An interference radar-based high-speed rail bridge health monitoring method, medium and device

Abstract

The present application relates to the technical field of bridge track health monitoring, in particular to a high-speed rail bridge health monitoring method, medium and equipment based on an interferometric radar. The method comprises: identifying the driving lane of the train on the bridge through the interferometric radar; based on the driving lane, obtaining the bridge data required for health monitoring when the train passes through the bridge through the interferometric radar; processing the bridge data and extracting the product function; analyzing the product function, selecting the effective product function; performing time-frequency analysis on the effective product function to obtain the time-frequency pattern of the bridge data, and performing health monitoring on the high-speed rail bridge according to the time-frequency pattern. According to the bridge signal collected by the interferometric radar, the driving lane of the high-speed train is determined, the vibration characteristics of the bridge are further analyzed through the processing of RLMD and SET, the clear time-frequency pattern is obtained, and the time-frequency characteristics of the bridge are determined according to the time-frequency pattern, thereby providing a reliable method for health monitoring during the driving of the high-speed train.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge track health monitoring, and particularly relates to a high-speed rail bridge health monitoring method based on an interference radar, a medium and equipment. BACKGROUND

[0002] Bridges are the basic components of civil infrastructure and play a vital role in the transportation network. Over time, continuous exposure to environmental conditions and traffic loads can lead to gradual degradation of their structural performance, which can compromise their safety, functionality, and durability. To reduce these risks, bridge health monitoring systems have been widely applied to provide real-time insights into the condition and performance of bridge structures.

[0003] However, the existing health monitoring methods applied to high-speed rail bridges using time-frequency analysis are not clear and accurate enough in analyzing bridge data, and cannot well obtain the vibration characteristics of the bridge during train operation, and further analyze the health status of the bridge.

[0004] Therefore, it is urgent to propose a more accurate and clear high-speed rail bridge health monitoring method to solve the problems in the prior art. SUMMARY

[0005] The present application aims to provide a high-speed rail bridge health monitoring method based on an interference radar, a medium and equipment, and the specific technical solutions are as follows:

[0006] A high-speed rail bridge health monitoring method based on an interference radar, comprising the following steps:

[0007] S1, identifying the driving lane of the train on the bridge through the interference radar;

[0008] S2, based on the driving lane, obtaining the bridge data during the train passing through for health monitoring through the interference radar;

[0009] S3, processing the bridge data using a robust local mean decomposition method to obtain its frequency modulation signal and amplitude signal, and then extracting the product function from the bridge data;

[0010] S4, analyzing the product function using Hilbert transform and Fourier transform to select effective product functions;

[0011] S5, performing time-frequency analysis on the effective product functions selected in S4 using synchronous extraction transform to obtain the time-frequency pattern of the bridge data, and performing health monitoring on the high-speed rail bridge according to the time-frequency pattern.

[0012] Further, the S1 specifically comprises:

[0013] The interferometric radar is placed at a distance of 20-40 m from the side of the bridge and is aimed at the middle region of the bridge span;

[0014] When the high-speed train passes the bridge, the interferometric radar acquires vibration data of the bridge;

[0015] The vibration data is analyzed to identify the driving lane of the high-speed train.

[0016] Further, the interferometric radar is an IBIS-FS interferometric radar.

[0017] Further, the S3 specifically includes:

[0018] S3.1, preprocessing the bridge data, including boundary condition processing and signal extension;

[0019] Boundary condition processing: determining the boundary symmetry point;

[0020] Signal extension: setting a symmetric reflection point at the determined boundary symmetry point, extending the signal boundary, and obtaining an extended signal ;

[0021] S3.2, iterative decomposition, extracting product function components from the extended signal one by one until the stopping rule is met;

[0022] S3.3, separating the product function component from the current signal, repeating S3.2 until the residual error is stable, and realizing the extraction of the product function;

[0023] S3.4, post-processing: including bridge data signal reconstruction and truncation, and fitting all results back to the original signal.

[0024] Further, S3.2 specifically is:

[0025] 1) Find local extrema:

[0026] Let the current signal be , initially ; find all local maxima and local minima on ;

[0027] 2) Calculate the local mean function :

[0028] For each group of adjacent extreme points, calculate the average value , ;

[0029] Extend all average values between the extreme point times to form a continuous local mean function , representing time;

[0030] for applying a moving average filter to obtain a smoothed result;

[0031] 3) calculating the envelope function :

[0032] calculating the local amplitude based on adjacent extreme points , ;

[0033] extending all to obtain an initial envelope signal;

[0034] robust processing using a robust local mean decomposition method:

[0035] obtaining a step local amplitude signal or a step local mean signal;

[0036] calculating the probability density function based on the step where is the bin in the histogram, is the number of bins;

[0037] calculating the center and the standard deviation σ of the step:

[0038] ;

[0039] ;

[0040] where, is the bin in the histogram;

[0041] calculating the optimal subset size : where, is the function that rounds the input value up to the nearest odd integer;

[0042] using to control the envelope smoothing process to obtain a continuously changing envelope function , i.e. the amplitude signal, that is smooth;

[0043] 4) signal separation and demodulation:

[0044] separating the local mean function from the current signal to obtain a de-meaned signal :

[0045] ;

[0046] with the envelope function demodulating to obtain a demodulated signal , i.e. a frequency modulated signal;

[0047] ;

[0048] 5) Check the stopping criterion of the screening:

[0049] define a zero baseline envelope signal ;

[0050] calculate the objective function :

[0051] ;

[0052] where is the signal length, is the zero baseline envelope value at the th sampling point;

[0053] if is smaller than a predetermined threshold or the envelope is flat enough, stop the screening process of the current product function; otherwise, take as the new input signal and go back to 1) repeat the iterative decomposition;

[0054] 6) Generate the product function component;

[0055] Once the stopping criterion is met, multiply the final demodulated signal with the envelope function to obtain the th product function component :

[0056] .

[0057] Further, S3.3 is specifically:

[0058] separate the product function component from the current signal to obtain a new residual signal :

[0059] ;

[0060] take the new residual signal as the new signal and let ;

[0061] repeat the iterative decomposition process of S3.2 to extract the next product function component;

[0062] stop the decomposition when becomes constant or contains no significant oscillation; ​

[0063] The final residual is denoted by where is the number of extracted product functions.

[0064] Further, S3.4 is specified as:

[0065] Truncation of all components: The product function components and the final residual obtained on the extended signal are truncated back to the original signal length;

[0066] Signal reconstruction: The original signal is represented as the sum of all product function components and the final residual:

[0067] .

[0068] Further, S5 is specified as:

[0069] The is represented by the short-time Fourier transform representation as follows:

[0070] ;

[0071] where is the Fourier transform representation; K is a finite number; denotes the amplitude of the effective product function, denotes the amplitude function; denotes the Fourier transform of the window function; denotes the angular frequency of the effective product function, is a function of the angular frequency; is the natural constant; is the time; is the imaginary unit;

[0072] The instantaneous frequency IF is represented as:

[0073] ;

[0074] where is the instantaneous frequency;

[0075] The Dirac δ function is used both for the time-frequency display of the signal and for the extraction of the key instantaneous frequencies, in which case the time-frequency pattern function is obtained by the synchrosqueezing transform as follows:

[0076] ;

[0077] where is the time-frequency pattern function; is the synchrosqueezing operator, which is represented as follows:

[0078] .

[0079] The application further provides a readable storage medium, which stores computer program instructions, and the computer program instructions realize the interference radar-based high-speed rail bridge health monitoring method when executed by a processor.

[0080] The application further provides an electronic device, which comprises at least one processor, at least one memory and computer program instructions stored in the memory, and the computer program instructions realize the interference radar-based high-speed rail bridge health monitoring method when executed by the processor.

[0081] The application has the following beneficial effects:

[0082] The interference radar-based high-speed rail bridge health monitoring method provided by the application can analyze and judge the driving lane of the high-speed train according to the bridge signal collected by the interference radar, further analyze the vibration characteristics of the bridge through the processing of the RLMD and the SET, obtain a clear time-frequency pattern, and judge the time-frequency characteristics of the bridge according to the time-frequency pattern, thereby providing a reliable method for health monitoring in the driving process of the high-speed train.

[0083] In addition to the purposes, features and advantages described above, the application has other purposes, features and advantages. The application will be further described in detail below with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0084] The accompanying drawings, which form a part of the present application, are included to provide a further understanding of the application, and are incorporated herein for purposes of illustrating the illustrative embodiments of the present application and the explanations provided herein are made with respect to the illustrative embodiments and are not intended to limit the present application. In the drawings:

[0085] Figure 1 Fig. 1 is a flowchart of the interference radar-based high-speed rail bridge health monitoring method of the application;

[0086] Figure 2 Fig. 2 is a schematic diagram of the interference radar arranged on the side close to the train;

[0087] Figure 3 Fig. 3 is vibration data Figure 1 Fig. 3 is vibration data, wherein (a) is a vibration data graph from the 2482nd second to the 2502nd second, and (b) is a vibration data graph from the 4188th second to the 4208th second;

[0088] Figure 4 Fig. 4 is a schematic diagram of the interference radar arranged on the side far from the train;

[0089] Figure 5 Fig. 5 is vibration data Figure 2Wherein, (a) is the vibration data graph from 6113th second to 6133th second, and (b) is the vibration data graph from 8202th second to 8222th second;

[0090] Figure 6 is the vibration data Figure 3 (2.5 hours);

[0091] Fig. 7 (a) is Figure 6 , and Fig. 7 (b) is a spectrum analysis graph based on the data in Fig. 7 (a);

[0092] Figure 8 is a time-frequency feature graph obtained by processing the data in Fig. 7 (a) by using a short-time Fourier transform method, wherein the curve part is a frequency spectrum distribution;

[0093] Figure 9 is a frequency modulation signal graph and an amplitude signal graph obtained by processing the data in Fig. 7 (a) by using the RLMD in the present application, wherein (a) is the frequency modulation signal graph, and (b) is the amplitude signal graph;

[0094] Fig. 10 (a) is a product function schematic diagram, Fig. 10 (b) is a schematic diagram after Hilbert transform of the product function, and Fig. 10 (c) is a schematic diagram after Fourier transform of the product function;

[0095] Figure 11 is a comparison graph of time-frequency analysis results obtained by using the method in the present application and by using a traditional analysis method, wherein (a) is a result graph obtained by using continuous wavelet transform for time-frequency analysis, (b) is a result graph obtained by using short-time Fourier transform for time-frequency analysis, and (c) is a result graph obtained by using synchronous extraction transform for time-frequency analysis. DETAILED DESCRIPTION

[0096] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0097] Embodiment:

[0098] Referring to Figure 1 , the embodiment provides a high-speed rail bridge health monitoring method based on an interference radar, comprising the following steps:

[0099] S1, identifying the driving lane of the train on the bridge by the interference radar; specifically:

[0100] Placing the interference radar to the side of the bridge 20-40 m, and aiming the interference radar at the mid-span region of the bridge; in the embodiment, the interference radar is an IBIS-FS interference radar;

[0101] When the high-speed rail passes through the bridge, the interference radar acquires the vibration data of the bridge;

[0102] The vibration data is analyzed to identify the driving lane of the high-speed train, specifically:

[0103] Since the high-speed train passes through the bridge from a single lane, the high-speed train has a large impact energy and a large load, so that the bridge produces asymmetric deformation, so when observed from the side of the bridge, the data will show positive and negative differences, some data changes along the negative value direction, and some data changes along the positive value; as shown in Figure 2 , the interference radar is placed on the right side of the bridge, when the train drives in the right lane close to the interference radar, the data of the interference radar shows negative value, as shown in Figure 3 ; when the train drives in the left lane far away from the interference radar, the data of the interference radar shows positive value, as shown in Figure 4 and Figure 5 ;

[0104] According to the change of the above data, it can be judged that the driving lane of the high-speed train.

[0105] S2, based on the driving lane, the bridge data (such as the acceleration time history data and displacement time history data of the bridge) required for health monitoring when the train passes through is obtained by the interference radar;

[0106] It should be noted that when the high-speed train drives in the right lane, the signal obtained by the interference radar will be disturbed, because the interference radar determines the deformation response of the bridge under the action of the train load by analyzing the phase difference between the echo signals received at different times, when working in the right lane, the radar's detection area captures the bridge echo signal at the same time, also captures the reflection signal of the high-speed train. This interference to the extraction of bridge echo signals will reduce the signal quality, therefore, the obtained bridge data needs to be further analyzed and processed.

[0107] S3, the bridge data is processed using a robust local mean decomposition method (RLMD) to obtain its frequency modulation signal and amplitude signal, and then the product function is extracted from the bridge data; specifically including:

[0108] S3.1, pre-processing the bridge data, including boundary condition processing and signal extension, to solve the local extreme value problem at the signal boundary and avoid decomposition distortion; specifically including:

[0109] 1) Boundary condition processing: determine the boundary symmetry point;

[0110] Left boundary processing:

[0111] ① If the first local extreme value is the maximum value ( ) :

[0112] Check if the first point is less than the first local minimum ( ), if so, consider the first point as a minimum, and designate as the symmetry point; if not, designate the first maximum as the left symmetry point;

[0113] 2) If the first local extremum is a minimum ( ):

[0114] Check if the first point is greater than the first local maximum ( ): if so, consider the first point as a maximum, and designate as the symmetry point; otherwise, designate as the left symmetry point.

[0115] Right boundary processing: similar to the left boundary, but for the signal end (check the relationship between the last point and the last local extremum, designate the right symmetry point).

[0116] 2) Signal extension: set symmetric reflection points on the determined boundary symmetry points, extend the signal boundary, and obtain the extended signal ;

[0117] The extension range is determined according to actual needs, but it is necessary to ensure that the length of the extended signal is sufficient to cover the boundary effect.

[0118] S3.2, iterative decomposition, extract the product function ( PF ) component from the extended signal one by one until the stopping rule is met; the extraction of each PF component is a sub-iteration process (called "screening process"); specifically:

[0119] 1) Find local extrema:

[0120] Let the current signal be , initially ; find all local maxima and local minima on ;

[0121] 2) Calculate the local mean function :

[0122] For each group of adjacent extremum points, calculate the average value , ;

[0123] Extend all average values between the extremum points to form a continuous local mean function (achieved by linear interpolation between the extremum points), denotes time;

[0124] for Apply moving average filter to obtain smooth result and reduce noise effect;

[0125] 3) Calculate envelope function :

[0126] Calculate local amplitude based on adjacent extreme points , ;

[0127] Extend all to obtain initial envelope signal;

[0128] Apply robust local mean decomposition (RLMD) for robust processing:

[0129] Obtain stepwise local amplitude signal or stepwise local mean signal; "step" refers to window or interval of signal processing;

[0130] Calculate probability density function based on step , where is bin in histogram, is number of bins;

[0131] Calculate center of step and standard deviation σ :

[0132] ;

[0133] ;

[0134] where, is the bin in histogram;

[0135] Calculate optimal subset size : , where, is a function that rounds input value up to the nearest odd integer, for example, ;

[0136] Use to control envelope smoothing process (such as applying moving average filter with size ), to obtain smooth and continuous envelope function , i.e. amplitude signal;

[0137] 4) Signal separation and demodulation:

[0138] Separate local mean function from current signal to obtain mean-removed signal :

[0139] ;

[0140] with the envelope function demodulating to get demodulated signal , i.e. frequency modulated signal;

[0141] ;

[0142] 5) Check the stopping criterion of screening:

[0143] define the zero baseline envelope signal (i.e. the normalized or mean-subtracted form of the envelope function);

[0144] calculate the objective function :

[0145] ;

[0146] where is the signal length, is the zero baseline envelope value of the th sampling point;

[0147] if is less than a predetermined threshold (the threshold is set according to the actual application) or the envelope is flat enough (i.e. is close to a pure frequency modulated signal), stop the screening process of the current product function; otherwise, is taken as the new input signal, return 1) repeat the iterative decomposition;

[0148] 6) Generate product function components;

[0149] Once the stopping criterion is met, multiply the final demodulated signal with the envelope function (i.e. multiply the frequency modulated signal with the amplitude signal) to get the th product function component :

[0150] .

[0151] S3.3, separate the product function component from the current signal, repeat S3.2 until the residual error is stable, realize the extraction of the product function; specifically:

[0152] separate the product function component from the current signal , get the new residual error signal :

[0153] ;

[0154] take the new residual error signal as the new signal, let ; ​

[0155] Repeat the iterative decomposition process of S3.2 to extract the next product function component;

[0156] When becomes constant (no oscillation) or contains no significant oscillation, stop the decomposition;

[0157] The final residual is denoted as where is the number of extracted product functions.

[0158] S3.4, Post-processing: including bridge data signal reconstruction and truncation, fitting all results back to the original signal; Specifically:

[0159] Truncating all components: truncate the product function components and the residual obtained on the extended signal back to the original signal length (remove the extended part);

[0160] Signal reconstruction: the original signal is expressed as the sum of all product function components and the final residual:

[0161] ;

[0162] where is the number of extracted product functions.

[0163] S4, Analyze the product functions using Hilbert transform and Fourier transform, and select effective product functions;

[0164] The product function contains multiple product function components, which contain different signals. By using Hilbert transform and Fourier transform on the product function, analyze and select the product function (i.e. effective product function) containing the bridge vibration characteristic signal (including vibration energy intensity, frequency distribution and time-frequency information, etc. Key information) for further analysis.

[0165] S5, Use the synchronous extraction transform method (SET) to analyze the effective product functions selected in S4 for time-frequency analysis, obtain clear time-frequency patterns of bridge data, and perform health monitoring on high-speed railway bridges according to the time-frequency patterns; Specifically:

[0166] Let be expressed as follows using short-time Fourier transform:

[0167] ;

[0168] where is the expression of Fourier transform; K is a finite number; denotes the amplitude of the effective product function, denotes the amplitude function; a Fourier transform representing a window function; an angular frequency representing an effective product function, is a function of an angular frequency; is a natural constant; is a time; is an imaginary unit;

[0169] The instantaneous frequency IF is represented as:

[0170] ;

[0171] wherein, is an instantaneous frequency;

[0172] The Dirac δ function (Dirac delta) is used for both time-frequency display and key instantaneous frequency extraction of the signal, in which case, the time-frequency pattern function expression is obtained by a synchronous extraction transform method as follows:

[0173] ;

[0174] wherein, is a time-frequency pattern function; is a synchronous extraction operator, and the expression is as follows:

[0175] ;

[0176] According to the time-frequency pattern function, a clear time-frequency pattern (the synchronous extraction operator is used for extracting the main key frequency in the time-frequency pattern, so that the energy of the time-frequency display is more concentrated) can be obtained, and health monitoring of the high-speed railway bridge is further realized.

[0177] The high-speed railway bridge health monitoring method based on the interference radar provided by the application can analyze and judge the driving lane of the high-speed train according to the bridge signal collected by the interference radar, further analyze the vibration characteristics of the bridge through the processing of the RLMD and SET, obtain a clear time-frequency pattern, judge the time-frequency characteristics of the bridge according to the time-frequency pattern, and provide a reliable method for health monitoring of the high-speed train in the driving process.

[0178] A certain section of the bridge through which the high-speed train passes is selected for experiment, the interference radar (IBIS-FS) is placed on the ground on the side of the bridge, 30m away from the side of the bridge, and the interference radar is aimed at the mid-span area of the bridge.

[0179] Referring to Figures 2-5 , the driving lane of the train is judged through the positive and negative signs in the subsequent vibration data by placing the interference radar on one side of the bridge.

[0180] When the high-speed train passes through the bridge, the interference radar obtains the vibration data of the bridge, the collection time is 2.5 hours, and the data graph obtained is seen in Figure 6 ; some data (a section selected by a rectangular frame) in Figure 6 are enlarged and subjected to fast Fourier transform FFT processing, seen in Fig. 7(a) and Fig. 7(b), and from Fig. 7(b), it can be observed that the low-frequency signal is dominant, resulting in the frequency distribution of the bridge itself being covered up.

[0181] The short-time Fourier transform method (STFT) is used to process the vibration signal of Fig. 7(a), and the time-frequency characteristic graph is obtained as Figure 8 , and from Figure 8 , it can be seen that the time-frequency characteristic graph is relatively fuzzy.

[0182] The vibration signal of Fig. 7(a) is processed by using the RLMD in the application, four pairs of frequency modulation signals and amplitude signals are obtained, and then each is multiplied to obtain a product function PF 1 (frequency modulation signal 1 multiplied by amplitude signal 1), PF 2 (frequency modulation signal 2 multiplied by amplitude signal 2) , PF 3 (frequency modulation signal 3 multiplied by amplitude signal 3), and PF 4 (frequency modulation signal 4 multiplied by amplitude signal 4), seen in Figure 9 ;

[0183] Then the product function is analyzed by using Hilbert transform and Fourier transform, seen in Fig. 10(a)-Fig. 10(c), and the effective product function PF 1, PF 1 contains the main vibration characteristics and frequency domain information of the bridge, so the time-frequency analysis of PF 1 is further carried out;

[0184] The SET in the application is used to carry out time-frequency analysis on PF 1, and the time-frequency characteristic graph is obtained as (c) in Figure 11 ; and the results obtained by using the traditional time-frequency analysis method (short-time Fourier transform STFT, continuous wavelet transform CWT) are compared, and the comparison graph is seen in Figure 11 ; from (a), (b), (c) in Figure 11 , it can be seen that the SET can better concentrate the time-frequency energy, and is more conducive to extracting the vibration characteristics of the bridge, and a clearer time-frequency characteristic graph is obtained, which provides a more accurate technical scheme for the time-frequency analysis of the bridge, so as to accurately analyze the health status of the bridge.

[0185] The embodiment also includes an electronic device comprising at least one processor, at least one memory, and computer program instructions stored in the memory for execution by the processor to perform the method of high-speed rail bridge health monitoring based on interferometric radar as described above.

[0186] The electronic device can be a mobile phone, a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The electronic device can include, but is not limited to, a processor, a memory. For example, the electronic device can also include an input / output device, a network access device, a bus, and the like.

[0187] The above merely provides preferred embodiments of the present application, but not for limiting the present application. For the person skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, and the like within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for health monitoring of high-speed railway bridges based on interferometric radar, characterized in that, Includes the following steps: S1. Identifying the train's travel lane on the bridge using interferometric radar; specifically including: Place the interferometric radar 20-40m away from the side of the bridge and aim it at the mid-span area of ​​the bridge. When the high-speed train passes over the bridge, the interferometric radar acquires the bridge's vibration data; The vibration data was analyzed to identify the high-speed train's operating lane; S2. Based on the driving lane, acquire bridge data as trains pass through using interferometric radar, which is required for health monitoring. S3. The bridge data is processed using a robust local mean decomposition method to obtain its frequency modulation signal and amplitude signal, and then the product function is extracted from the bridge data. S4. Analyze the product function using Hilbert transform and Fourier transform, and select an effective product function; S5. Use synchronous extraction transformation to perform time-frequency analysis on the effective product function selected in S4 to obtain the time-frequency graph of the bridge data, and conduct health monitoring of the high-speed railway bridge based on the time-frequency graph.

2. The method for health monitoring of high-speed railway bridges based on interferometric radar according to claim 1, characterized in that, The interferometric radar is the IBIS-FS interferometric radar.

3. The method for health monitoring of high-speed railway bridges based on interferometric radar according to claim 1, characterized in that, S3 specifically includes: S3.1 Preprocess the bridge data, including boundary condition processing and signal extension; Boundary condition handling: Determine the boundary symmetric points; Signal extension: By setting symmetrical reflection points at defined boundary symmetrical points, the signal boundary is extended to obtain an extended signal. ; S3.2 Iterative decomposition: Extract the product function components one by one from the extended signal until the stopping rule is met; S3.3 Separate the product function component from the current signal, and repeat S3.2 until the residual is stable to achieve the extraction of the product function; S3.4 Post-processing: This includes bridge data signal reconstruction and truncation, adapting all results back to the original signal.

4. The method for health monitoring of high-speed railway bridges based on interferometric radar according to claim 3, characterized in that, S3.2 specifically refers to: 1) Finding local extrema: Let the current signal be Initially ;exist Find all local maxima on the top and local minima ; 2) Calculate the local mean function : For each group of adjacent extreme points, calculate the average value. , ; Extend all averages Between the time of reaching the extreme point, a continuous local mean function is formed. , t Indicates time; right Apply a moving average filter to obtain a smoothed result; 3) Calculate the envelope function : Calculate local amplitude based on adjacent extreme points , ; Extend all The initial envelope signal is obtained; A robust local mean decomposition method is used for robust processing: Obtain the local amplitude signal or local average signal of the step size; Calculate the probability density function based on the step size. ,in These are the bins in the histogram. It refers to the number of boxes; Calculate the center of the step size and standard deviation σ : ； ； in, It is the th in the histogram One box; Calculate the optimal subset size : ,in, It is a function that rounds the input value up to the nearest odd integer; use By controlling the envelope smoothing process, a smoothly changing continuous envelope function can be obtained. That is, amplitude signal; 4) Signal separation and demodulation: From the current signal Separating local mean functions To obtain the mean-reduced signal : ； Using envelope function right Demodulation is performed to obtain the demodulated signal. That is, frequency modulation signal; ； 5) Check the screening stopping criteria: Define zero baseline envelope signal ; Calculate the objective function : ； in, It is the signal length. It is the first The zero baseline envelope value of each sampling point; if If the value is less than a predetermined threshold or the envelope is sufficiently flat, then stop the current product function selection process; otherwise, continue. As a new input signal, return 1) Repeat the iterative decomposition; 6) Generate the components of the product function; Once the stopping criterion is met, the final demodulated signal is multiplied by the envelope function to obtain the first... i Each product function component : 。 5. The method for health monitoring of high-speed railway bridges based on interferometric radar according to claim 4, characterized in that, S3.3 specifically refers to: From the current signal The product function components are separated from the original signal to obtain a new residual signal. : ； New residual signal Consider it a new signal, ; Repeat the iterative decomposition process of S3.2 to extract the next product function component; when The decomposition stops when the result becomes a constant or does not contain significant oscillations. The final residual is recorded as ,in It represents the number of product functions extracted.

6. The method for health monitoring of high-speed railway bridges based on interferometric radar according to claim 5, characterized in that, S3.4 specifically refers to: Truncate all components: Truncate the product function components and residuals obtained on the extended signal back to the original signal length; Signal reconstruction: original signal It can be expressed as the sum of all product function components and the final residual: 。 7. The method for health monitoring of high-speed railway bridges based on interferometric radar according to claim 6, characterized in that, S5 specifically includes: Will The short-time Fourier transform representation is as follows: ； in, This is the representation of the Fourier transform; K It is a finite number; This represents the amplitude of the effective product function. Represents the amplitude function; The Fourier transform of the window function; This represents the angular frequency of the effective product function. It is a function of angular frequency; It is a natural constant; For time; The imaginary unit; Instantaneous frequency IF is represented as: ； in, Instantaneous frequency; Using Dirac δ The function is used for both time-frequency display of the signal and extraction of key instantaneous frequencies. In this case, the time-frequency graph function expression obtained through the synchronous extraction transformation method is as follows: ； in, This is a time-frequency graph function; To extract the operator synchronously, the expression is as follows: 。 8. A readable storage medium, characterized in that, It stores computer program instructions, which, when executed by a processor, implement a high-speed railway bridge health monitoring method based on interferometric radar as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, include: A method for monitoring the health of high-speed railway bridges based on interferometric radar, comprising at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions are executed by the processor.

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