A method and system for automatically checking modal parameter identification of a power transmission tower

Abstract

The application provides a power transmission tower modal parameter identification method and system for automatic inspection, converts acceleration response data of each measuring point of a structure to be identified into natural vibration frequencies of the structure to be detected based on a dynamic differential equation, obtains a power spectrum of the structure to be identified, and obtains initial natural vibration frequencies of each measuring point of the structure to be identified; coherence analysis is performed on the initial natural vibration frequencies of any two measuring points, and a plurality of coherent initial natural vibration frequency peak points between the coherence function of 0.8-1 are obtained; cluster analysis is performed on the plurality of coherent initial natural vibration frequency peak points, and the coherent initial natural vibration frequency peak points within the cluster identification range are selected as real natural vibration frequency peak points, and the frequency corresponding to the real natural vibration frequency peak points is the real natural vibration frequency; the real natural vibration frequency is selected in the power spectrum of the structure to be identified for modal identification analysis, and the subjectivity and noise interference problems existing in the selection of the real natural vibration frequency are solved.

Description

Technical Field

[0001] This invention belongs to the field of structural health monitoring technology, specifically to an automatic inspection method and system for identifying modal parameters of transmission towers. Background Technology

[0002] With the rapid development of modern society and economy, civil structures are trending towards larger, more complex, and lighter designs. Research in the field of structural health monitoring has attracted increasing attention, as accurate parameter evaluation can reflect the rational dynamic characteristics of a structure under certain conditions. In civil structures, the assessment of the actual health status of a structure is mainly based on on-site measurements and modal identification of its dynamic behavior (including natural frequencies, damping ratios, and mode shapes). Operational modal analysis provides an effective method for acquiring structural response signals through environmental vibration tests (e.g., wind, traffic, and ground vibrations), and identifying the modal properties of the structure from the output signals alone.

[0003] Based on response signals (typically acceleration), structural modes can be identified using both time-domain and frequency-domain methods. Compared to time-domain methods, frequency-domain methods can model unknown inputs within the resonance band of interest based on a stochastic process with constant power spectrum characteristics, while ignoring other bands with irrelevant information, thus avoiding modeling errors caused by potential noise in other bands. However, before performing the identification process, the true natural frequencies need to be determined through power spectrum analysis. Environmental vibrations do not require artificial excitation of the structure at the expense of precise signal-to-noise ratio control. The structure's natural frequencies are submerged in noise signals and cannot be intuitively determined at low signal-to-noise ratios. Inexperienced selection can lead to inaccurate identification results of the structure's natural frequencies, thereby affecting the scientific validity of structural health monitoring decisions. Summary of the Invention

[0004] To address the issues of subjectivity and noise interference in the selection of the true natural frequency in existing methods, this invention provides an automatic verification method and system for identifying the modal parameters of transmission towers.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an automatic method for identifying modal parameters of transmission towers, comprising the following steps:

[0006] S1. Based on the dynamic differential equation, the acceleration response data of each measuring point of the structure to be identified is converted into the natural frequency of the structure to be detected, the power spectrum of the structure to be identified is obtained, and the initial natural frequency of each measuring point of the structure to be identified is obtained.

[0007] S2. Perform coherent analysis on the initial natural frequencies of any two measurement points in step S1 to obtain multiple coherent initial natural frequency peak points with the coherence function between 0.8 and 1.

[0008] S3. Perform cluster analysis on the multiple coherent initial natural frequency peak points in step S2, select the coherent initial natural frequency peak points within the cluster recognition range as the true natural frequency peak points, and the frequency corresponding to the true natural frequency peak points is the true natural frequency.

[0009] S4. Select the true natural frequency from the power spectrum of the structure to be identified for modal identification analysis.

[0010] Furthermore, in step S2, coherence analysis is performed based on the autocoherence spectrum of the initial natural frequencies of any two measurement points and the mutual coherence spectrum of the initial natural frequencies of any two measurement points to obtain the relationship curve between frequency and coherence function, and multiple coherent initial natural frequency peak points with coherence function between 0.8 and 1 are selected.

[0011] Furthermore, in step S2, the coherence function between the initial natural frequencies of any two measuring points... It can be represented as:

[0012]

[0013] Among them, G AA (ω i ), G BB (ω i ) and G AB (ω i These are the autocoherence spectrum of the initial natural frequency of measuring point A, the autocoherence spectrum of the initial natural frequency of measuring point B, and the mutual coherence spectrum between the initial natural frequencies of measuring points A and B, respectively.

[0014] Furthermore, in step S2, the cross-coherence spectrum between the initial natural frequencies of measuring points A and B is as follows:

[0015]

[0016] Among them, G AB (ω i C AB (ω i ) and Q AB (ω i θ represents the mutual interference spectrum between the initial natural frequencies of measuring points A and B, the real part in complex form, and the imaginary part in complex form, respectively. AB (ω i ) is the phase angle of the signals at measurement points A and B, where i is the imaginary unit.

[0017] Furthermore, in step S3, the clustering criteria for performing cluster analysis are as follows:

[0018]

[0019] in, and Both are correlation functions obtained from pairwise analysis of N measurement points. MP is the distance function obtained from the difference of coherence analysis between measurement points, and ε is the cluster recognition range, which is determined by tolerance and frequency resolution.

[0020] Furthermore, in step S3, when the cluster identification range ε ​​is 0.2, the cluster analysis result is as follows:

[0021]

[0022] Among them, v q The classification results are the initial natural frequency peak points selected after correlation analysis, which are divided into true natural frequency peak points and noise points.

[0023] Furthermore, in step S1, the environmental excitation method is used to obtain the acceleration response data of each measuring point of the structure to be identified.

[0024] Furthermore, in step S1, the specific dynamic equation is:

[0025]

[0026] in, Represents the i-th order acceleration. Y represents the i-th order velocity. i (t) represents the i-th order displacement, k i Let m represent the i-th order regularized stiffness. i Let f represent the quality of the i-th order regularization. i (t) represents the i-th order external load, ω i Let ξ represent the natural frequency of the i-th mode. i This represents the damping ratio of the i-th mode.

[0027] The present invention also provides an automatic verification system for identifying modal parameters of transmission towers, comprising:

[0028] The data acquisition module is used to convert the acceleration response data of each measuring point of the structure to be identified into the natural frequency of the structure to be detected based on the dynamic differential equation, obtain the power spectrum of the structure to be identified, and obtain the initial natural frequency of each measuring point of the structure to be identified.

[0029] The coherence analysis module is used to perform coherence analysis on the initial natural frequencies of any two measurement points, and obtain multiple coherent initial natural frequency peak points with the coherence function between 0.8 and 1.

[0030] The clustering analysis module is used to perform clustering analysis on multiple coherent initial natural frequency peak points. The coherent initial natural frequency peak points within the clustering recognition range are selected as the true natural frequency peak points, and the frequency corresponding to the true natural frequency peak points is the true natural frequency.

[0031] The modal identification module is used to select the true natural frequencies from the power spectrum of the structure to be identified for modal identification analysis.

[0032] Compared with the prior art, the present invention has at least the following beneficial effects:

[0033] This invention provides an automatic method for identifying modal parameters of transmission towers. It utilizes the cross-correlation principle of different measuring points to determine the true natural frequency of the structure. At the true natural frequency, the coherence function between the signals from two measuring points will approach 1. Then, a clustering algorithm is used to analyze the coherence function of each group, clustering the natural frequencies that meet the coherence requirements. The true natural frequency of the structure is found among the peak values ​​of natural frequencies containing noise, resulting in a true natural frequency with quantitative indicators. This method is applicable to personnel with varying levels of professional background, does not rely on the experience or preferences of practitioners, and avoids the influence of subjective human factors on the identification results.

[0034] This invention has a clear physical meaning and effectively combines dynamic theory and machine learning methods. It only requires selecting the frequency band range and recognition accuracy of interest, and then inputting the test response data to directly perform calculations and distinguish the true natural frequency from the noise.

[0035] Using the true natural frequency calculated by this invention as a parameter, the true dynamic characteristics of the structure can be accurately and reliably identified, avoiding the influence of false modes caused by noise interference and human factors on the results, and providing a basis for the automated development of modal identification. Based on the framework of this method, the problem of eliminating noise interference and subjective human selection is eliminated, providing a foundation for the automatic identification and real-time monitoring of structural modal parameters. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating the method of the present invention.

[0037] Figure 2 This is a schematic diagram of the results after coherence analysis in an example of the present invention.

[0038] Figure 3 This is a schematic diagram of the results after cluster analysis in an example of the present invention.

[0039] Figure 4 This is the result of identifying the true natural frequency after coherent clustering analysis in the example of this invention. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0041] This embodiment is implemented based on the technical solution of the present invention, and provides detailed implementation methods and specific operation processes. The description is for the purpose of explaining the present invention and not limiting it, but the scope of protection of the present invention is not limited to the following embodiment.

[0042] When using frequency domain methods for modal identification, the true natural frequency needs to be confirmed first. However, due to strong environmental noise, the true natural frequency can be submerged in noise signals when the signal-to-noise ratio is low, making it impossible to judge intuitively. Furthermore, in the presence of noise interference, personnel with varying backgrounds may have different subjective preferences when selecting the true natural frequency. This noise interference and subjective selection can prevent the true natural frequency from being accurately identified, thus hindering accurate modal identification results.

[0043] Therefore, this invention proposes a method for automatically verifying the modal parameters of transmission towers based on coherent clustering theory within the framework of structural dynamics. This method solves the problem by using coherent clustering analysis to obtain a quantitative method for determining the true natural frequency. The basic idea is to use the cross-correlation principle of different measurement points to determine the true natural frequency of the structure. At the true natural frequency, the coherence function between the signals from two measurement points will approach 1. Then, a clustering algorithm is used to perform clustering analysis on the true natural frequencies obtained after coherent analysis. Peaks that meet the requirements are clustered, and the true natural frequency of the structure is found among the natural frequency peaks containing noise, thus obtaining the true natural frequency with quantitative indicators. Specifically:

[0044] First, acceleration response data of the structure to be identified is collected. Then, a frequency band of interest is selected, and the true natural frequency of the structure is verified using the principle of coherent analysis. Further, all peak points in the coherent analysis results are extracted, and clustering methods are used to analyze the peaks that meet the requirements. Finally, the peak points of the true natural frequency are selected, and irrelevant and noise points are filtered out to obtain the true natural frequency for subsequent modal identification analysis. The coherent clustering framework developed in this method can also provide a foundation for the automatic identification and real-time monitoring of modal parameters.

[0045] Example

[0046] like Figure 1 As shown, an automatic method for identifying modal parameters of transmission towers includes the following steps:

[0047] S1. Using the environmental excitation method, the acceleration response data of each measuring point of the structure to be identified is collected. Based on the dynamic differential equation, the acceleration response data of each measuring point of the structure to be identified is converted into the natural frequency of the structure to be detected, the power spectrum of the structure to be identified is established, and the initial natural frequency of each measuring point of the structure to be identified is obtained.

[0048] S2. Perform coherent analysis on the initial natural frequencies of any two measurement points in step S1. Based on the principle that the coherence between the signals of the two measurement points is close to 1 at the true natural frequency, select multiple coherent initial natural frequency peak points with coherence between 0.8 and 1.

[0049] S3. Perform cluster analysis on the peak points obtained in S2. Select the coherent initial natural frequency peak points of the structure to be detected that are within the cluster recognition range from the coherent initial natural frequency peak points containing noise as the true natural frequency peak points.

[0050] The true natural frequency peak point is obtained in S4 and S3, which corresponds to the natural frequency on the power spectrum. The true natural frequency of the structure to be identified can be used for modal identification analysis.

[0051] In S1, based on the dynamic analysis theory of linear multi-degree-of-freedom systems, the dynamic equations can be used in canonical coordinates, giving the relationship between inputs, outputs (e.g., acceleration response), and structural characteristics (e.g., natural frequencies):

[0052]

[0053] in, Represents the i-th order acceleration. Y represents the i-th order velocity. i (t) represents the i-th order displacement, k i Let m represent the i-th order regularized stiffness. i Let f represent the quality of the i-th order regularization. i (t) represents the i-th order external load, ω i Let ξ represent the natural frequency of the i-th mode. i This represents the damping ratio of the i-th mode.

[0054] Based on the dynamic equations, the power spectrum can be calculated, such as... Figure 4 As shown, the true natural frequency in the initial natural frequency obtained at this time is mixed with noise, and the true natural frequency cannot be determined based on evidence.

[0055] In step S2, coherence analysis is performed by acquiring the autocoherence spectrum of the initial natural frequencies at any two measurement points and the cross-coherence spectrum of the initial natural frequencies between any two measurement points. Coherence analysis is a method in the frequency domain for determining the validity of a signal and the true characteristics of a structure. The coherence function is defined as a measure of the relationship between two measurement points. Structurally, any two measurement points must have identical values ​​at the true natural frequencies, meaning the coherence function value is 1. However, due to unavoidable noise interference, the coherence function is always less than 1 but close to 1, providing a quantitative standard for determining the true natural frequencies. Therefore, the coherence function between any two measurement points... It can be represented as:

[0056]

[0057] Among them, G AA (ω i ), G BB (ω i ) and G AB (ω i These are the autocoherence spectrum of the initial natural frequency at measuring point A, the autocoherence spectrum of the initial natural frequency at measuring point B, and the mutual coherence spectrum of the initial natural frequencies between measuring points A and B, respectively.

[0058] The coherence function does not reflect the relationship between input and output, but rather the relationship between two outputs, with two extreme cases being... and The former indicates that the initial natural frequencies of points A and B are uncorrelated, while the latter indicates that the initial natural frequencies of points A and B are exactly the same, meaning they are the same measurement point. In fact, due to the unavoidable presence of noise, the coherence function always has the following condition:

[0059]

[0060] Since the two outputs must be correlated at the true natural frequencies to reflect the overall vibration characteristics of the structure, the coherence function must be close to 1.0 at the true natural frequencies. In this invention, it is considered that when the coherence function is between 0.8 and 1, it can be proven that the initial natural frequencies of the two measuring points reflect the true natural frequencies of the structure. If this criterion is not met, it can be determined that the initial natural frequencies of points A and B are uncorrelated and cannot be identified as the coherent initial natural frequencies of the structure under test.

[0061] Preferably, the response in the time domain is transformed to the frequency domain, and cross-coherence spectrum analysis is performed on the initial natural frequencies of any two measurement points. When the coherence is between 0.8 and 1, the initial natural frequencies of measurement point A and measurement point B of the structure under test can be determined to be coherent initial natural frequencies.

[0062] Preferably, the mutual interference spectrum of the initial natural frequencies at any two measuring points can be considered as a multi-input, single-output system, and the complex expression of the mutual interference spectrum is:

[0063]

[0064] Among them, G AB (ω i C AB (ω i ) and Q AB (ω iθ represents the mutual interference spectrum of the initial natural frequencies between measuring points A and B, the real part in complex form, and the imaginary part in complex form, respectively. AB (ω i ) is the phase angle of the signals at measurement points A and B, where i is the imaginary unit.

[0065] It has the following properties: Based on acceleration data, the power spectrum can be calculated using dynamic equations, such as... Figure 4 As shown, the results display the main modes and their corresponding natural frequencies, with the peak values ​​representing some potential natural frequencies containing noise interference.

[0066] Based on the power spectrum results, it can be concluded that noise interference does indeed make it difficult to determine the true natural frequency. Clearly, this can lead to different researchers choosing the wrong true frequency, resulting in erroneous analyses. Figure 2 As shown, the true natural frequency of the structure was confirmed using the method proposed in this invention, coherence analysis was performed, and all peak points were collected. Significant peaks were observed compared to the power spectrum.

[0067] Furthermore, in step S3, a density-based clustering algorithm is used to perform cluster analysis on the coherent initial natural frequencies of the coherent function between 0.8 and 1, and the clustering results of the true natural frequency peak points at each natural frequency are obtained, realizing the function of automatic judgment and identification.

[0068] Considering the effective interval in coherence analysis, and taking into account the factors of tolerance range and frequency resolution, density-based clustering algorithms propose a 2-norm clustering criterion suitable for structural dynamic characteristic analysis:

[0069]

[0070] in, and It is the coherence function obtained by pairwise analysis of N measurement points, MP is the distance function obtained by subtracting the coherence analysis between measurement points, and ε is the cluster recognition range, which is determined by the tolerance and frequency resolution.

[0071] Preferably, using density-based clustering algorithms for cluster analysis can identify clusters of arbitrary shapes and outliers (noise) in the data, and identify points that do not belong to any cluster, which can be used for density-based outlier detection.

[0072] Furthermore, the clustering identification range ε ​​is set to 0.2, and the results of the clustering analysis are as follows:

[0073]

[0074] Among them, v q The classification results of the peak points selected after correlation analysis are divided into real points and noise points, which are determined by the interpolation range of the distance function MP and the cluster recognition range ε.

[0075] Furthermore, after coherent clustering analysis, multiple true natural frequency peaks can be obtained. Based on these peaks, the true natural frequency of the structure under test can be determined from the power spectral density. The true natural frequency can be used for modal identification using frequency domain methods to obtain modal parameters such as mode shape and damping ratio.

[0076] In specific implementation examples, based on the peak point results selected by coherence analysis, the coherence function is set... The peak points are uncorrelated, indicating that the corresponding frequencies are not the true natural frequencies of the structure; coherence function The peak points are automatically clustered using the proposed clustering criteria, such as... Figure 3 As shown, peak points can be divided into two categories: true natural frequency peak points and noise points. True natural frequency peak points reflect the true natural frequency of the structure, while noise points are the noise interference results from the coherent clustering analysis of each measurement point and cannot be determined as true natural frequencies. Ultimately, in this embodiment, the 6th order true natural frequency is obtained in the principal axis direction, effectively distinguishing between the true frequency and noise interference.

[0077] Based on the analysis results of coherent clustering, the true natural frequencies of the structure can be quantitatively and definitively determined from the initial power spectrum containing noise components, such as... Figure 4 As shown, there are a total of 6 true natural frequencies along the principal axis, all of which have been accurately identified, eliminating the interference of manual selection and noise, and improving the accuracy of true natural frequency identification.

[0078] Based on the analysis results of the embodiments, the proposed method demonstrates good applicability and robustness for modal analysis of engineering structures under environmental excitation. Furthermore, the proposed method is applicable to interpreting the results of any parameter frequency domain identification technique, and can be used to further solve for dynamic characteristic parameters such as mode shapes and damping ratios, such as the half-power bandwidth method and frequency domain decomposition method.

[0079] The present invention also provides an automatic verification system for identifying modal parameters of transmission towers, comprising:

[0080] The data acquisition module is used to convert the acceleration response data of each measuring point of the structure to be identified into the natural frequency of the structure to be detected based on the dynamic differential equation, obtain the power spectrum of the structure to be identified, and obtain the initial natural frequency of each measuring point of the structure to be identified.

[0081] The coherence analysis module is used to perform coherence analysis on the initial natural frequencies of any two measurement points, and obtain multiple coherent initial natural frequency peak points with the coherence function between 0.8 and 1.

[0082] The clustering analysis module is used to perform clustering analysis on multiple coherent initial natural frequency peak points. The coherent initial natural frequency peak points within the clustering recognition range are selected as the true natural frequency peak points, and the frequency corresponding to the true natural frequency peak points is the true natural frequency.

[0083] The modal identification module is used to select the true natural frequencies from the power spectrum of the structure to be identified for modal identification analysis.

Claims

1. A method for automatically identifying modal parameters of transmission towers, characterized in that, Includes the following steps: S1. Based on the dynamic differential equation, the acceleration response data of each measuring point of the structure to be identified is converted into the natural frequency of the structure to be detected, the power spectrum of the structure to be identified is obtained, and the initial natural frequency of each measuring point of the structure to be identified is obtained. S2. Perform coherent analysis on the initial natural frequencies of any two measurement points in step S1 to obtain multiple coherent initial natural frequency peak points with the coherence function between 0.8 and 1. S3. Perform cluster analysis on the multiple coherent initial natural frequency peak points in step S2, select the coherent initial natural frequency peak points within the cluster recognition range as the true natural frequency peak points, and the frequency corresponding to the true natural frequency peak points is the true natural frequency. S4. Select the true natural frequency from the power spectrum of the structure to be identified for modal identification analysis; In step S1, the dynamic differential equation is specifically as follows: in, Represents the i-th order acceleration. Represents the i-th order velocity. Represents the i-th order displacement. Denotes the i-th order regularized stiffness. Denotes the quality of the i-th order regularization. Represents the i-th order external load. Represents the natural frequency of the i-th mode. This represents the damping ratio of the i-th mode.

2. The method for automatically identifying modal parameters of transmission towers according to claim 1, characterized in that, In step S2, coherence analysis is performed based on the autocoherence spectrum of the initial natural frequencies of any two measurement points and the mutual coherence spectrum of the initial natural frequencies of any two measurement points to obtain the relationship curve between frequency and coherence function, and multiple coherent initial natural frequency peak points with coherence function between 0.8 and 1 are selected.

3. The method for automatically identifying modal parameters of transmission towers according to claim 2, characterized in that, In step S2, the coherence function between the initial natural frequencies of any two measuring points , can be represented as: in, , and These are the autocoherence spectrum of the initial natural frequency of measuring point A, the autocoherence spectrum of the initial natural frequency of measuring point B, and the mutual coherence spectrum between the initial natural frequencies of measuring points A and B.

4. The method for automatically identifying modal parameters of transmission towers according to claim 3, characterized in that, In step S2, the cross-coherence spectrum between the initial natural frequencies of measuring points A and B is as follows: in, , and These are the mutual interference spectrum between the initial natural frequencies of measuring points A and B, the real part in complex form, and the imaginary part in complex form, respectively. It is the phase angle of the signals at measurement points A and B, where i is the imaginary unit.

5. The method for automatically identifying modal parameters of transmission towers according to claim 1, characterized in that, In step S3, the clustering criteria for cluster analysis are as follows: in, and All N The correlation function obtained from pairwise analysis of each measurement point MP It is the distance function obtained by subtracting the coherence between measurement points. It is the clustering recognition range, which is determined by tolerance and frequency resolution.

6. The method for automatically identifying modal parameters of transmission towers according to claim 5, characterized in that, In step S3, the clustering identification range When the value is 0.2, the cluster analysis results are as follows: in, The classification results are the initial natural frequency peak points selected after correlation analysis, which are divided into true natural frequency peak points and noise points.

7. The method for automatically identifying modal parameters of transmission towers according to claim 1, characterized in that, In step S1, the environmental excitation method is used to obtain the acceleration response data of each measuring point of the structure to be identified.

8. An automatic detection system for identifying modal parameters of transmission towers, characterized in that, The system comprises the steps of performing the method according to any one of claims 1 to 7, wherein the system includes: The data acquisition module is used to convert the acceleration response data of each measuring point of the structure to be identified into the natural frequency of the structure to be detected based on the dynamic differential equation, obtain the power spectrum of the structure to be identified, and obtain the initial natural frequency of each measuring point of the structure to be identified. The coherence analysis module is used to perform coherence analysis on the initial natural frequencies of any two measurement points, and obtain multiple coherent initial natural frequency peak points with the coherence function between 0.8 and 1. The clustering analysis module is used to perform clustering analysis on multiple coherent initial natural frequency peak points. The coherent initial natural frequency peak points within the clustering recognition range are selected as the true natural frequency peak points, and the frequency corresponding to the true natural frequency peak points is the true natural frequency. The modal identification module is used to select the true natural frequencies from the power spectrum of the structure to be identified for modal identification analysis.

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