Turnout zone modal parameter automatic identification method and system

Abstract

The invention provides a turnout zone modal parameter automatic identification method and system, and relates to the technical field of turnout zone modal testing, and the method comprises the steps: obtaining first information which comprises the size information of a target turnout zone and the connecting line length of testing equipment; performing region division processing on the target turnout region according to the first information to obtain a test region division result; sequentially performing excitation and response measurement on each test area according to the test area division result and the partition MRIT modal test scheme to obtain excitation-response data; performing matrix construction processing according to the excitation-response data to obtain a modal parameter stability diagram of each test area; performing clustering processing according to the modal parameter stability diagram to obtain a clustering result; and obtaining a modal parameter identification result based on a clustering result. According to the spatial clustering algorithm based on density, false modal and interference modal clusters can be automatically removed, the precision and efficiency of modal parameter analysis are greatly improved, and finally the overall modal parameter distribution of the turnout system is generated.

Description

Technical Field

[0001] The present invention relates to the technical field of turnout area modal testing, and in particular to a method and system for automatically identifying modal parameters of a turnout area. Background Art

[0002] As an important part of the railway system, turnouts are mainly used to change the direction of train travel and help trains switch smoothly from one track to another. They have a complex structure, mainly including key components such as point rails, stock rails, frogs and connecting rods. During the operation of high-speed trains, turnout systems need to withstand frequent vibrations and shocks, and the connection parts of various components are prone to wear and fatigue damage. Modal testing is an important means to study the dynamic characteristics of structures. By obtaining the modal parameters of the turnout system, its response characteristics under various working conditions can be revealed, providing a scientific basis for the structural optimization design, dynamic performance regulation, vibration characteristics analysis and health monitoring of turnouts. However, the existing modal testing methods are mostly applied to ordinary section lines, and there is still a research gap for complex turnout systems. Although traditional laboratory testing methods (such as exciter and drop shaft test) can effectively excite structural modes, the equipment is large in scale and difficult to apply to actual working conditions on site; although conventional hammer tests are convenient, the excitation range is limited and cannot fully cover the complex structural characteristics of turnouts, resulting in inaccurate distribution of modal parameters. In addition, existing modal parameter identification methods rely on manual intervention, and engineers are required to subjectively judge the stability of modal parameters. This is not only time-consuming and labor-intensive, but also prone to inaccurate results due to subjective errors, making it difficult to meet the needs of efficient and accurate analysis of complex systems.

[0003] Based on the above-mentioned shortcomings of the prior art, there is an urgent need for a method and system for automatically identifying modal parameters in a turnout area. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for automatically identifying modal parameters of a turnout area to improve the above-mentioned problem. In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides a method for automatically identifying modal parameters of a turnout area, comprising:

[0006] Acquiring first information, wherein the first information includes size information of a target turnout area and a length of a connection line of a test device;

[0007] Performing a region division process on the target turnout area according to the first information, dividing each turnout rail into a plurality of modal test areas based on the coherence principle of the excitation-response data, and obtaining a test area division result;

[0008] According to the test area division result and the preset partitioned MRIT modal test scheme, each test area is sequentially excited and responded to, and common measurement points are reserved between adjacent test areas to obtain excitation-response data;

[0009] Performing matrix construction processing according to the excitation-response data, and calculating the modal parameters of each test area to obtain a modal parameter stability diagram of each test area;

[0010] Performing clustering processing according to the modal parameter stability diagram, removing false modes and modal cluster interference, and screening stable modal parameters to obtain clustering results;

[0011] Based on the clustering results, the modal vibration shapes in the adjacent areas with frequencies within a preset range are normalized with the common measuring points as the reference, and the modal parameter identification results are obtained by splicing. The modal parameter identification results are the modal parameter distribution of the turnout system as a whole.

[0012] In a second aspect, the present application also provides a system for automatically identifying modal parameters of a turnout area, including:

[0013] An acquisition module, used for acquiring first information, wherein the first information includes size information of a target turnout area and a connection line length of a test device;

[0014] A division module, used for performing a region division process on the target turnout area according to the first information, dividing each turnout rail into a plurality of modal test areas based on the coherence principle of the excitation-response data, and obtaining a test area division result;

[0015] A test module, used to perform excitation and response measurements on each test area in turn according to the test area division result and a preset partitioned MRIT modal test scheme, and retain common measurement points between adjacent test areas to obtain excitation-response data;

[0016] A construction module, used to perform matrix construction processing according to the excitation-response data, and calculate the modal parameters of each test area to obtain a modal parameter stability diagram of each test area;

[0017] A clustering module, used for performing clustering processing according to the modal parameter stability diagram, removing false modes and modal cluster interference, and screening stable modal parameters to obtain clustering results;

[0018] The output module, based on the clustering results, normalizes the modal vibration shapes in the adjacent areas with frequencies within a preset range with reference to the common measuring points, and splices them to obtain the modal parameter identification results, which are the modal parameter distribution of the turnout system as a whole.

[0019] The beneficial effects of the present invention are:

[0020] Based on the characteristics of the complex structure of the turnout, the present invention adopts a regional division strategy to divide each turnout rail into multiple modal test areas, and normalizes and splices the modal parameters by retaining common measurement points between adjacent areas. In addition, combined with a density-based spatial clustering algorithm, this method can automatically remove false modes and interfering modal clusters, greatly improve the accuracy and efficiency of modal parameter analysis, and finally generate the overall modal parameter distribution of the turnout system, providing reliable data support for long-term health monitoring, fault diagnosis and optimization design of the turnout. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 It is a schematic flow chart of the method for automatically distinguishing modal parameters of a turnout area described in an embodiment of the present invention;

[0023] Figure 2 It is a schematic diagram of each modal test area in the turnout area;

[0024] Figure 3 It is a schematic diagram of the partitioned MRIT modal test principle;

[0025] Figure 4 This is the flow chart of DBSCAN clustering algorithm;

[0026] Figure 5 Schematic diagram of modal parameter identification results. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0029] Embodiment 1:

[0030] This embodiment provides a method for automatically identifying modal parameters of a turnout area.

[0031] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300, step S400, step S500 and step S600.

[0032] Step S100, obtaining first information, the first information including size information of the target turnout area and the length of the connection line of the test equipment;

[0033] It is understandable that the size information of the target turnout area includes the length, width, location of the connection parts and related geometric characteristics of each rail in the turnout area, which is the premise for dividing the test area and determining the measurement point layout plan. The length of the connecting line of the test equipment is an important constraint affecting the test range, especially in the field test environment. Due to the physical limitations of the equipment, the length of the connecting line directly determines the reach of the sensor.

[0034] Step S200, performing a region division process on the target turnout area according to the first information, dividing each turnout rail into a plurality of modal test areas based on the coherence principle of the excitation-response data, and obtaining a test area division result;

[0035] Further, step S200 includes step S210 to step S230.

[0036] Step S210: Perform preliminary division of the area range according to the size information of the target turnout area in the first information and the length of the connection line of the test equipment, by limiting the distance between the connection point of the test equipment and the farthest excitation point of the target area to the reachable range of the equipment, and using the geometric segmentation method, divide the turnout area into a number of preliminary test intervals along the longitudinal direction of the rail to obtain a preliminary test range;

[0037] Step S220: Calculate the arrangement of excitation points and response points according to the preliminary test range, calculate the spatial distribution relationship between each excitation point and the corresponding response point, and screen out the area with a coherence coefficient greater than a preset threshold, so as to obtain the effective excitation point and response point distribution of each rail;

[0038] Step S230: perform partition numbering processing according to the distribution of effective excitation points and response points, assign a unique identification number to each modal test area, and establish a mapping relationship between the area number and the excitation point-response point to generate a test area division result.

[0039] It should be noted that due to the limited length of the connecting line of the test equipment and the obvious attenuation of the rail vibration during the longitudinal propagation process, it is impossible to obtain effective response signals in areas far away from the excitation point. Therefore, the effective test range in actual on-site measurements is usually limited and it is difficult to cover the entire turnout area. To solve this problem, combined with the length of the connecting line of the test equipment and the propagation characteristics of the rail vibration, a regional division method based on the coherence of the excitation-response data is adopted. The coherence coefficient (such as ≥0.8) is used as the threshold for evaluating the signal quality to determine the actual available measurement range. On this basis, according to the geometric structure characteristics of the turnout, the rail is divided into multiple modal test areas, such as Figure 2 As shown in the figure, the straight basic rail, curved tip rail, curved guide rail, wing rail, long center rail and short center rail are divided into different test areas, and different test ranges are marked with different colors in the figure. Furthermore, for the straight basic rail part, it is divided equally according to the equipment coverage range; for complex structures such as curved tip rails and curved guide rails, the area division is refined in combination with the curvature change to ensure that the test covers the key parts. Common measuring points are set between adjacent areas for subsequent normalization and splicing operations of modal parameters. Ultimately, through this division method, it is possible to achieve complete modal testing of complex turnout structures under the constraints of equipment and signal conditions, providing high-quality test data support for subsequent modal parameter identification.

[0040] Step S300: performing excitation and response measurements on each test area in turn according to the test area division result and the preset partitioned MRIT modal test scheme, and retaining common measurement points between adjacent test areas to obtain excitation-response data;

[0041] Further, step S300 includes step S310 to step S330.

[0042] Step S310: Determine the measurement point layout in each test area according to the test area division result and the preset partitioned MRIT modal test plan, and obtain the measurement point layout plan by arranging multiple excitation points and response measurement points in each test area and ensuring that at least one common measurement point is shared between adjacent areas;

[0043] Step S320: Based on the measurement point layout plan, the excitation points in each test area are subjected to hammer excitation processing one by one, and data is collected through sensors to obtain excitation point time series data and response point time series data;

[0044] Step S330: Perform data integration processing according to the excitation point time series data and the response point time series data, and obtain the excitation-response data of each test area by recording the signal correspondence between all excitation points and response points as an excitation-response matrix.

[0045] It should be noted that the partitioned MRIT modal test scheme is an efficient modal test method for complex structures such as turnout systems, which aims to address the shortcomings of traditional testing methods in terms of coverage, data integrity and test efficiency. This method is based on the divided test areas, and each area is used as an independent test unit. Through the multi-reference point impact test (MRIT) technology, the modal characteristics in each test area are fully stimulated and measured. Specifically, multiple acceleration sensors are arranged in each test area, and the positions of the sensors need to avoid modal nodes to ensure that the complete vibration characteristics of the structure can be captured. During the test, all excitation points in the test area are stimulated in turn by a force hammer, and the response signals of the sensors are recorded synchronously to form a frequency response function matrix of the test area. Each element of the matrix represents the frequency response function between the excitation point and the response point, which can reflect the dynamic characteristics of the area. In order to ensure the continuity and integrity of the test data, at least one common measuring point is shared between adjacent test areas. Such as Figure 3 As shown in the figure, [H(ω)] is the frequency response function matrix of the entire rail, 1, a, c, g, h, j, l, m represent the area number, and each element in the matrix is ​​the frequency response function between each excitation point and the response measurement point, such as H ij (ω) represents the frequency response function of the response measured at point j when the excitation is performed at point i. Each measurement area in the figure represents the frequency response function matrix of each test area of ​​the rail. For example, measurement area I in the figure indicates that acceleration sensors are placed at points 1, a, and c of the measurement rail, and the hammer is excited from point 1 to point c in sequence. The frequency response function of each excitation data and the response data of the three sensors is obtained. During the test, a common measurement point is reserved between adjacent areas. For example, the last measurement point of measurement area I is point c, and the first measurement point of measurement area II is also point c. Through this design, not only can the seamless splicing of modal data between areas be achieved, but also a benchmark is provided for the subsequent normalization of modal parameters. The advantage of multi-reference point hammer test is that it can effectively avoid the situation where a single sensor misses the signal due to being at a modal node, and improve the coverage and reliability of modal test. In addition, this method can also establish the frequency response function matrix of each test area through multiple excitations and multi-point measurements, laying the foundation for modal parameter identification. Compared with the traditional single-point excitation method, the partitioned MRIT scheme is more suitable for the modal testing needs of complex multi-component structures such as turnouts. It can accurately capture the dynamic characteristics of the system and support subsequent modal parameter identification and splicing, providing high-quality basic data for health monitoring and optimal design of turnout systems.

[0046] Step S400, performing matrix construction processing according to the excitation-response data, and calculating the modal parameters of each test area to obtain a modal parameter stability diagram of each test area;

[0047] It should be noted that this step is based on the excitation-response data, and performs matrix construction, modal parameter extraction and stability analysis in sequence. The modal parameter stability diagram systematically displays the modal characteristics of each test area, provides accurate data support for modal identification, and lays the foundation for the unified splicing of the overall modal parameters of the turnout system.

[0048] Further, step S400 includes step S410 to step S440.

[0049] Step S410, performing frequency domain analysis processing according to the excitation-response data, and constructing a frequency response function matrix of each test area by processing the signal between the excitation point and the response measurement point;

[0050] Step S420, performing data conversion processing according to the frequency response function matrix, converting the frequency domain data into time domain data by adopting an inverse transformation algorithm, and generating a corresponding impulse response function matrix;

[0051] Step S430: construct a generalized Hankel matrix of the test area using a system identification algorithm according to the impulse response function matrix, and extract modal parameters of the generalized Hankel matrix by matrix decomposition to obtain preliminary modal parameter data;

[0052] Step S440: Perform stability analysis based on the preliminary modal parameter data, and construct a modal parameter stability diagram by calculating the stability of each modal parameter under different Hankel matrix orders.

[0053] Specifically, the excitation and response data of all measuring points are divided and sorted according to the test area, and the frequency response function matrix [H(ω)] of each test area is obtained based on the frequency response function H1 estimation formula (1).

[0054]

[0055] Where H1(ω) is the frequency response function of region 1; S fx (ω) is the cross power spectrum density between the excitation data and the response data; S ff (ω) is the auto-power spectral density of the excitation data.

[0056] Perform inverse Fourier transform on all frequency response functions, as shown in formula (2), and obtain the impulse response function matrix of each test area.

[0057]

[0058] Where x[n] is the nth sample of the time domain signal; X[k] is the kth sample of the frequency domain signal; N is the number of signal points; and i is the imaginary unit.

[0059] The generalized Hankel matrix of each test area is constructed based on the impulse response function matrix of each test area, and the singular value decomposition formula (3) is performed on each generalized Hankel matrix.

[0060]

[0061] In the formula, is the generalized Hankel matrix; [U] is the left singular vector matrix; [∑] is the singular value matrix; [V] is the right singular vector matrix.

[0062] Based on the basic formula (4) of the ERA method, the minimum system implementation of each test area of ​​the turnout system is obtained [[A1], [B1], [G]]

[0063]

[0064] Where [A1] is the system matrix; [B1] is the control matrix; [G] is the observation matrix; [E L ]、[E N ] is the construction matrix, [E N ] t =[[I N ][0 N ]…[0 N ]],[E L ] T =[[I L ][0 L ]…[0 L ]].

[0065] Perform eigenvalue decomposition (5) on the system matrix [A1] of each test area to obtain the eigenvalue matrix [Z] and eigenvector [Ψ] of each test area.

[0066] [A1][Ψ]=[Ψ][Z] (5)

[0067] Based on the relationship between the system matrix eigenvalue and the system eigenvalue (6), the system matrix eigenvalue [Λ] of each test area is obtained, and further based on the relationship between the system eigenvalue and the modal parameter (7), the modal frequency ω of each test area is obtained. i and modal damping ratio ζ i

[0068]

[0069]

[0070] In the formula, σ i is the attenuation coefficient of the i-th order mode; is the eigenvalue λ of the i-th order system i The real part of ; Δt is the data sampling time interval; is the eigenvalue λ of the i-th order system i The imaginary part of di is the damped natural frequency of the i-th mode.

[0071] Based on the relationship between the modal vibration matrix and the system eigenvector matrix (8), the modal vibration matrix of each test area [Φ] is obtained.

[0072] [Φ]=[G][Ψ] (8)

[0073] Repeat the above calculation process to obtain the modal parameters of each test area under different Hankel matrix orders, and calculate the stability of each modal parameter under different Hankel matrix orders to obtain the modal parameter stability diagram of each test area. The diagram intuitively shows the distribution of parameters such as modal frequency and damping ratio under different orders.

[0074] Step S500, clustering is performed according to the modal parameter stability diagram, and the clustering result is obtained by removing the interference of false modes and modal clusters and screening the stable modal parameters;

[0075] It should be noted that traditional modal analysis is to manually determine the stable axis from the stability diagram to obtain the most stable stable modal parameters of each order to distinguish between true modes and false modes. This method has low analysis efficiency and is prone to introduce subjective errors. In addition, in the stability diagram, there will be modal clusters with different frequencies near the true mode, but with basically the same damping ratio and vibration mode, which will interfere with the selection of stable modes and affect the accuracy of modal parameter identification results.

[0076] Further, step S500 includes step S510 to step S540.

[0077] Step S510, performing parameter initialization processing according to the modal parameter stability diagram, and obtaining the initialization parameter configuration by analyzing the density of the modal points in the diagram, the initialization parameter configuration including the field radius parameter and the minimum point number parameter;

[0078] Step S520: traverse all data points in the modal parameter stability diagram based on the initialization parameter configuration, calculate the number of points in the neighborhood of each data point, and mark the points with a neighborhood point number greater than or equal to the number as core points, and mark the points with insufficient neighborhood points but connected to the core points as boundary points, to obtain a core point set and a boundary point set;

[0079] Step S530: Perform cluster expansion processing according to the core point set and the boundary point set, by adding all directly density-reachable points in the core point and its neighborhood into the same cluster, and recursively processing other core points density-connected to the current core point, to obtain a preliminary clustering result;

[0080] Step S540: perform interference removal processing according to the preliminary clustering results, by analyzing the central characteristics and distribution density of each cluster, screening clusters that meet the modal frequency, damping ratio and vibration mode consistency, and removing isolated false modal points and interference modal clusters to obtain the final clustering results.

[0081] Specifically, this method determines the most stable mode based on the proportion of the total stable times of each order mode in the stability diagram to the total calculation times. At the same time, based on the characteristics of the appearance of modal clusters near the real mode, a clustering algorithm is used to automatically analyze and identify the stability diagram data. There are many clustering algorithms. The commonly used K-Means and other clustering algorithms require the number of clusters K to be given in advance, but in actual modal identification, no prior information such as the modal order of the structure is known, so it is not applicable. This method uses a density-based spatial clustering algorithm (DBSCAN). The algorithm process is as follows: Figure 4 As shown in the figure, d(p,q) represents the Euclidean distance between data points p and q in multidimensional space; i represents the dimension number of the data point; n represents the number of features of the data point; p i represents the characteristic value of p data point in dimension i; q i Represents the eigenvalue of the q data point in the i dimension. The core idea of ​​this algorithm is to cluster according to the density around the data points, rather than assuming the number of clusters in advance like traditional clustering algorithms. It is very suitable for analyzing stable graph data that only appear in clusters near the true mode, but the number of clusters is unknown.

[0082] Step S600: Based on the clustering results, the modal vibration modes in the adjacent areas with frequencies within a preset range are normalized with the common measuring points as the reference, and the modal parameter identification results are obtained by splicing. The modal parameter identification results are the modal parameter distribution of the turnout system as a whole.

[0083] Further, step S600 includes step S610 to step S630.

[0084] Step S610: performing normalization processing based on the clustering results, by normalizing the modal vibration coefficients of the common reference measuring points to 1, thereby obtaining normalized modal vibration data;

[0085] It can be understood that by normalizing the modal vibration coefficients of the common measuring points to 1, it is ensured that the modal vibration characteristics of each region at the common measuring point remain consistent when splicing across regions, eliminating the modal vibration scale differences caused by regional division. The normalized modal vibration data provides standardized input for subsequent modal matching and splicing.

[0086] Step S620, performing matching processing according to the normalized modal vibration shape data, establishing a cross-region correspondence relationship of the modal vibration shape by comparing and matching the modal vibration shape point by point, and marking each pair of successfully matched modal vibration shape pairs to obtain the modal vibration shape matching result;

[0087] Specifically, firstly, the modal vibration shapes of the modal frequencies in each region within the preset range are compared to select the modes with similar frequencies; then, based on these modal vibration shapes with similar frequencies, point-by-point comparison is performed within the range of all measuring points to establish the cross-regional modal correspondence. During the matching process, it is necessary to ensure the continuity and consistency of the modal vibration shapes at the common measuring points, and mark the successfully matched modal vibration shape pairs to form the modal vibration shape matching results.

[0088] Step S630: perform splicing processing according to the modal vibration shape matching results, and splice each pair of matching modal vibration shapes according to the consistency of common measurement points by traversing each region one by one to obtain the final modal parameter identification result.

[0089] It should be noted that the matching modal vibration shapes are unified and integrated based on the common measuring points during splicing to ensure that the modal parameters after splicing have consistent frequency, vibration shape and damping characteristics in adjacent test areas. By gradually splicing the modal parameters of adjacent areas, the complete modal parameter distribution of the turnout system is generated, as shown in the following figure. Figure 5 The modal parameter identification results of the turnout area are shown in Figure 1. This distribution includes the modal characteristics of all turnout components (such as straight base rails, curved point rails, curved guide rails, etc.), providing complete data support for the overall dynamic analysis and health monitoring of the turnout system.

[0090] Embodiment 2:

[0091] This embodiment provides a system for automatically identifying modal parameters of a turnout area, the system comprising:

[0092] An acquisition module, used for acquiring first information, the first information including size information of a target turnout area and a connection line length of a test device;

[0093] A division module is used to perform a region division process on the target turnout area according to the first information, and divide each turnout rail into multiple modal test areas based on the coherence principle of the excitation-response data to obtain a test area division result;

[0094] The test module is used to perform excitation and response measurements on each test area in turn according to the test area division results and the preset partitioned MRIT modal test plan, and retain common measurement points between adjacent test areas to obtain excitation-response data;

[0095] A construction module is used to perform matrix construction processing according to the excitation-response data, and calculate the modal parameters of each test area to obtain the modal parameter stability diagram of each test area;

[0096] The clustering module is used to perform clustering processing according to the modal parameter stability diagram, remove the interference of false modes and modal clusters, and screen the stable modal parameters to obtain the clustering results;

[0097] The output module, based on the clustering results, normalizes the modal vibration shapes in the adjacent areas with frequencies within the preset range with the common measuring points as the reference, and splices them to obtain the modal parameter identification results. The modal parameter identification results are the modal parameter distribution of the turnout system as a whole.

[0098] In a specific implementation disclosed in the present application, the partitioning module includes:

[0099] The first division unit is used to perform preliminary division processing of the area range according to the size information of the target turnout area in the first information and the length of the connection line of the test equipment, by limiting the distance between the connection point of the test equipment and the farthest excitation point of the target area to the reachable range of the equipment, and using the geometric segmentation method to divide the turnout area into a plurality of preliminary test intervals along the longitudinal direction of the rail to obtain the preliminary test range;

[0100] The first calculation unit is used to calculate the arrangement of the excitation points and the response points according to the preliminary test range, calculate the spatial distribution relationship between each excitation point and the corresponding response point, and screen out the area range with a coherence coefficient greater than a preset threshold, so as to obtain the effective excitation point and response point distribution of each rail;

[0101] The first numbering unit is used to perform partition numbering according to the distribution of effective excitation points and response points, and generate a test area division result by assigning a unique identification number to each modal test area and establishing a mapping relationship between the area number and the excitation point-response point.

[0102] In a specific embodiment disclosed in the present application, the test module includes:

[0103] A first processing unit is used to determine the measurement point layout in each test area according to the test area division result and the preset partitioned MRIT modal test plan, and obtain the measurement point layout plan by arranging multiple excitation points and response measurement points in each test area and ensuring that at least one common measurement point is shared between adjacent areas;

[0104] The second processing unit performs hammer excitation processing on each excitation point in each test area one by one based on the measurement point layout plan, and collects data through sensors to obtain time series data of the excitation point and time series data of the response point;

[0105] The first integration unit is used to perform data integration processing according to the excitation point time series data and the response point time series data, and obtain the excitation-response data of each test area by recording the signal correspondence between all excitation points and response points as an excitation-response matrix.

[0106] In a specific embodiment disclosed in the present application, the building blocks include:

[0107] The first analysis unit is used to perform frequency domain analysis processing according to the excitation-response data, and construct a frequency response function matrix of each test area by processing the signal between the excitation point and the response measurement point;

[0108] The first conversion unit is used to perform data conversion processing according to the frequency response function matrix, and convert the frequency domain data into time domain data by adopting an inverse transformation algorithm to generate a corresponding impulse response function matrix;

[0109] The first construction unit is used to construct a generalized Hankel matrix of the test area according to the impulse response function matrix using a system identification algorithm, and extract the modal parameters of the generalized Hankel matrix through matrix decomposition to obtain preliminary modal parameter data;

[0110] The second calculation unit is used to perform stability analysis based on the preliminary modal parameter data, and to construct a modal parameter stability diagram by calculating the stability of each modal parameter under different Hankel matrix orders.

[0111] In a specific implementation disclosed in the present application, the clustering module includes:

[0112] A third processing unit is used to perform parameter initialization processing according to the modal parameter stability diagram, and obtain the initialization parameter configuration by analyzing the density of the modal points in the diagram, and the initialization parameter configuration includes a field radius parameter and a minimum point number parameter;

[0113] The third calculation unit traverses all data points in the modal parameter stability diagram based on the initialization parameter configuration, calculates the number of points in the neighborhood of each data point, and marks the points with a number of neighborhood points greater than or equal to the number as core points, and marks the points with insufficient number of neighborhood points but connected to the core points as boundary points, thereby obtaining a core point set and a boundary point set;

[0114] The fourth processing unit is used to perform cluster expansion processing according to the core point set and the boundary point set, by adding all directly density-reachable points in the core point and its neighborhood into the same cluster cluster, and recursively processing other core points density-connected with the current core point, to obtain a preliminary clustering result;

[0115] The fifth processing unit is used to perform interference removal processing based on the preliminary clustering results. By analyzing the central characteristics and distribution density of each cluster, the clusters that meet the modal frequency, damping ratio and vibration type consistency are screened, and isolated false modal points and interference modal clusters are removed to obtain the final clustering results.

[0116] In a specific implementation disclosed in the present application, the output module includes:

[0117] A sixth processing unit performs normalization processing based on the clustering result, and obtains normalized modal vibration shape data by normalizing the modal vibration shape coefficients of the common reference measuring points to 1;

[0118] The first matching unit is used to perform matching processing according to the normalized modal vibration shape data, establish a cross-region correspondence relationship of the modal vibration shape by comparing and matching the modal vibration shape point by point, and mark each pair of successfully matched modal vibration shape pairs to obtain the modal vibration shape matching result;

[0119] The seventh processing unit is used to perform splicing processing according to the modal vibration shape matching result, and splice each pair of matching modal vibration shapes according to the consistency of the common measuring points by traversing the area by area, so as to obtain the final modal parameter identification result.

[0120] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for automatically identifying modal parameters of a turnout area, characterized in that: include: Acquiring first information, wherein the first information includes size information of a target turnout area and a length of a connection line of a test device; Performing a region division process on the target turnout area according to the first information, dividing each turnout rail into a plurality of modal test areas based on the coherence principle of the excitation-response data, and obtaining a test area division result; According to the test area division result and the preset partitioned MRIT modal test scheme, each test area is sequentially excited and responded to, and common measurement points are reserved between adjacent test areas to obtain excitation-response data; Performing matrix construction processing according to the excitation-response data, and calculating the modal parameters of each test area to obtain a modal parameter stability diagram of each test area; Performing clustering processing according to the modal parameter stability diagram, removing false modes and modal cluster interference, and screening stable modal parameters to obtain clustering results; Based on the clustering results, the modal vibration shapes in the adjacent areas with frequencies within a preset range are normalized with the common measuring points as the reference, and the modal parameter identification results are obtained by splicing. The modal parameter identification results are the modal parameter distribution of the turnout system as a whole.

2. The method for automatically identifying modal parameters of a turnout area according to claim 1, characterized in that: According to the test area division result and the preset partitioned MRIT modal test scheme, each test area is excited and responded to in turn, and common measurement points are reserved between adjacent test areas to obtain excitation-response data, including: Determine the measurement point layout in each test area according to the test area division result and the preset partitioned MRIT modal test plan, and obtain the measurement point layout plan by arranging multiple excitation points and response measurement points in each test area while ensuring that at least one common measurement point is shared between adjacent areas; Based on the measurement point layout plan, the excitation points in each test area are subjected to hammer excitation treatment one by one, and data is collected through sensors to obtain the time series data of the excitation points and the response points; Data integration processing is performed according to the excitation point time series data and the response point time series data, and the excitation-response data of each test area are obtained by recording the signal correspondence between all excitation points and response points as an excitation-response matrix.

3. The method for automatically identifying modal parameters of a turnout area according to claim 1, characterized in that: Matrix construction is performed according to the excitation-response data, and the modal parameters of each test area are calculated to obtain a modal parameter stability diagram of each test area, including: Perform frequency domain analysis on the excitation-response data, and construct a frequency response function matrix for each test area by processing the signal between the excitation point and the response measurement point; Performing data conversion processing according to the frequency response function matrix, converting the frequency domain data into time domain data by adopting an inverse transformation algorithm, and generating a corresponding impulse response function matrix; According to the impulse response function matrix, a generalized Hankel matrix of the test area is constructed using a system identification algorithm, and modal parameters of the generalized Hankel matrix are extracted by matrix decomposition to obtain preliminary modal parameter data; A stability analysis is performed based on the preliminary modal parameter data, and a modal parameter stability diagram is constructed by calculating the stability of each order of modal parameters under different Hankel matrix orders.

4. The method for automatically identifying modal parameters of a turnout area according to claim 1, characterized in that: Clustering is performed according to the modal parameter stability diagram, by removing false modes and modal cluster interference and screening stable modal parameters to obtain clustering results, including: Performing parameter initialization processing according to the modal parameter stability graph, and obtaining an initialization parameter configuration by analyzing the density of modal points in the graph, wherein the initialization parameter configuration includes a domain radius parameter and a minimum point number parameter; Based on the initialization parameter configuration, all data points in the modal parameter stability diagram are traversed, the number of points in the neighborhood of each data point is calculated, and points with a number of neighborhood points greater than or equal to the number of points are marked as core points, and points with insufficient number of points in the neighborhood but connected to the core points are marked as boundary points, thereby obtaining a core point set and a boundary point set; Performing cluster expansion processing according to the core point set and the boundary point set, adding all directly density-reachable points within the core point and its neighborhood into the same cluster, and recursively processing other core points density-connected to the current core point, to obtain a preliminary clustering result; Interference removal processing is performed according to the preliminary clustering results. By analyzing the central characteristics and distribution density of each cluster, clusters that meet the modal frequency, damping ratio and vibration mode consistency are screened, and isolated false modal points and interference modal clusters are removed to obtain the final clustering results.

5. The method for automatically identifying modal parameters of a turnout area according to claim 1, characterized in that: Based on the clustering results, the modal vibration shapes in the adjacent areas with frequencies within the preset range are normalized with the common measuring points as the reference, and the modal parameter identification results are obtained by splicing. The modal parameter identification results are the modal parameter distribution of the turnout system as a whole, including: Performing normalization processing based on the clustering results, by normalizing the modal vibration coefficients of the common reference measuring points to 1, thereby obtaining normalized modal vibration data; Performing matching processing according to the normalized modal vibration shape data, establishing a cross-regional correspondence relationship of the modal vibration shapes by comparing and matching the modal vibration shapes point by point, and marking each pair of successfully matched modal vibration shape pairs to obtain a modal vibration shape matching result; The modal vibration shape matching results are spliced ​​according to the above-mentioned splicing processing. By traversing each region one by one, each pair of matching modal vibration shapes is spliced ​​according to the consistency of the common measuring points to obtain the final modal parameter identification result.

6. A switch area modal parameter automatic identification system, characterized in that: include: An acquisition module, used for acquiring first information, wherein the first information includes size information of a target turnout area and a connection line length of a test device; A division module, used for performing a region division process on the target turnout area according to the first information, dividing each turnout rail into a plurality of modal test areas based on the coherence principle of the excitation-response data, and obtaining a test area division result; A test module, used to perform excitation and response measurements on each test area in turn according to the test area division result and a preset partitioned MRIT modal test scheme, and retain common measurement points between adjacent test areas to obtain excitation-response data; A construction module, used to perform matrix construction processing according to the excitation-response data, and calculate the modal parameters of each test area to obtain a modal parameter stability diagram of each test area; A clustering module, used for performing clustering processing according to the modal parameter stability diagram, removing false modes and modal cluster interference, and screening stable modal parameters to obtain clustering results; The output module, based on the clustering results, normalizes the modal vibration shapes in the adjacent areas with frequencies within a preset range with reference to the common measuring points, and splices them to obtain the modal parameter identification results, which are the modal parameter distribution of the turnout system as a whole.

7. The switch area modal parameter automatic identification system according to claim 6 is characterized in that: The test module includes: A first processing unit is used to determine the measurement point layout in each test area according to the test area division result and the preset partitioned MRIT modal test plan, and obtain the measurement point layout plan by arranging multiple excitation points and response measurement points in each test area and ensuring that at least one common measurement point is shared between adjacent areas; The second processing unit performs hammer excitation processing on each excitation point in each test area one by one based on the measurement point layout plan, and collects data through sensors to obtain time series data of the excitation point and time series data of the response point; The first integration unit is used to perform data integration processing according to the excitation point time series data and the response point time series data, and obtain the excitation-response data of each test area by recording the signal correspondence between all excitation points and response points as an excitation-response matrix.

8. The switch area modal parameter automatic identification system according to claim 6, characterized in that: The building blocks include: A first analysis unit is used to perform frequency domain analysis processing according to the excitation-response data, and construct a frequency response function matrix of each test area by processing the signal between the excitation point and the response measurement point; A first conversion unit is used to perform data conversion processing according to the frequency response function matrix, convert the frequency domain data into time domain data by adopting an inverse transformation algorithm, and generate a corresponding impulse response function matrix; A first construction unit is used to construct a generalized Hankel matrix of the test area according to the impulse response function matrix using a system identification algorithm, and extract modal parameters of the generalized Hankel matrix by matrix decomposition to obtain preliminary modal parameter data; The second calculation unit is used to perform stability analysis based on the preliminary modal parameter data, and construct a modal parameter stability diagram by calculating the stability of each order of modal parameters under different Hankel matrix orders.

9. The switch area modal parameter automatic identification system according to claim 6, characterized in that: The clustering module comprises: A third processing unit is used to perform parameter initialization processing according to the modal parameter stability diagram, and obtain an initialization parameter configuration by analyzing the density of the modal points in the diagram, wherein the initialization parameter configuration includes a field radius parameter and a minimum point number parameter; A third calculation unit, based on the initialization parameter configuration, traverses all data points in the modal parameter stability diagram, calculates the number of points in the neighborhood of each data point, and marks points with a number of neighborhood points greater than or equal to the number as core points, and marks points with insufficient number of neighborhood points but connected to the core points as boundary points, to obtain a core point set and a boundary point set; A fourth processing unit is used to perform cluster expansion processing according to the core point set and the boundary point set, by adding all directly density-reachable points in the core point and its neighborhood to the same cluster cluster, and recursively processing other core points density-connected to the current core point, to obtain a preliminary clustering result; The fifth processing unit is used to perform interference removal processing according to the preliminary clustering results, and screen the clusters that meet the modal frequency, damping ratio and vibration type consistency by analyzing the central characteristics and distribution density of each cluster cluster, and remove the isolated false modal points and interference modal clusters to obtain the final clustering results.

10. The switch area modal parameter automatic identification system according to claim 6, characterized in that: The output module comprises: A sixth processing unit, performing normalization processing based on the clustering result, by normalizing the modal vibration coefficients of the common reference measuring points to 1, thereby obtaining normalized modal vibration data; A first matching unit is used to perform matching processing according to the normalized modal vibration shape data, establish a cross-region correspondence relationship of the modal vibration shape by comparing and matching the modal vibration shape point by point, and mark each pair of successfully matched modal vibration shape pairs to obtain a modal vibration shape matching result; The seventh processing unit is used to perform splicing processing according to the modal vibration shape matching result, and splice each pair of matching modal vibration shapes according to the consistency of common measuring points by traversing area by area to obtain the final modal parameter identification result.

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