Turnout point rail guided wave signal reconstruction method and system based on three-dimensional dispersion characteristics

Abstract

The application provides a kind of switch blade guide wave signal reconstruction method and system based on three-dimensional dispersion characteristics, it is related to switch detection technical field, along switch blade arrangement includes calculation sensor and verification sensor sensor array;Establish switch blade finite element model, construct wave number-frequency-distance three-dimensional dispersion matrix by characteristic frequency method;Based on guide wave excitation center frequency, extract wave number data from matrix to establish theoretical frequency distribution matrix;Acquire calculation sensor time domain signal and obtain actual frequency distribution vector by Fourier transform;Establish and solve linear equation of modal waveform amplitude coefficient, use verification sensor signal to carry out tolerance verification and iterative optimization;Based on the optimized coefficient, reconstruct guide wave signal at any target distance by waveform transfer equation.The application overcomes the limitations of traditional methods in variable cross-section switch blade, realizes high-precision reconstruction of guide wave propagation process.

Description

Technical Field

[0001] This invention relates to the field of turnout detection technology, and more specifically, to a method and system for reconstructing turnout tip rail guided wave signals based on three-dimensional dispersion characteristics. Background Technology

[0002] As a key variable cross-section component in the track system, the switch rail's complex geometry leads to significant three-dimensional dispersion characteristics in its guided wave propagation. Currently widely used guided wave signal reconstruction methods are based on the theory of constant cross-section waveguides, achieving signal reconstruction through linear interpolation. While this method performs well in the base rail with a uniform cross-section, it produces severe waveform distortion in the variable cross-section switch rail section. More importantly, in engineering practice, limitations in sensor deployment costs and installation conditions restrict the number of sensors that can be placed on the switch rail surface. This further reduces the accuracy of signal reconstruction based on traditional methods, making it difficult to meet the requirements for accurate identification and location of switch rail defects, severely hindering the practical application of switch inspection. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for reconstructing turnout tip rail guided wave signals based on three-dimensional dispersion characteristics, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows: In a first aspect, this application provides a method for reconstructing turnout tip rail guided wave signals based on three-dimensional dispersion characteristics, including: Calculation and verification sensors are designed and installed longitudinally along the turnout switch rail; A finite element model of the turnout switch rail is established, and a three-dimensional dispersion matrix of the turnout switch rail based on the finite element model is constructed using the wavenumber-frequency-distance method. Based on the preset guided wave excitation center frequency and the calculated sensor position information, wavenumber data is extracted from the three-dimensional dispersion matrix to establish a theoretical frequency distribution matrix; The time-domain signal collected by the computational sensor is acquired and transformed to obtain the actual frequency distribution vector; Based on the theoretical frequency distribution matrix and the actual frequency distribution vector, the modal waveform amplitude coefficients are solved, and the modal waveform amplitude coefficients are verified and optimized using the time-domain signal collected by the verification sensor to obtain the optimized modal waveform amplitude coefficients. Based on the optimized modal waveform amplitude coefficients and the wave number data extracted from the three-dimensional dispersion matrix, the guided wave signal at any target distance of the turnout switch rail is calculated and reconstructed through wave propagation.

[0004] Secondly, this application also provides a turnout tip rail guided wave signal reconstruction system based on three-dimensional dispersion characteristics, comprising: The sensor deployment module is used to deploy and verify sensors along the longitudinal direction of the turnout switch rail. The dispersion characteristic calculation module is used to establish a finite element model of the turnout switch rail and construct a three-dimensional dispersion matrix of the turnout switch rail based on the finite element model. The theoretical modeling module is used to extract wavenumber data from the three-dimensional dispersion matrix to establish a theoretical frequency distribution matrix based on the preset guided wave excitation center frequency and the sensor position information. The signal acquisition and processing module is used to acquire and transform the time-domain signal collected by the calculation sensor to obtain the actual frequency distribution vector; The parameter solving and optimization module is used to solve the modal waveform amplitude coefficients based on the theoretical frequency distribution matrix and the actual frequency distribution vector, and to verify and optimize the modal waveform amplitude coefficients using the time-domain signal collected by the verification sensor, so as to obtain the optimized modal waveform amplitude coefficients. The signal reconstruction module is used to calculate and reconstruct the guided wave signal at any target distance of the turnout switch rail based on the optimized modal waveform amplitude coefficients and wave number data extracted from the three-dimensional dispersion matrix.

[0005] Thirdly, this application also provides a turnout switch point rail guided wave signal reconstruction device based on three-dimensional dispersion characteristics, comprising: Memory, used to store computer programs; A processor is used to implement the steps of the turnout switch rail guided wave signal reconstruction method based on three-dimensional dispersion characteristics when executing the computer program.

[0006] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for reconstructing turnout switch rail guided wave signals based on three-dimensional dispersion characteristics.

[0007] The beneficial effects of this invention are as follows: This invention employs a wavenumber-frequency-distance three-dimensional matrix constructed using the characteristic frequency method, which accurately reflects the propagation characteristics of guided waves at various cross-sections of the switch rail. By introducing a sparse optimization-based algorithm for solving the modal waveform amplitude coefficients and combining iterative optimization mechanisms for verification sensors, the accuracy and stability of signal reconstruction are effectively improved. Compared with existing technologies, this invention not only solves the problems of poor adaptability and low reconstruction accuracy of traditional methods in variable cross-section structures, but also significantly reduces the requirements for sensor deployment density while ensuring reconstruction quality, providing reliable technical support for full-length, full-section health monitoring of turnout switch rails.

[0008] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic flowchart of the turnout tip rail guided wave signal reconstruction method based on three-dimensional dispersion characteristics as described in this embodiment of the invention. Figure 1 ; Figure 2 This is a schematic flowchart of the turnout tip rail guided wave signal reconstruction method based on three-dimensional dispersion characteristics as described in this embodiment of the invention. Figure 2 ; Figure 3 This is a schematic diagram of the turnout tip rail guided wave signal reconstruction system based on three-dimensional dispersion characteristics as described in an embodiment of the present invention; Figure 4 This is a schematic diagram of the turnout tip rail guided wave signal reconstruction device based on three-dimensional dispersion characteristics as described in an embodiment of the present invention.

[0011] Marked in the image: 800. Switch tip guide wave signal reconstruction device based on three-dimensional dispersion characteristics; 801. Processor; 802. Memory; 803. Multimedia component; 804. I / O interface; 805. Communication component. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0013] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0014] Example 1: This embodiment provides a method for reconstructing turnout tip rail guided wave signals based on three-dimensional dispersion characteristics.

[0015] See Figure 1 , Figure 2 The figure shows that this method includes: S1. Arrange and verify sensors along the longitudinal direction of the turnout switch rail; Specifically, including One computational sensor and one verification sensor, wherein the verification sensor is located behind the computational sensor, i.e., in front of it. The first point is a calculation sensor, the second... These points are for verifying the sensors.

[0016] Based on the above embodiments, this method further includes: S2. Establish a finite element model of the turnout switch rail, and construct a three-dimensional dispersion matrix of the turnout switch rail based on the finite element model; Specifically, step S2 includes: S21. Obtain the geometric dimensions and alignment parameters of the turnout switch rail to establish a finite element model of the turnout switch rail; Specifically, in finite element software, a finite element model of the turnout switch rail is established based on its geometric dimensions and alignment parameters. A planar mesh is created for the key cross-sections of the switch rail from the mid-span portion, and then the switch rail body mesh is formed by lofting and stretching towards both ends. The mesh division should be approximately uniform to ensure that the mesh size remains approximately consistent across all parts of the switch rail.

[0017] S22. Set a frequency range in the finite element model, and use the characteristic frequency method to obtain the structural vibration mode of the turnout switch rail at several discrete frequencies within the frequency range; In this embodiment, the frequency scanning range is set in the finite element software. The characteristic frequency method is used to solve for several discrete frequency points within the frequency range. The structural vibration mode of the switch point rail.

[0018] S23. Displacement calculation nodes are arranged longitudinally at the web position of the turnout switch rail to obtain mode displacement data; S24. Based on the modal displacement data, plot the distance-displacement curves corresponding to each structural mode at each discrete frequency; Specifically, for each frequency Each mode below A distance-displacement curve is plotted, which reflects the variation law of the mode displacement along the longitudinal direction of the turnout switch rail. The curve approximates a sine wave with a continuously changing period.

[0019] S25. Based on the distance-displacement curve, determine the wave value at each location, and construct a three-dimensional dispersion matrix of wave number-frequency-distance according to the correspondence between frequency, wave value and distance.

[0020] Specifically, for each distance-displacement curve, the period length Q at each position on the curve is measured, where the period length Q represents the guided wave wavelength at the corresponding position; The wavenumber at the corresponding position, frequency, and mode is calculated based on the conversion relationship between wavelength and wavenumber. The conversion relationship is as follows: ; In the formula, Indicates frequency Lower mode In distance wave number at that point In distance The length of the sine wave period at that location.

[0021] By calculating all frequencies All modes and all locations Construct a three-dimensional dispersion matrix of wavenumber, frequency, and distance at a given location: ; In the formula, Wavenumber-frequency-distance three-dimensional dispersion matrix.

[0022] Based on the above embodiments, this method further includes: S3. Based on the preset guided wave excitation center frequency and the calculated sensor position information, extract wavenumber data from the three-dimensional dispersion matrix to establish a theoretical frequency distribution matrix; Specifically, step S3 includes: S31. Extract the frequency at the center frequency from the three-dimensional dispersion matrix. All guided wave modes In each computing sensor Wave value at The guided wave modes are obtained by transforming the structural mode shapes; S32. Based on the wave values, calculate the phase change of each guided wave mode as it propagates from the guided wave exciter to each calculated sensor point, and obtain the theoretical frequency distribution matrix: ; In the formula, This represents the theoretical frequency distribution matrix, with dimension 1. Where s is the number of sensors to be calculated. This represents the total number of guided wave modes. Indicates the first The waveguide mode is at the _ in the _ ... Calculate sensor location Wave value at that location, It represents the imaginary unit.

[0023] Based on the above embodiments, this method further includes: S4. Acquire and transform the time-domain signal collected by the calculation sensor to obtain the actual frequency distribution vector; Specifically, step S4 includes: S41. After the waveguide exciter transmits a signal at the center frequency, the time-domain signal recorded by the calculation sensor is acquired; Specifically, in the waveguide exciter at the center frequency After the signal is transmitted, the time-domain signals recorded by all computational sensors are acquired synchronously. Let the time-domain signal acquired by the s-th computational sensor be... ,in, It is a time variable.

[0024] S42. Perform a Fourier transform on the time-domain signal acquired by each computing sensor to obtain the corresponding frequency-domain signal; S43. Extract the complex frequency components of the frequency domain signal at the center frequency, and arrange all the complex frequency components of the calculated sensors in the order of the sensors to form the actual frequency distribution vector.

[0025] ; In the formula, Represents the actual frequency distribution vector. Indicates the first The complex frequency components of the sensor are calculated. Indicates the center frequency. Represents the effective angular frequency vector. This represents the total duration of the signal.

[0026] Based on the above embodiments, this method further includes: S5. Based on the theoretical frequency distribution matrix and the actual frequency distribution vector, solve for the modal waveform amplitude coefficients, and use the time-domain signal collected by the verification sensor to verify and optimize the modal waveform amplitude coefficients to obtain the optimized modal waveform amplitude coefficients; Specifically, step S5 includes: S51. Based on the theoretical frequency distribution matrix and the actual frequency distribution vector, establish a linear equation regarding the modal waveform amplitude coefficients: ; In the formula, This is the theoretical frequency distribution matrix; Let be the amplitude coefficient of the modal waveform to be determined. The actual frequency distribution vector, Represents the number of non-zero elements, satisfying .

[0027] S52. Set an initial value for the number of non-zero elements in the modal waveform amplitude coefficients, and solve the linear equation using a sparse optimization algorithm to obtain the initial modal waveform amplitude coefficients. , The maximum number of contents in One non-zero element, and the rest of the elements are zero; Based on the above embodiments, this method further includes: S53. Based on the location of the verification sensor, calculate the theoretical frequency distribution vector corresponding to all guided wave modes at the center frequency: ; In the formula, Indicates the first Each guided wave mode at the center frequency The wavenumbers are extracted from the three-dimensional dispersion matrix. Indicates the location of the verification sensor At this point, the theoretical frequency distribution vector of all guided wave modes .

[0028] S54. Using the theoretical frequency distribution vector and the initial modal waveform amplitude coefficients, calculate the signal energy of the reconstructed signal at the sensor location. : ; S55. Determine whether the signal energy exceeds the preset tolerance. If so, adjust the initial value of the number of non-zero elements and resolve the linear equation until the error is less than the preset tolerance. Specifically, judgment If the condition is not met, increase the number of non-zero elements by one and resolve the linear equation until... .

[0029] Based on the above embodiments, this method further includes: S6. Based on the optimized modal waveform amplitude coefficients and the wave number data extracted from the three-dimensional dispersion matrix, the guided wave signal at any target distance of the turnout switch rail is calculated and reconstructed through wave propagation. Specifically, step S6 includes: S61. Extract all guided wave modes at the target distance from the three-dimensional dispersion matrix at the center frequency. Wave value at The target distance The coordinates are any selected position along the longitudinal direction of the turnout switch rail.

[0030] For each guided wave mode Calculate its distance from the target Time-domain response at: ; In the formula, Represents the time-domain response of mode 𝑚. This represents the amplitude coefficient of the optimized modal waveform. This represents the signal weighting coefficient, which is taken in this embodiment. , This indicates the number of cycles of a sinusoidal signal.

[0031] The target range is obtained by superimposing the time-domain responses of all guided wave modes. Complete reconstruction signal at: ; In the formula, The reconstructed signal represents the propagation state of the guided wave at the target distance of the turnout tip rail, and includes the amplitude, phase and dispersion characteristics of each guided wave mode.

[0032] By changing the target distance value and repeating the above calculation process, the propagation signal of the guided wave at different positions on the turnout switch rail can be obtained, realizing the full-domain reconstruction of the guided wave propagation process of the entire switch rail section, and providing a complete data foundation for subsequent defect detection and location.

[0033] Example 2: like Figure 3 As shown, this embodiment provides a turnout tip rail guided wave signal reconstruction system based on three-dimensional dispersion characteristics. The system includes: The sensor deployment module is used to deploy and verify sensors along the longitudinal direction of the turnout switch rail. The dispersion characteristic calculation module is used to establish a finite element model of the turnout switch rail and construct a three-dimensional dispersion matrix of the turnout switch rail based on the finite element model. The theoretical modeling module is used to extract wavenumber data from the three-dimensional dispersion matrix to establish a theoretical frequency distribution matrix based on the preset guided wave excitation center frequency and the sensor position information. The signal acquisition and processing module is used to acquire and transform the time-domain signal collected by the calculation sensor to obtain the actual frequency distribution vector; The parameter solving and optimization module is used to solve the modal waveform amplitude coefficients based on the theoretical frequency distribution matrix and the actual frequency distribution vector, and to verify and optimize the modal waveform amplitude coefficients using the time-domain signal collected by the verification sensor, so as to obtain the optimized modal waveform amplitude coefficients. The signal reconstruction module is used to calculate and reconstruct the guided wave signal at any target distance of the turnout switch rail based on the optimized modal waveform amplitude coefficients and wave number data extracted from the three-dimensional dispersion matrix.

[0034] Based on the above embodiments, the dispersion feature calculation module includes: Modeling units are used to obtain the geometric dimensions and alignment parameters of the turnout switch rails in order to establish a finite element model of the turnout switch rails. The feature analysis unit is used to set the frequency range in the finite element model and use the characteristic frequency method to obtain the structural vibration mode of the turnout switch rail at several discrete frequencies within the frequency range. The data extraction unit is used to arrange displacement calculation nodes longitudinally at the web position of the turnout switch rail in order to obtain mode displacement data. The curve plotting unit is used to plot the distance-displacement curves corresponding to each structural mode at each discrete frequency based on the mode displacement data. The matrix construction unit is used to determine the wave value at each position based on the distance-displacement curve, and construct a three-dimensional dispersion matrix of wave number-frequency-distance according to the correspondence between frequency, wave value and distance.

[0035] Based on the above embodiments, the theoretical modeling module includes: The data extraction unit is used to extract the wave values ​​of all guided wave modes at each computational sensor at the center frequency from the three-dimensional dispersion matrix, wherein the guided wave modes are obtained by structural mode shape conversion; The matrix calculation unit is used to calculate the phase change of each guided wave mode from the guided wave exciter to each calculation sensor point based on the wave value, and obtain the theoretical frequency distribution matrix.

[0036] Based on the above embodiments, the signal acquisition and processing module includes: The signal acquisition unit is used to acquire and calculate the time-domain signal recorded by the sensor after the waveguide exciter transmits a signal at the center frequency; The transformation unit is used to perform Fourier transform on the time-domain signal acquired by each computing sensor to obtain the corresponding frequency-domain signal; The vector construction unit is used to extract the complex frequency components of the frequency domain signal at the center frequency, and arrange the complex frequency components of all calculated sensors in the sensor order to form the actual frequency distribution vector.

[0037] Based on the above embodiments, the parameter solving and optimization module includes: The equation-establishing unit is used to establish a linear equation about the modal waveform amplitude coefficients based on the theoretical frequency distribution matrix and the actual frequency distribution vector. The sparse solver unit is used to set the initial value of the number of non-zero elements in the modal waveform amplitude coefficients and to solve the linear equation using a sparse optimization algorithm to obtain the initial modal waveform amplitude coefficients.

[0038] Based on the above embodiments, the parameter solving and optimization module further includes: The verification theory calculation unit is used to calculate the theoretical frequency distribution vector corresponding to all guided wave modes at the center frequency based on the position of the verification sensor. The signal energy calculation unit is used to calculate and verify the theoretical signal energy of the reconstructed signal at the sensor location using the theoretical frequency distribution vector and the initial modal waveform amplitude coefficient. A tolerance judgment unit is used to determine whether the theoretical signal energy exceeds a preset tolerance. An iterative control unit is used to adjust the initial value of the number of non-zero elements and trigger the sparse solution unit to resolve the linear equation when the theoretical signal energy exceeds the preset tolerance, until the theoretical signal energy meets the tolerance requirement.

[0039] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0040] Example 3: Corresponding to the above method embodiments, this embodiment also provides a turnout switch rail guided wave signal reconstruction device based on three-dimensional dispersion characteristics. The turnout switch rail guided wave signal reconstruction device based on three-dimensional dispersion characteristics described below and the turnout switch rail guided wave signal reconstruction method based on three-dimensional dispersion characteristics described above can be referred to in correspondence.

[0041] Figure 4This is a block diagram illustrating a turnout tip rail guided wave signal reconstruction device 800 based on three-dimensional dispersion characteristics, according to an exemplary embodiment. Figure 4 As shown, the turnout switch rail guided wave signal reconstruction device 800 based on three-dimensional dispersion characteristics may include: a processor 801 and a memory 802. The turnout switch rail guided wave signal reconstruction device 800 may also include one or more of the following: a multimedia component 803, an I / O interface 804, and a communication component 805.

[0042] The processor 801 controls the overall operation of the turnout switch rail guided wave signal reconstruction device 800 based on three-dimensional dispersion characteristics to complete all or part of the steps in the aforementioned turnout switch rail guided wave signal reconstruction method based on three-dimensional dispersion characteristics. The memory 802 stores various types of data to support the operation of the turnout switch rail guided wave signal reconstruction device 800. This data may include, for example, instructions for any application or method operating on the turnout switch rail guided wave signal reconstruction device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the turnout tip rail guided wave signal reconstruction device 800 based on three-dimensional dispersion characteristics and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or one or more combinations thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0043] In an exemplary embodiment, the turnout switch rail guided wave signal reconstruction device 800 based on three-dimensional dispersion characteristics can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described turnout switch rail guided wave signal reconstruction method based on three-dimensional dispersion characteristics.

[0044] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the above-described method for reconstructing turnout switch rail guided wave signals based on three-dimensional dispersion characteristics. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by the processor 801 of the turnout switch rail guided wave signal reconstruction device 800 based on three-dimensional dispersion characteristics to complete the above-described method for reconstructing turnout switch rail guided wave signals based on three-dimensional dispersion characteristics.

[0045] Example 4: Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the above-described method for reconstructing turnout tip rail guided wave signals based on three-dimensional dispersion characteristics.

[0046] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the turnout switch rail guided wave signal reconstruction method based on three-dimensional dispersion characteristics as described in the above method embodiments.

[0047] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0048] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0049] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for reconstructing turnout tip rail guided wave signals based on three-dimensional dispersion characteristics, characterized in that, include: Calculation and verification sensors are designed and installed longitudinally along the turnout switch rail; A finite element model of the turnout switch rail is established, and a three-dimensional dispersion matrix of the turnout switch rail based on the finite element model is constructed using the wavenumber-frequency-distance method. Based on the preset guided wave excitation center frequency and the calculated sensor position information, wavenumber data is extracted from the three-dimensional dispersion matrix to establish a theoretical frequency distribution matrix; The time-domain signal collected by the computational sensor is acquired and transformed to obtain the actual frequency distribution vector; Based on the theoretical frequency distribution matrix and the actual frequency distribution vector, the modal waveform amplitude coefficients are solved, and the modal waveform amplitude coefficients are verified and optimized using the time-domain signal collected by the verification sensor to obtain the optimized modal waveform amplitude coefficients. Based on the optimized modal waveform amplitude coefficients and the wave number data extracted from the three-dimensional dispersion matrix, the guided wave signal at any target distance of the turnout switch rail is calculated and reconstructed through wave propagation.

2. The method for reconstructing turnout tip rail guided wave signals based on three-dimensional dispersion characteristics according to claim 1, characterized in that, The establishment of the finite element model of the turnout switch rail, and the construction of the three-dimensional dispersion matrix of the turnout switch rail based on the finite element model, including: Obtain the geometric dimensions and alignment parameters of the turnout switch rail in order to establish a finite element model of the turnout switch rail; In the finite element model, a frequency range is set, and the structural vibration modes of the turnout switch rail at several discrete frequencies within the frequency range are obtained using the characteristic frequency method. Displacement calculation nodes are arranged longitudinally at the web position of the turnout switch rail to obtain mode displacement data; Based on the modal displacement data, plot the distance-displacement curves corresponding to each structural mode at each discrete frequency; Based on the distance-displacement curve, the wave value at each location is determined, and a three-dimensional dispersion matrix of wave number-frequency-distance is constructed according to the correspondence between frequency, wave value and distance.

3. The method for reconstructing turnout tip rail guided wave signals based on three-dimensional dispersion characteristics according to claim 1, characterized in that, The process of extracting wavenumber data from the three-dimensional dispersion matrix to establish a theoretical frequency distribution matrix based on the preset guided wave excitation center frequency and the calculated sensor position information includes: From the three-dimensional dispersion matrix, the wave values ​​of all guided wave modes at the center frequency at each calculated sensor are extracted. The guided wave modes are obtained by structural mode shape conversion. Based on the wave values, the phase change of each guided wave mode propagating from the guided wave exciter to each computational sensor point is calculated, and the theoretical frequency distribution matrix is ​​obtained.

4. The method for reconstructing turnout tip rail guided wave signals based on three-dimensional dispersion characteristics according to claim 1, characterized in that, The step of acquiring and transforming the time-domain signal collected by the computational sensor to obtain the actual frequency distribution vector includes: After the waveguide exciter transmits a signal at the center frequency, the time-domain signal recorded by the calculation sensor is acquired. Perform a Fourier transform on the time-domain signal acquired by each computing sensor to obtain the corresponding frequency-domain signal; Extract the complex frequency components of the frequency domain signal at the center frequency, and arrange all the complex frequency components of the calculated sensors in the order of the sensors to form the actual frequency distribution vector.

5. The method for reconstructing turnout tip rail guided wave signals based on three-dimensional dispersion characteristics according to claim 1, characterized in that, Based on the theoretical frequency distribution matrix and the actual frequency distribution vector, the modal waveform amplitude coefficients are solved, including: Based on the theoretical frequency distribution matrix and the actual frequency distribution vector, a linear equation is established regarding the modal waveform amplitude coefficients; An initial value is set for the number of non-zero elements in the modal waveform amplitude coefficients, and the linear equation is solved using a sparse optimization algorithm to obtain the initial modal waveform amplitude coefficients.

6. A turnout tip rail guided wave signal reconstruction system based on three-dimensional dispersion characteristics, characterized in that, include: The sensor deployment module is used to deploy and verify sensors along the longitudinal direction of the turnout switch rail. The dispersion characteristic calculation module is used to establish a finite element model of the turnout switch rail and construct a three-dimensional dispersion matrix of the turnout switch rail based on the finite element model. The theoretical modeling module is used to extract wavenumber data from the three-dimensional dispersion matrix to establish a theoretical frequency distribution matrix based on the preset guided wave excitation center frequency and the sensor position information. The signal acquisition and processing module is used to acquire and transform the time-domain signal collected by the calculation sensor to obtain the actual frequency distribution vector; The parameter solving and optimization module is used to solve the modal waveform amplitude coefficients based on the theoretical frequency distribution matrix and the actual frequency distribution vector, and to verify and optimize the modal waveform amplitude coefficients using the time-domain signal collected by the verification sensor, so as to obtain the optimized modal waveform amplitude coefficients. The signal reconstruction module is used to calculate and reconstruct the guided wave signal at any target distance of the turnout switch rail based on the optimized modal waveform amplitude coefficients and wave number data extracted from the three-dimensional dispersion matrix.

7. The turnout tip rail guided wave signal reconstruction system based on three-dimensional dispersion characteristics according to claim 6, characterized in that, The dispersion feature calculation module includes: Modeling units are used to obtain the geometric dimensions and alignment parameters of the turnout switch rails in order to establish a finite element model of the turnout switch rails. The feature analysis unit is used to set the frequency range in the finite element model and use the characteristic frequency method to obtain the structural vibration mode of the turnout switch rail at several discrete frequencies within the frequency range. The data extraction unit is used to arrange displacement calculation nodes longitudinally at the web position of the turnout switch rail in order to obtain mode displacement data. The curve plotting unit is used to plot the distance-displacement curves corresponding to each structural mode at each discrete frequency based on the mode displacement data. The matrix construction unit is used to determine the wave value at each position based on the distance-displacement curve, and construct a three-dimensional dispersion matrix of wave number-frequency-distance according to the correspondence between frequency, wave value and distance.

8. The turnout tip rail guided wave signal reconstruction system based on three-dimensional dispersion characteristics according to claim 6, characterized in that, The theoretical modeling module includes: The data extraction unit is used to extract the wave values ​​of all guided wave modes at each computational sensor at the center frequency from the three-dimensional dispersion matrix, wherein the guided wave modes are obtained by structural mode shape conversion; The matrix calculation unit is used to calculate the phase change of each guided wave mode from the guided wave exciter to each calculation sensor point based on the wave value, and obtain the theoretical frequency distribution matrix.

9. The turnout tip rail guided wave signal reconstruction system based on three-dimensional dispersion characteristics according to claim 6, characterized in that, The signal acquisition and processing module includes: The signal acquisition unit is used to acquire and calculate the time-domain signal recorded by the sensor after the waveguide exciter transmits a signal at the center frequency; The transformation unit is used to perform Fourier transform on the time-domain signal acquired by each computing sensor to obtain the corresponding frequency-domain signal; The vector construction unit is used to extract the complex frequency components of the frequency domain signal at the center frequency, and arrange the complex frequency components of all calculated sensors in the sensor order to form the actual frequency distribution vector.

10. The turnout tip rail guided wave signal reconstruction system based on three-dimensional dispersion characteristics according to claim 6, characterized in that, The parameter solving and optimization module includes: The equation-establishing unit is used to establish a linear equation about the modal waveform amplitude coefficients based on the theoretical frequency distribution matrix and the actual frequency distribution vector. The sparse solver unit is used to set the initial value of the number of non-zero elements in the modal waveform amplitude coefficients and to solve the linear equation using a sparse optimization algorithm to obtain the initial modal waveform amplitude coefficients.

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