the damping dictionary.

[0086] Specifically, the steps to construct the search formula include:

[0087] Step S311: Determine the modal frequencies and mode shapes of the structure to be detected based on the time-domain response signal.

[0088] This method uses sparse decomposition with prior information (SDPI) to extract modal frequencies and mode shapes from compressive vibration signals. These acquired modal frequencies and mode shapes are then used as the basic information for the search formula in the DISD (Discretionary Modal Damping Identification Method) provided in this embodiment. In existing methods, the compressive vibration signal is used as input data, and the modal frequencies and modal damping ratios calculated from the previously acquired compressive vibration response signal are used as prior information. A frequency search range is set, and a corresponding frequency dictionary is established. The orthogonal matching pursuit algorithm is then used to identify the modal parameters, thereby achieving the identification of operational modal frequencies and mode shapes.

[0089] Step S312: Using the modal frequencies and mode shapes as basic information, construct a search formula.

[0090] The search formula is:

[0091] Arg max(||[R(ψd′Φ) T ,R(ψd″Φ) T ]||2)std′∈D′ ξ＝{1,…,q} ,d″∈D″ ξ＝{1,…,q} ;

[0092] Where ξ is the error limit, for example: ξ = 0.001.

[0093] Step S33: Solve the optimal solution of the search formula using an optimization algorithm to obtain the sparse coefficient matrix.

[0094] Given the free vibration signal and the damping dictionary, the optimal solution is obtained by using the orthogonal matching pursuit algorithm to solve the constructed search formula, thus obtaining the sparse coefficient matrix.

[0095] According to the theory of compressed sensing (CS), if the sparse coefficient matrix Y and the damping dictionary D satisfy the constraint isometry condition, and the sparse coefficient matrix Y is sparse in the domain of the damping dictionary D, then the sparse coefficient matrix Y can be obtained by solving the above sparse model through optimization algorithm.

[0096] In practice, sparse representation solutions include two main categories: relaxation algorithms and greedy algorithms. In this embodiment, the orthogonal matching pursuit algorithm in the greedy algorithm is used to iteratively calculate the constructed sparse model, track the local optimal results, and finally obtain the optimal sparse representation result, which is the sparse coefficient matrix.

[0097] Step S4: Determine the modal damping ratio based on the sparse coefficient matrix, thereby identifying the operating modal damping of the structure to be detected.

[0098] Since the damping ratio can be directly calculated from the sparse coefficient matrix, the modal damping ratio can be obtained after the sparse coefficient matrix Y is calculated in step S3 above.

[0099] Specifically, the step of determining the modal damping ratio based on the sparse coefficient matrix includes:

[0100] Based on the damping setting range for different modal orders, the damping ratio for the operating mode is obtained from the sparse coefficient matrix. In specific implementation, when determining the modal damping ratio, the relative error of the damping reference value for different modal orders can be used. Evaluate the accuracy of the identified modal damping ratios:

[0101]

[0102] Where, ξ j Let ξ′ represent the theoretical damping ratio of the j-th mode. j This represents the j-th natural frequency of the recognition. The closer the modal damping ratio is to 0, the higher the accuracy of the recognition.

[0103] Compressed sensing theory breaks through the Nyquist sampling theorem's requirement that the sampling frequency must be greater than twice the maximum recognizable mode frequency. This theory states that signals can be compressed and sampled at rates far below the Nyquist rate, and the original signal can be accurately recovered. Therefore, compressed sensing can be applied to compressed sampling systems. Addressing the problem that existing methods cannot identify operating mode damping, this invention achieves operating mode damping identification based on compressed sensing.

[0104] The operational modal damping identification method disclosed in this embodiment proposes a new compressed sampling scheme and a sub-sample compressed sampling method, and proposes a new identification framework: frequency-mode shape-damping. In addition, a new search formula is also provided to realize the identification of modal damping under the working excitation of the structure to be tested.

[0105] Building upon the previously disclosed method for identifying operational modal damping based on compressed sensing, this embodiment discloses a method for detecting structural damage, such as... Figure 3 As shown, it is applied to the compression sensing-based operating mode damping identification method, the method comprising:

[0106] Step H1: Acquire the time-domain response signal of the structure to be detected by randomly sampling the structure to be detected.

[0107] Sensors are used to randomly sample signals from the structure under test to acquire the time-domain response signal of the structure. The random sample format used in this step is the same as the signal sampling format disclosed in step S1 above.

[0108] Step H2: Determine the damage detection result of the structure to be detected based on the time-domain response signal.

[0109] The damage detection structure of the structure to be detected is determined by analyzing the time-domain response signal collected in step H1 above.

[0110] Specifically, the step of determining the damage detection result of the structure to be detected based on the time-domain response signal includes:

[0111] Determine multiple sets of operating mode damping corresponding to the structure under test based on the time-domain response signal;

[0112] Based on the damping of multiple operating modes and the original modal damping of the structure under test, determine whether the structure under test is in a damaged state.

[0113] If the structure to be tested is in a damaged state, the location of the damage to the structure to be tested is determined based on the operating mode damping.

[0114] The acquired time-domain response signal is analyzed to obtain the modal damping corresponding to the time-domain response signal. The analyzed modal damping is compared with the modal damping of the structure under test before the fault to determine whether the structure under test has a fault. The location of the fault is determined based on the difference between the damping ratio values ​​calculated by a single sensor and other sensors. For example, if the damping ratio value calculated by a certain sensor differs from the values ​​calculated by other sensors by more than 5%, it is considered that a fault has occurred at the location of the signal acquired by that sensor.

[0115] Furthermore, based on the disclosed method for identifying operating mode damping, this embodiment discloses an operating mode damping identification system based on compressed sensing, such as... Figure 4 As shown, the identification system includes:

[0116] The signal acquisition module 100 is used to acquire the time-domain response signal of the structure to be detected in the form of random subsamples to obtain multiple sets of random subsample signals; its function is as described in step S1.

[0117] The first signal processing module 200 is used to calculate the cross-correlation function of each random subsample signal using natural excitation technology to obtain the free vibration signal; its function is as described in step S2.

[0118] The second signal processing module 300 is used to calculate the sparse coefficient matrix of each free vibration signal using the orthogonal matching pursuit algorithm; its function is as described in step S3.

[0119] The damping module 400 is determined to obtain the modal damping ratio from the sparse coefficient matrix according to the damping setting range of each mode, thereby identifying the operating modal damping of the structure to be detected. Its function is as described in step S4.

[0120] Furthermore, this embodiment also provides a terminal device, wherein the terminal device includes a memory and one or more processors; the memory stores one or more programs; the programs contain instructions for executing the compressed sensing-based operating mode damping identification method; and the processor is used to execute the programs.

[0121] Furthermore, this embodiment also provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the compressed sensing-based operating mode damping identification method and / or the steps in the structural damage detection method.

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] To further illustrate the methods, systems, and devices provided in this embodiment, such as Figure 5 , Figure 6 and Figure 7 As shown, taking a four-degree-of-freedom mass-spring-damping system as the structure to be detected as an example, the recognition effect achieved by the method, system and device provided by the present invention is verified.

[0127] First, the mass matrix of the four-degree-of-freedom mass-spring-damped system is set as M = diag(

[1111] ), and the stiffness matrix is ​​set as follows:

[0128]

[0129] The damping matrix was set to C = 0.1M + βK, considering β = 0.0005 and 0.001. The excitation F was zero-mean, unit-variance Gaussian white noise. Simulation was performed using numerical software, sampling vibration response data at a frequency of 30Hz, with 100,000 samples and compression ratios of 3.33 and 5. The compressed signal was then used to verify the performance of the operating mode damping identification method provided in this invention. Figure 6 The figure shown is a time-domain plot of the original vibration displacement response signal measured based on a four-degree-of-freedom mass-spring-damped system when β = 0.0005 in an embodiment of the present invention; as shown... Figure 7 The image shown is a spectrum of the original signal before compression of the vibration displacement response data measured based on a four-degree-of-freedom mass-spring-damping system when β = 0.0005 in an embodiment of the present invention.

[0130] In this example, the subsample length is set to 5000, and the reference channel for the Natural Excitation Technique (NExT) is [missing information]. The initial damping search range is [0-10%], with a search interval of 0.02%. For β = 0.0005, the modal frequency is ω. d,1 =2.95Hz, ω d,2 =5.87Hz, ω d,3 =7.70Hz, ω d,4 = 9.75Hz; for β = 0.001, the modal frequency is ωd,1 =2.95Hz, ω d,2 =5.86Hz, ω d,3 =7.70Hz, ω d,4 =9.75Hz. The operating mode damping identification method disclosed in this embodiment is used to identify the operating mode damping. The identified modal parameters are shown in Table 1. As can be seen from the results in Table 1, the operating mode damping identification method (DISD method) provided in this embodiment has good identification accuracy.

[0131] Table 1

[0132]

[0133] This invention discloses a method for identifying operating mode damping, a damage detection method, a system, and equipment. It involves acquiring the time-domain response signal of the structure under test in the form of random subsamples to obtain multiple sets of random subsample signals. The cross-correlation function of each set of random subsample signals is calculated using natural excitation technology to obtain free vibration signals. The sparse coefficient matrix of each free vibration signal is calculated using an orthogonal matched pursuit algorithm. The modal damping ratio is determined based on the sparse coefficient matrix, thereby identifying the operating mode damping of the structure under test. The operating mode damping identification method disclosed in this embodiment uses a random subsample signal acquisition method, which meets the requirements of natural excitation technology, enabling the identification of modal damping under operating excitation. Furthermore, the identification framework used in this embodiment is frequency-mode shape-damping, which improves the identification accuracy and provides technical support for the accurate identification of structural damage.

[0134] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application.

Claims

1. A method for identifying operating mode damping based on compressed sensing, characterized in that, include: The time-domain response signal of the structure to be detected is acquired in the form of random subsamples to obtain multiple sets of random subsample signals; The cross-correlation function of each group of random sub-sample signals is calculated using natural excitation techniques to obtain the free vibration signal; The sparse coefficient matrix of each free vibration signal is calculated using the orthogonal matching pursuit algorithm; The modal damping ratio is determined based on the sparse coefficient matrix, thereby identifying the operating modal damping of the structure to be detected.

2. The method for identifying operating mode damping according to claim 1, characterized in that, The step of acquiring the time-domain response signal of the structure to be detected in the form of random subsamples includes: Multiple sensors are used to collect compression vibration signals of the structure to be tested according to a preset subsample length and a preset number of subsamples; the compression vibration signals include one or more of the following: displacement vibration signals, velocity vibration signals and acceleration vibration signals.

3. The method for identifying operating mode damping according to claim 1, characterized in that, The steps for calculating the sparse coefficient matrix of each free vibration signal using the orthogonal matching pursuit algorithm include: Based on the preset damping search range of each sub-sample, a damping dictionary is established using the free vibration function as the basis function; Construct a search formula corresponding to the sparse representation of the damping dictionary; The optimal solution to the search formula is obtained by solving the orthogonal matching pursuit algorithm, resulting in a sparse coefficient matrix.

4. The method for identifying operating mode damping according to claim 3, characterized in that, The steps for constructing a search formula corresponding to the sparse representation of the damping dictionary include: The modal frequencies and mode shapes of the structure under test are determined based on the time-domain response signal. Using the modal frequencies and mode shapes as basic information, a search formula is constructed.

5. The method for identifying operating mode damping according to claim 1, characterized in that, The step of determining the modal damping ratio based on the sparse coefficient matrix includes: Based on the damping setting range under different modes, the damping ratio under the operating mode is obtained from the sparse coefficient matrix.

6. A method for detecting structural damage, characterized in that, It is applied to the compression sensing-based operating mode damping identification method as described in any one of claims 1-5, the method comprising: The temporal response signal of the structure to be detected is acquired in the form of random subsamples. The damage detection result of the structure to be detected is determined based on the time-domain response signal.

7. The method for detecting structural damage according to claim 6, characterized in that, The step of determining the damage detection result of the structure to be detected based on the time-domain response signal includes: Determine multiple sets of operating mode damping corresponding to the structure under test based on the time-domain response signal; Based on the damping of multiple operating modes and the original modal damping of the structure under test, determine whether the structure under test is in a damaged state. If the structure to be tested is in a damaged state, the location of the damage to the structure to be tested is determined based on the operating mode damping.

8. A system for identifying operating mode damping based on compressed sensing, characterized in that, include: The signal acquisition module is used to acquire the time-domain response signal of the structure to be detected in the form of random subsamples, and obtain multiple sets of random subsample signals. The first signal processing module is used to calculate the cross-correlation function of each random sub-sample signal using natural excitation technology to obtain the free vibration signal; The second signal processing module is used to calculate the sparse coefficient matrix of each free vibration signal using the orthogonal matching pursuit algorithm; A damping module is determined to obtain the modal damping ratio from the sparse coefficient matrix according to the damping setting range of each mode, thereby identifying the operating modal damping of the structure to be detected.

9. A terminal device, characterized in that, The terminal device includes a memory and one or more processors; the memory stores one or more programs; the programs include instructions for executing the compression sensing-based operating mode damping identification method as described in any one of claims 1-5, and / or instructions for executing the structural damage detection method as described in claim 6 or 7; the processor is used to execute the programs.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs that can be executed by one or more processors to implement the steps in the compression sensing-based operating mode damping identification method as described in any one of claims 1-5, and / or the steps in the structural damage detection method as described in claim 6 or 7.

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