A ballast railway bed mechanical state monitoring method based on high-frequency environmental noise

Abstract

The application discloses a kind of based on high-frequency ambient noise ballast railway bed mechanical state monitoring method, comprising: S1, the three-axis acceleration signal under high-frequency ambient noise of ballast railway is collected;S2, three-axis acceleration signal is integrated to obtain three-axis velocity signal, based on three-axis velocity signal calculation horizontal / vertical power spectral density, average spectral ratio curve is calculated to horizontal / vertical power spectral density;S3, the initial basic structure model is constructed based on the prior information of bed and subgrade, average spectral ratio curve is inverted using global inversion algorithm based on initial basic structure model, and the shear wave velocity of bed is obtained, and the mechanical state of bed is judged according to the shear wave velocity of bed.The application establishes average spectral ratio curve by collecting the signal under high-frequency ambient noise, and obtains the shear wave velocity of bed by inversion, realizes real-time monitoring and long-term tracking to the mechanical state of ballast railway bed, with significant engineering application potential and popularization value.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ballast railway operation and maintenance, and particularly relates to a ballast railway bed mechanical state monitoring method based on high-frequency environmental noise. BACKGROUND

[0002] Ballast railway is the most widely used track structure form in China's railway system, and the granular bed is an important part of the ballast railway foundation structure. Reasonable sub-track supporting stiffness is a necessary condition to ensure the stability, comfort and safety of train operation. However, the stiffness of the granular bed in the working conditions such as bridge and subgrade transition section, tunnel entrance and exit section, poor subgrade drainage section, sand area and coal transportation line is significantly time-varying due to the influence of factors such as climate change, rain erosion and repeated action of train load.

[0003] The current specification of the granular bed mechanical state evaluation needs to remove the sleeper fastener for manual static loading test, and the invasive operation needs to occupy a large amount of sky window time and labor cost, which seriously restricts the detection efficiency and operation applicability. The nondestructive testing methods currently applied in the field of railway foundation, such as mobile line loading car, track inspection trolley and ground penetrating radar, mainly focus on physical indicators or track geometric indicators, and cannot effectively evaluate the mechanical properties of the bed. In recent years, the elastic surface wave detection method is introduced into the related research, which evaluates the mechanical properties of the bed through the elastic wave velocity of the granular medium layer. However, the high-speed railway line in China uses concrete sleepers, and the wave impedance difference between the sleepers and the bed is significant. The interface elastic wave energy is mainly composed of reflected wave (78%), and the wave field under the track is complex. The traditional surface wave detection analysis method is based on the one-dimensional horizontal layering assumption, which cannot effectively obtain the dispersion curve containing the wave velocity characteristics of the bed layer, and the detection effect is poor and continuous testing cannot be carried out. SUMMARY

[0004] In view of one or more deficiencies in the prior art described above, the present application provides a ballast railway bed mechanical state monitoring method based on high-frequency environmental noise, which can obtain the dynamic parameters of the bed and subgrade using the environmental vibration noise around the site, without active excitation, and realize real-time monitoring and long-term continuous tracking of the mechanical state of the bed.

[0005] In order to achieve the above purpose, the present application adopts one or more of the following technical solutions:

[0006] A ballast railway bed mechanical state monitoring method based on high-frequency environmental noise, comprising the following steps:

[0007] S1, collecting triaxial acceleration signals under high-frequency environmental noise of the ballast railway;

[0008] S2, integrating the triaxial acceleration signal to obtain a triaxial velocity signal, calculating a horizontal / vertical power spectral density based on the triaxial velocity signal, and calculating an average spectral ratio curve based on the horizontal / vertical power spectral density;

[0009] S3, constructing an initial foundation structure model based on prior information of the track bed and the roadbed, performing inversion on the average spectral ratio curve based on the initial foundation structure model using a global inversion algorithm to obtain a track bed shear wave velocity, and determining a track bed mechanical state according to the track bed shear wave velocity.

[0010] Preferably, in step S1, the frequency range of the high-frequency environmental noise reflecting the track bed state is 50Hz-1kHz.

[0011] Preferably, in step S1, a triaxial accelerometer is used to obtain a triaxial acceleration signal under high-frequency environmental noise, and the triaxial accelerometer uses a wide frequency response sensor with an effective frequency response range covering the 50Hz-1kHz frequency range.

[0012] Preferably, the triaxial accelerometer is arranged on the upper surface of a sleeper box of a ballast railway and is coupled and fixed to the surface of the bulk track bed through coupling nails.

[0013] Preferably, in step S2, the specific steps of integrating the triaxial acceleration signal to obtain a triaxial velocity signal and calculating a horizontal / vertical power spectral density based on the triaxial velocity signal are as follows:

[0014] S21, performing detrended mean removal processing on the triaxial acceleration signal;

[0015] S22, integrating the processed triaxial acceleration signal to obtain a triaxial velocity signal, and performing detrended processing on the triaxial velocity signal;

[0016] S23, removing windows containing discontinuity points and near-field disturbance noise from the processed triaxial velocity signal to obtain a new triaxial velocity signal, and calculating a horizontal / vertical power spectral density based on the new triaxial velocity signal.

[0017] Preferably, after the average spectral ratio curve is calculated by iteratively calculating the horizontal / vertical power spectral density in step S2, the average spectral ratio curve is smoothed to eliminate curve burrs.

[0018] Preferably, in step S22, a preset sliding window is used to perform detrended processing on the triaxial velocity signal, and the sliding window is set to have a length of 100s and a step of 25s.

[0019] Preferably, in step S23, the specific steps of calculating a horizontal / vertical power spectral density based on the new triaxial velocity signal and calculating an average spectral ratio curve by iteratively calculating the horizontal / vertical power spectral density are as follows:

[0020] The new triaxial velocity signal is processed in a sliding time window manner, and continuous time periods are divided into multiple overlapping time periods, and the time-frequency spectrum of the signal is calculated by using short-time Fourier transform for each time period respectively;

[0021] The spectral power densities of the three directions are obtained from the time-frequency spectrum respectively, the ratio curve of the horizontal and vertical power spectrums is calculated after synthesizing the horizontal vibration power spectrum, and the following formula is used for calculation:

[0022]

[0023] wherein, , : the power spectral density of the jth window of the two orthogonal horizontal components; : the vertical component power spectral density of the jth window;

[0024] The average spectral ratio curve is calculated by using the following formula:

[0025]

[0026] wherein, n: the number of windows; : the ratio of the horizontal and vertical power spectrums of the jth window.

[0027] Preferably, in step S2, after obtaining the newly sampled window, the average spectral ratio curve is iteratively updated, and the average spectral ratio curve updated for the tth (t≥2) time is calculated by using the following formula:

[0028]

[0029] wherein, n: the number of windows; i: the sliding step;

[0030] The sliding window is updated in real time by using the following recursive iteration formula:

[0031]

[0032] Preferably, in step S3, for the spectral ratio curve updated for each iteration, the local optimization algorithm is used for fast inversion based on the bed shear wave velocity obtained by initial inversion, and the mechanical parameters of the bed are updated.

[0033] Preferably, in step S3, the initial basic structure model includes multiple initial parameters such as layer thickness, shear wave velocity, longitudinal wave velocity, Poisson's ratio and density; and the prior information of the bed and the subgrade includes the layer thickness and the material of the bed and the subgrade.

[0034] Preferably, in step S3, the global inversion algorithm uses the Monte Carlo algorithm, and the local optimization inversion algorithm uses the downhill method.

[0035] Preferably, in step S3, the specific process of judging the mechanical state of the ballast bed according to the shear wave velocity of the ballast bed is as follows:

[0036] Based on the relationship between the shear wave velocity and the shear modulus And the conversion relationship between the shear modulus and the elastic modulus The elastic modulus and the shear modulus of the ballast bed are calculated, and the mechanical state of the ballast bed is judged based on the elastic modulus and the shear modulus.

[0037] The application also provides a ballast railway ballast bed mechanical state monitoring system based on high-frequency environmental noise, comprising:

[0038] A signal acquisition module is configured to acquire triaxial acceleration signals under high-frequency environmental noise of a ballast railway.

[0039] A time-frequency processing module is configured to integrate the triaxial acceleration signals to obtain triaxial velocity signals, calculate horizontal / vertical power spectral densities based on the triaxial velocity signals, and calculate average spectral ratio curves based on the horizontal / vertical power spectral densities.

[0040] An inversion evaluation module is configured to construct an initial basic structure model based on prior information of a ballast bed and a subgrade, perform inversion on the average spectral ratio curves based on the initial basic structure model by using a global inversion algorithm, obtain a shear wave velocity of the ballast bed, and judge the mechanical state of the ballast bed according to the shear wave velocity of the ballast bed.

[0041] The application also provides an electronic device comprising a memory and a processor, wherein the memory is configured to store a program, and the processor is coupled to the memory and configured to execute the program stored in the memory to implement the steps of the ballast railway ballast bed mechanical state monitoring method based on high-frequency environmental noise according to any one of the above.

[0042] The application also provides a non-transitory computer-readable storage medium configured to store a readable computer program, and the computer program is executed by a processor to implement the steps of the ballast railway ballast bed mechanical state monitoring method based on high-frequency environmental noise according to any one of the above.

[0043] By adopting the above technical solutions, the application has the following advantages:

[0044] 1. This invention establishes an average spectral ratio curve by collecting signals under high-frequency environmental noise and obtains the shear wave velocity of the ballast track through inversion, thereby realizing the monitoring of the mechanical state of the ballast track. Furthermore, the sampling and calculation process in the monitoring method of this invention is continuous, enabling real-time monitoring and long-term tracking of the mechanical state of the ballast track. It has strong applicability to key nodes in the line or scenarios with large fluctuations in the ballast track state, and has significant engineering application potential and promotion value.

[0045] 2. This invention utilizes high-frequency environmental vibration and noise around the track to obtain dynamic parameters of the track bed and subgrade, enabling testing of the mechanical state of ballasted railway track beds. On the one hand, it eliminates the need for static loads. Compared to the manual static loading testing methods in current standards, this invention does not require the removal of sleeper fasteners when applied to track bed mechanical state monitoring, resulting in minimal intrusion and no impact on normal line operation. On the other hand, compared to traditional active excitation detection methods for elastic surface waves, this invention eliminates the need for artificial excitation sources, reducing costs. Furthermore, it overcomes the limitation of traditional active excitation detection methods for elastic surface waves in being unable to conduct continuous testing, enabling real-time, long-term tracking monitoring.

[0046] 3. Compared with non-destructive testing technologies such as mobile track loading vehicles, track inspection trolleys, and ground-penetrating radar, the ballast railway track bed mechanical condition monitoring method of the present invention can directly evaluate the track bed mechanical condition, and is more efficient and accurate in track bed mechanical condition monitoring. Attached Figure Description

[0047] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0048] Figure 1 This is a flowchart of the method for monitoring the mechanical state of ballasted railway track bed based on high-frequency environmental noise in an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the coupling method between the sensor and the loose-bed track in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the sensor arrangement in an embodiment of the present invention;

[0051] Figure 4 This is a diagram showing the processing effect of step S2 in an embodiment of the present invention;

[0052] Figure 5 This is a time-frequency analysis of rail transit load noise in an embodiment of the present invention. Figure 1 ;

[0053] Figure 6 This is a time-frequency analysis of ground traffic load noise in an embodiment of the present invention. Figure 2 ;

[0054] Figure 7 is a STA / LTA screening effect diagram in an embodiment of the present application;

[0055] Figure 8 is an iterative calculation diagram of horizontal / vertical spectral ratio curve sliding update in a monitoring process in an embodiment of the present application;

[0056] Figure 9 is a power spectral density and horizontal / vertical spectral ratio curve of measuring point one in an embodiment of the present application;

[0057] Figure 10 is a power spectral density and horizontal / vertical spectral ratio curve of measuring point two in an embodiment of the present application;

[0058] Figure 11 is a power spectral density and horizontal / vertical spectral ratio curve of measuring point three in an embodiment of the present application;

[0059] Figure 12 is a power spectral density and horizontal / vertical spectral ratio curve of measuring point four in an embodiment of the present application;

[0060] Figure 13 is a comparison diagram of global inversion effect and actual measurement based on each measuring point in an embodiment of the present application.

[0061] 1, three-axis accelerometer; 2, coupling nail; 3, measuring point one; 4, measuring point two; 5, measuring point three; 6, measuring point four. DETAILED DESCRIPTION

[0062] It should be noted that the following detailed description is illustrative only, and is intended to provide further description in order to provide a further understanding of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0063] It should be noted that the terms used herein are only intended to describe specific embodiments, and are not intended to limit exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, they indicate the presence of a feature, step, operation, device, component, and / or combination thereof. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0064] Embodiment one

[0065] In a typical embodiment of the present application, a ballast railway bed mechanical state monitoring method based on high-frequency environmental noise is provided, as shown in the following. Figures 1-13As shown, comprising the following steps:

[0066] S1, collect triaxial acceleration signals under high-frequency environmental noise of ballast railway.

[0067] Specifically, a triaxial accelerometer is arranged on the surface of the sleeper box of the ballast railway, and the triaxial vibration acceleration signals of the triaxial accelerometer under high-frequency environmental noise are collected, wherein the high-frequency environmental noise is mainly generated by surrounding traffic load, construction disturbance and other human activities, and the frequency range of the track bed state is 50Hz-1kHz.

[0068] Referring to Figure 2 , the triaxial accelerometer 1 includes three acceleration sensors, and the three acceleration sensors are arranged to form an orthogonal triaxial sensor. In the embodiment, the acceleration sensor is a wide frequency response sensor, such as a piezoelectric accelerometer with a frequency response range of 0.2HZ-1500Hz. The effective frequency response range can cover the high-frequency noise frequency range of 50Hz-1kHz, so as to ensure effective collection of high-frequency environmental noise signals.

[0069] In the embodiment, the triaxial accelerometer 1 is coupled to the surface of the bulk track bed through the coupling nail 2. The length of the coupling nail 2 is 25cm, and the diameter is 2cm. The length is greater than twice the maximum particle size of the bulk ballast, so that the triaxial accelerometer can be stably fixed in the track bed pore.

[0070] S2, integrate the triaxial acceleration signals to obtain triaxial velocity signals, calculate the horizontal / vertical power spectral density based on the triaxial velocity signals, and calculate the average spectral ratio curve based on the horizontal / vertical power spectral density.

[0071] Further, the specific steps are:

[0072] S21, the collected triaxial acceleration signals are simultaneously subjected to detrend and mean value processing, so as to reduce the drift signal of the sensor itself and extract pure vibration noise.

[0073] S22, integrate the processed triaxial acceleration signals to obtain triaxial velocity signals, and use a preset sliding window to perform detrend processing on the curve of the triaxial velocity signals, so as to eliminate the secondary bending and other distortions of the integral results caused by the slow drift of the sensor. In the detrend processing, the sliding window of the velocity signal is set to a length of 100s and a step of 25s. The discontinuity points on the time curve generated by fitting between the windows can be removed by the long-short time algorithm (STA / LTA), and the processing effect is as shown in Figure 4 for comparison.

[0074] S23, remove the windows containing discontinuity points and near-field disturbance noise from the processed triaxial velocity signals to obtain new triaxial velocity signals, and calculate the horizontal / vertical power spectral density of the new triaxial velocity signals.

[0075] Further, the specific processing procedure of step S23 includes:

[0076] S231, using STA / LTA algorithm to remove the windows containing discontinuity points and near-field disturbance noise in the three-axis velocity signal. Since the noise generated by the traffic load usually lasts for 25-30s, the LTA of 60s is selected in the embodiment to avoid filtering the long-time interference noise, and the STA of 0.5s is selected to screen out the transient noise. The ratio of STA / LTA is taken as the maximum value of 2.0 and the minimum value of 0.1 according to the recommended value under the strict condition, which can effectively eliminate the mutation signals in the high-frequency environmental noise and the discontinuity points generated by the sliding window detrending, and extract the effective environmental noise window. The velocity signals before and after the processing are as shown in FIG. 2. Figure 7

[0077] S232, processing the new three-axis velocity signal in the sliding time window mode, dividing the continuous time period into multiple overlapping time periods, and calculating the time-frequency spectrum of the signal in each time period by using the short-time Fourier transform. When the new three-axis velocity signal is processed by using the time-frequency analysis, the short-time Fourier transform (STFT) is used to analyze the time-frequency of the velocity signal in each sampling period. The data is processed in the sliding window mode, the continuous time period (for example, 12 hours) is divided into multiple overlapping time periods, and the time-frequency spectrum of the signal in each time period is calculated. The short-time Fourier transform can divide the non-stationary vibration signal into multiple quasi-stationary frames for frequency spectrum analysis. In the short-time Fourier transform, the velocity signal is first divided into multiple sliding time windows, there is a certain proportion of overlapping time period between adjacent windows, then the short-time Fourier transform is performed on each time period to obtain the time-frequency spectrum of the entire three-axis velocity signal. In the embodiment, the window length is set to 4.096s, the step length is 3.2768s, and the adjacent windows overlap by 20%.

[0078] S232, from the time-frequency spectrum of the STFT result, the horizontal and vertical three channels (two horizontal components and a vertical component) are calculated to obtain the frequency spectrum power density, and after the horizontal vibration power spectrum is synthesized, the ratio curve of the horizontal and vertical power spectrum is calculated by using the following formula (1):

[0079] (1)

[0080] wherein, , : the power spectrum density of the jth window of the two orthogonal horizontal components; : the power spectrum density of the vertical component of the jth window;

[0081] Based on the sliding calculation mode including n windows in total, the sliding step length is i, and the geometric mean spectrum ratio curve calculation formula is:

[0082] ​ (2)

[0083] wherein, n: window quantity; : the ratio of the jth window horizontal and vertical power spectrum.

[0084] In long-term monitoring, after obtaining a newly sampled window, the average spectrum ratio curve is iteratively updated, and the average spectrum ratio curve of the tth (t≥2) update is calculated using the following formula (3):

[0085] (3)

[0086] wherein, n: window quantity; i: sliding step.

[0087] As shown in Figure 8 , to realize real-time sliding update, the following recursive iteration formula is used to calculate the sliding window:

[0088] (4)

[0089] S233, the log bandwidth is used to smooth the data of the curve after each update to reduce the burr fluctuation of the spectrum ratio curve.

[0090] S3, based on the prior information of the track bed and the roadbed, an initial basic structure model is constructed, and based on the initial basic structure model, a global inversion algorithm is used to invert the average spectrum ratio curve to obtain the shear wave velocity of the track bed, and the mechanical state of the track bed is judged according to the shear wave velocity of the track bed.

[0091] Further, the prior information of the track bed and the roadbed includes the layered thickness and material information of the track bed and the roadbed. The initial basic structure model includes multiple initial parameters such as layered thickness, shear wave velocity, longitudinal wave velocity, Poisson's ratio and density.

[0092] Further, for the spectrum ratio curve of each iterative update, based on the shear wave velocity of the track bed obtained by the initial inversion, a local optimization algorithm is used for fast inversion to quickly update the mechanical parameters of the track bed and monitor the mechanical state change of the track bed. In this embodiment, the global inversion uses the Monte Carlo algorithm, and the local optimization inversion uses the downhill method.

[0093] Further, according to the theoretical basis formula, the shear wave velocity and the shear modulus, and the shear modulus and the elastic modulus have the following conversion relationship:

[0094] (5)

[0095] (6)

[0096] According to formulas (5) and (6), the quasi-static elastic modulus and shear modulus of the ballast track bed can be calculated.

[0097] Embodiment Two

[0098] The embodiment provides an implementation process of the ballast railway bed mechanical state monitoring method based on high-frequency ambient noise of Embodiment One applied to railway bed mechanical state monitoring, referring to Figures 2-13 .

[0099] As shown in Figure 2 , the embodiment of the application adopts a piezoelectric acceleration sensor with three frequency response ranges of 0.2-1500 Hz to form a three-axis accelerometer 1, the three-axis accelerometer 1 is coupled with the surface of the granular bed through a coupling nail 2, the length of the coupling nail 2 is 25 cm and the diameter is 2 cm, the length is greater than twice the maximum particle size of the granular ballast, and the coupling nail 2 can be stably fixed in the bed pore.

[0100] In the embodiment of the application, a dynamic acquisition instrument is used to upload the sensor micro-vibration electrical signal to a work computer for continuous recording at a sampling frequency of 2 kHz, and real-time calculation and processing are performed.

[0101] The embodiment takes a ballast railway test line as a test object, the line is located in a suburban area during the test, there is no operating vehicle on the line, and facilities including a subway, a high-speed railway line, a general-speed railway line and a city expressway exist within a range of 2 km around the line, the pre-site investigation shows that the ballast gradation of the line is seriously deteriorated and there is a sign of fine-grained soil intrusion, the thickness of the granular bed layer is about 0.4 m, the surface layer of the subgrade is filled by the fine-grained soil mixed with 10% lime, the thickness is not less than 0.4 m, and the bottom layer of the subgrade is replaced by medium-coarse sand. On the basis of the above prior information, the robustness of the inversion process is improved, the initial parameter search range and the number of layers are reasonably expanded, and the local optimal solution or the wrong convergence path is avoided, so that the stability and adaptability of the inversion result are improved, the initial parameters of the ballast railway layer can be preset, the construction of the initial basic structure model is completed, and the specific parameters are shown in Table 1.

[0102] Table 1

[0103]

[0104] Wherein, H is the thickness of each layer of the bed and subgrade, Vp is the shear wave longitudinal wave velocity, Vs is the shear wave transverse wave velocity, Poisson is the Poisson ratio, and Rho represents the mass density of the material.

[0105] As shown in Figure 3 , four measuring points are arranged in the sleeper box in the embodiment, the first measuring point 3 is located at the center of the sleeper box, the second measuring point 4 is 55 cm away from the first measuring point 3 and is the main area of the ballast railway tamping operation, the third measuring point 5 is located at the edge of the sleeper, and the fourth measuring point 6 is located at the bottom of the bed slope and is 50 cm away from the third measuring point 5 horizontally.

[0106] In the embodiments provided by the present application, first, the triaxial acceleration signals are synchronously de-trended and de-meaned, then the triaxial velocity signals are obtained by integration, and the velocity curve is de-trended again by using a preset sliding window. The vertical vibration channel processing of measuring point four 6 in the embodiments is shown in FIG. 6, wherein the sliding window is set to have a length of 100 s and a step of 25 s; the discontinuous points on the time curve generated by fitting between the windows can be removed by the long-short time algorithm (STA / LTA) to remove the windows containing the discontinuous points. Figure 4

[0107] In order to verify the high-frequency environmental noise source generated by human activities in the embodiments, verify the feasibility and reliability of the on-site implementation of the present application, the velocity signal is converted into the time-frequency domain by short-time Fourier transform and matched with the spectral characteristics of the surrounding human activities, and reference is made to FIGS. 7 and 8 to illustrate the typical noise segments in the vertical vibration time-frequency domain of measuring point four 6. Figure 5 Figure 6

[0108] The rail transit load is a periodic wheel-rail dynamic force, and the typical characteristics of the noise generated thereby are wideband equidistant line spectrum, the first two peak frequency distributions are in the range of 0-50 Hz and 200-300 Hz, the former is mainly generated by the vertical bending vibration of the rail structure, the body resonance, the wheel-rail contact noise and the vibration of the fastener system; the latter is mainly generated by the wheel-rail contact disorder (such as rail corrugation, weld, etc.). Meanwhile, the amplitude and frequency of the signal center part are lower than those when the train approaches and departs, as shown in FIG. 9, at this time, the nearest distance between the test site and the surrounding operating rail line in the embodiments is about 810 m, and the time-frequency characteristics shown in FIG. 10 during the test contain the rail transit load noise generated when the train passes. Figure 5 Figure 5

[0109] The ground vehicle load is a non-periodic random tire-pavement dynamic force, and the excitation frequency is mainly generated by factors such as pavement roughness, tire stiffness and vehicle speed, wherein the noise in the low frequency range (5-30 Hz) is mainly generated by the vibration of the vehicle suspension system, the body modal vibration during driving; the noise in the medium frequency range (30-100 Hz) is mainly generated by factors such as pavement unevenness wavelength, vehicle speed and engine vibration; and the noise in the high frequency range (>100 Hz) is mainly generated by factors such as pavement micro-texture excitation and brake disc vibration. As shown in FIG. 11, at this time, the nearest distance between the test site and the surrounding ground expressway in the embodiments is about 17 m, and the time-frequency characteristics shown in FIG. 12 during the test contain the ground traffic load generated when the multiple types of vehicles on the bidirectional three-lane pass. Figure 6 Figure 6

[0110] ​​​​​​​The high-frequency noise source in the environment is determined by comparing the time-frequency spectrum characteristics, the vibration source directivity, and monitoring by converting the acceleration signal to the sound pressure signal. In addition, the high-frequency environmental noise generated by human activities in the embodiment also includes peripheral construction load, near-field pedestrian disturbance, etc. Since the above factors are not the dominant noise source in the test, this embodiment will not be described again.

[0111] In order to eliminate the sudden signal in the high-frequency environmental noise and the discontinuity generated by the sliding window detrending, the STA / LTA algorithm is used for rapid screening in this embodiment. Since the noise generated by the traffic load usually lasts for 25-30 s, the LTA of 60 s length is selected to avoid the failure to filter the long-time interference noise; the STA of 0.5 s length is selected to exclude transient noise. The ratio is taken as the maximum value of 2.0 and the minimum value of 0.1 according to the recommended value under strict conditions. Figure 7 Fig. 6 shows the effective environmental noise window extracted by the STA / LTA algorithm of the measuring point four 6 based on the above parameter setting.

[0112] In the monitoring process, the power spectrum density of the horizontal and vertical three channels of the above four measuring points is calculated synchronously, and the window length of 2 12 sampling points is set to 4.096 s, the window overlap is set to 20% Tukey window function for short-time Fourier transform, and the ratio curve of the horizontal and vertical power spectrum is calculated based on formula (7):

[0113] (7)

[0114] wherein, , : the power spectrum density of the jth window of the two orthogonal horizontal components; : the vertical component power spectrum density of the jth window.

[0115] Based on the sliding calculation method containing n windows in total, the sliding step is i, and the geometric mean spectrum ratio curve calculation formula of the tth update is:

[0116] (8)

[0117] wherein, n: window number; i: sliding step; : the ratio of the horizontal and vertical power spectrum of the jth window.

[0118] As Figure 8 shown, to realize real-time sliding update, the following recursive iteration formula is used for calculation:

[0119] (9)

[0120] And the log bandwidth smoothing processing is conducted on each updated curve to reduce burr fluctuation of the spectral ratio curve.

[0121] The results of the total time length of 12h are recorded, and the results are referred to Figures 9-12 , wherein, Figure 9 In (a) (b) (c) (d), the vertical channel spectral ratio curve, the horizontal north-south channel spectral ratio curve, the horizontal east-west channel spectral ratio curve and the H / V spectral ratio curve of the measuring point one are respectively; Figure 10 In (a) (b) (c) (d), the vertical channel spectral ratio curve, the horizontal north-south channel spectral ratio curve, the horizontal east-west channel spectral ratio curve and the H / V spectral ratio curve of the measuring point two are respectively; Figure 11 In (a) (b) (c) (d), the vertical channel spectral ratio curve, the horizontal north-south channel spectral ratio curve, the horizontal east-west channel spectral ratio curve and the H / V spectral ratio curve of the measuring point three are respectively; Figure 12 In (a) (b) (c) (d), the vertical channel spectral ratio curve, the horizontal north-south channel spectral ratio curve, the horizontal east-west channel spectral ratio curve and the H / V spectral ratio curve of the measuring point four are respectively. In the H / V spectral ratio method, the high-frequency component and the low-frequency component can be divided according to the ratio of the surface wave wavelength to the stratum thickness. When the wavelength is less than or equal to the stratum thickness, the high-frequency component is mainly used, and the wavelength is more sensitive to the layered structure. At this time, the surface wave and the body wave have energy components. When the wavelength is much larger than the stratum thickness, the low-frequency component is mainly used, and the surface wave base step (especially the Rayleigh wave) is the dominant component. By analyzing the power spectrum of each channel of the track bed in the embodiment, it is judged that the track bed layer is mainly in the surface wave base step mode.

[0122] The global inversion algorithm is adopted to invert the initial monitoring obtained horizontal / vertical spectral ratio curve to obtain the track bed shear wave velocity; the spectral ratio curve updated by each iteration is based on the initial inversion result, and the local optimization algorithm is used for rapid inversion to update and obtain the mechanical parameter change of the track bed.

[0123] Since the site in the embodiment has no train operation for a long time, the track bed state is considered to be unchanged within the monitoring time span, so the geometric mean spectral ratio curve calculated by the whole window is taken as an example, the Monte Carlo algorithm is used for iterative inversion based on the base step mode of the surface wave to obtain the basic structure model m containing the track bed layer, wherein the inversion objective function is as follows:

[0124] (10)

[0125] In the formula, E(m): the objective function value of the model m; j: the index of the frequency sampling point; : the observed HVSR spectral ratio value of the jth frequency point, which comes from the measured data; : the HVSR spectral ratio value of the jth frequency point, which is obtained from the model m based on the diffusion field forward modeling; : the standard deviation of the jth frequency point.

[0126] The calculation of the above inversion objective function involves the diffusion field theory, the core idea of which is that in the diffusion wave field, the autocorrelation and cross-correlation of displacement signals can be represented by the imaginary part of the Green function. When the source and the receiving point coincide, the displacement autocorrelation reflects the energy density in each direction, and the H / V spectrum ratio is the ratio of the sum of the energy density in the horizontal two directions to the square root of the energy density in the vertical direction. In theory, H / V can be directly written as the ratio of the imaginary part of the Green function:

[0127]

[0128] The Green function is determined by the geometric structure and physical parameters of the medium. Here, it is assumed that the site is a horizontally layered, homogeneous, isotropic, and elastic half-space. In forward calculation, the Green function is integrated in the radial wave number domain, and is decomposed into two parts: surface wave (Rayleigh wave, Love wave) and body wave (P wave, S wave):

[0129] Surface wave part: Rayleigh and Love wave modes are extracted by analytical method (using dispersion curve and pole position).

[0130] Body wave part: Numerically integrated in a limited wave number interval.

[0131] The ellipticity of Rayleigh wave and the response amplitude of each mode are determined by the dispersion curve. Finally, the imaginary parts of the Green functions in the horizontal and vertical directions are combined to obtain the theoretical H / V curve corresponding to the layered model.

[0132] Since this part of the content belongs to the basic knowledge of the field of geophysical prospecting, and is not the core technology content of the present application, it will not be described here.

[0133] The present embodiment judges the inversion effect by variance, sets the initial random model number to 50000; allows low-velocity regions to appear in the strata; takes the regularization factor as 0.001; sets the initial perturbation range of Monte Carlo inversion as 40%, and gradually reduces the range with the convergence effect of inversion until the final inversion result tends to be stable. The HV curves in the top 5% error range are compared with the measured values, as shown in Figs. (a), (b), (c) and (d), the HV curves obtained by inversion of the four measuring points one, two, three and four substantially coincide with the measured curves in shape, and the optimal solution and the average value of the top 5% error of all measuring points are summarized as shown in Table 2: Figure 13

[0134] Table 2

[0135]

[0136] Wherein, H is the thickness of the layered, E represents the elastic modulus, and G represents the shear modulus. ​

[0137] According to the prior research in the field:

[0138] Clean ballast: the elastic modulus E is 210-275Mpa;

[0139] Dirty ballast: the elastic modulus E is in the range of 345-380MPa;

[0140] Dirty ballast under wet conditions: the elastic modulus E is in the range of 135-170MPa.

[0141] According to the inversion results and the prior standards in the field, it can be judged that the mechanical properties of the track bed at measuring point two 4, measuring point three 5 and measuring point four 6 are significantly higher than the normal value, and the measured ballast may have characteristics such as local gradation deterioration and dirtiness, which is consistent with the actual ballast gradation deterioration and the state of fine soil invasion.

[0142] In the application of long-term monitoring of the mechanical state of the field track bed, the spectral ratio curve updated each time in the subsequent iteration is based on the initial inversion result, and a local optimization algorithm is used for rapid inversion to update and obtain the change of the mechanical parameters of the track bed.

[0143] In another typical embodiment of the application, a mechanical state monitoring system for a ballasted railway track bed based on high-frequency environmental noise is provided, which comprises:

[0144] A signal acquisition module is used to acquire triaxial acceleration signals under high-frequency environmental noise of the ballasted railway.

[0145] A time-frequency processing module is used to integrate the triaxial acceleration signals to obtain triaxial velocity signals, calculate horizontal / vertical power spectral density based on the triaxial velocity signals, and calculate the average spectral ratio curve based on the horizontal / vertical power spectral density.

[0146] An inversion evaluation module is used to construct an initial basic structure model based on the prior information of the track bed and the subgrade, perform inversion on the average spectral ratio curve based on the initial basic structure model using a global inversion algorithm, obtain the shear wave velocity of the track bed, and determine the mechanical state of the track bed according to the shear wave velocity of the track bed.

[0147] On the other hand, the application also provides an electronic device comprising a memory and a processor, wherein the memory is used to store a program; the processor is coupled with the memory and is used to execute the program stored in the memory to complete the steps in the mechanical state monitoring method for a ballasted railway track bed based on high-frequency environmental noise provided by the embodiment one.

[0148] In yet another aspect, the present application also provides a non-transitory computer readable storage medium for storing a computer program readable, which, when executed by a processor, is capable of completing the steps of the ballast railway track bed mechanical state monitoring method based on high-frequency environmental noise provided by the first embodiment.

[0149] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made 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 monitoring the mechanical state of ballasted railway track bed based on high-frequency environmental noise, characterized in that, Includes the following steps: S1. Collect triaxial acceleration signals under high-frequency environmental noise of ballast railway; S2. Integrate the triaxial acceleration signal to obtain a triaxial velocity signal, calculate the horizontal / vertical power spectral density based on the triaxial velocity signal, and calculate the average spectral ratio curve based on the horizontal / vertical power spectral density; S3. Construct an initial foundation structure model based on prior information of the track bed and subgrade. Based on the initial foundation structure model, use a global inversion algorithm to invert the average spectral ratio curve to obtain the track bed shear wave velocity. Determine the mechanical state of the track bed based on the track bed shear wave velocity.

2. The method for monitoring the mechanical state of ballasted railway track bed based on high-frequency environmental noise as described in claim 1, characterized in that, In step S1, the frequency range of the high-frequency environmental noise that reflects the track bed condition is 50Hz~1kHz.

3. The method for monitoring the mechanical state of ballasted railway track bed based on high-frequency environmental noise as described in claim 1, characterized in that, In step S2, the triaxial acceleration signal is integrated to obtain the triaxial velocity signal, and the specific steps for calculating the horizontal / vertical power spectral density based on the triaxial velocity signal are as follows: S21. Perform detrending and mean-removing processing on the triaxial acceleration signal; S22. Integrate the processed triaxial acceleration signal to obtain a triaxial velocity signal, and perform detrending processing on the triaxial velocity signal; S23. Remove the window containing discontinuities and near-field disturbance noise from the processed triaxial velocity signal to obtain a new triaxial velocity signal, and calculate the horizontal / vertical power spectral density of the new triaxial velocity signal.

4. The method for monitoring the mechanical state of ballasted railway track bed based on high-frequency environmental noise as described in claim 3, characterized in that, In step S22, a preset sliding window is used to perform detrending processing on the triaxial velocity signal. The sliding window is set to a length of 100s and a step size of 25s.

5. The method for monitoring the mechanical state of ballasted railway track bed based on high-frequency environmental noise as described in claim 3, characterized in that, In step S23, the specific steps for calculating the horizontal / vertical power spectral density of the new triaxial velocity signal and iteratively calculating the average spectral ratio curve from the horizontal / vertical power spectral density are as follows: The new triaxial velocity signal is processed using a sliding time window method, dividing the continuous time period into multiple overlapping time periods, and the time spectrum of the signal is calculated by using short-time Fourier transform for each time period. The spectral power density in three directions is obtained from the time spectrum. After synthesizing the horizontal vibration power spectrum, the ratio curve of the horizontal to the vertical power spectrum is calculated using the following formula: in, , : The power spectral density of the j-th window of two orthogonal horizontal components; : The vertical component power spectral density of the j-th window; The average spectral ratio curve is calculated using the following formula: Where n: number of windows; : The ratio of the horizontal to the vertical power spectrum of the j-th window.

6. The method for monitoring the mechanical state of ballasted railway track bed based on high-frequency environmental noise as described in claim 1, characterized in that, In step S2, after obtaining the newly sampled window, the average spectral ratio curve is iteratively updated, and the average spectral ratio curve of the t-th update is calculated using the following formula: Where n: number of windows; i: sliding step size; The sliding window is calculated and updated in real time using the following recursive iterative formula: 。 7. The method for monitoring the mechanical state of ballasted railway track bed based on high-frequency environmental noise as described in claim 1, characterized in that, In step S3, for the spectral ratio curve updated in each iteration, the shear wave velocity of the track bed obtained from the initial inversion is used to perform a fast inversion using a local optimization algorithm to update the track bed mechanical parameters; the global inversion algorithm uses the Monte Carlo algorithm, and the local optimization inversion uses the downslope method.

8. A monitoring system for the mechanical state of ballasted railway track bed based on high-frequency environmental noise, characterized in that, include: The signal acquisition module is used to acquire triaxial acceleration signals under high-frequency environmental noise conditions on ballasted railways. The time-frequency processing module is used to integrate the triaxial acceleration signal to obtain a triaxial velocity signal, calculate the horizontal / vertical power spectral density based on the triaxial velocity signal, and calculate the average spectral ratio curve based on the horizontal / vertical power spectral density. The inversion evaluation module is used to construct an initial basic structure model based on prior information of the track bed and subgrade. Based on the initial basic structure model, a global inversion algorithm is used to invert the average spectral ratio curve to obtain the track bed shear wave velocity. The mechanical state of the track bed is determined based on the track bed shear wave velocity.

9. An electronic device comprising a memory and a processor, characterized in that, The memory is used to store a program, and the processor is coupled to the memory to execute the program stored in the memory to complete the steps in the method for monitoring the mechanical state of ballast railway track bed based on high-frequency environmental noise as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium for storing a readable computer program, characterized in that, When executed by a processor, the computer program is able to complete the steps in the method for monitoring the mechanical state of ballast track bed based on high-frequency environmental noise as described in any one of claims 1-7.

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