CN110728000B - Ballastless track elastic fastener damage identification method - Google Patents

Ballastless track elastic fastener damage identification method Download PDF

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CN110728000B
CN110728000B CN201910934412.5A CN201910934412A CN110728000B CN 110728000 B CN110728000 B CN 110728000B CN 201910934412 A CN201910934412 A CN 201910934412A CN 110728000 B CN110728000 B CN 110728000B
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lossless
elastic fastener
acceleration time
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ballastless track
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CN110728000A (en
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胡琴
袁瑞杰
朱宏平
沈易军
陈晗
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Huazhong University of Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0066—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by exciting or detecting vibration or acceleration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0075—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by means of external apparatus, e.g. test benches or portable test systems

Abstract

The invention belongs to the field of ballastless track detection, and particularly discloses a method for identifying damage of an elastic fastener of a ballastless track. The method specifically comprises the following steps: establishing a lossless finite element model, optimizing the model, and determining the lossless stiffness coefficient of the elastic fastener; measuring actual exciting force data and actual acceleration time-course data; substituting the actual excitation force data into a lossless finite element model to obtain simulated acceleration time-course data; calculating the actual stiffness coefficient of each elastic fastener by using an MCMC method according to the actual acceleration time-course data and the simulated acceleration time-course data; and judging whether the ratio of the actual rigidity coefficient to the lossless rigidity coefficient is 1, if so, the elastic fastener is not damaged, and if not, the elastic fastener is damaged. The method can eliminate the influence of uncertain factors during testing, and only considers the damage condition of the elastic fastener, thereby effectively improving the accuracy of the damage identification of the ballastless track elastic fastener.

Description

Ballastless track elastic fastener damage identification method
Technical Field
The invention belongs to the field of ballastless track detection, and particularly relates to a method for identifying damage of an elastic fastener of a ballastless track.
Background
The high-speed railway track structure at home and abroad mainly adopts two forms: the ballasted track and the ballastless track. The ballastless track structure of the high-speed railway is gradually and widely applied to the track structure of the high-speed railway due to good overall performance, extremely high smoothness and stability during operation.
In a ballastless track structure of a high-speed railway, the elastic fastener is connected with a steel rail and a track plate, plays a role in fixing the steel rail and is an important component in the track structure. Along with the development trend of high-speed overloading of trains, the undersized foundation of the track structure is greatly impacted and vibrated, so that the elastic fastener is damaged and even loosened to different degrees. The vibration of the vehicle and the steel rail can be aggravated by loosening the fastener, the dynamic performance of a vehicle-track coupling system is changed, the comfort and the safety of train operation are greatly influenced, and particularly the speed of the train running is up to 300 km/h. Therefore, it is important to identify the damage of the fastener and maintain the fastener in time.
Currently, in the actual operation of railroads in many cities, the detection of structural damage to the railway is often dependent on visual inspection. The detection to elastic fastener mainly relies on the manual work to investigate along the circuit, and traditional manual work is patrolled and examined the method efficiency lower like this to operating time receives the restriction of skylight time, and there is certain defect in current fastener damage detection technique moreover, and this method can only detect the outward appearance state of fastener, whether disappearance and fracture promptly, can't discern damage position and degree simultaneously accurately.
Disclosure of Invention
Aiming at the defects or improvement requirements in the prior art, the invention provides a method for identifying the damage of the ballastless track elastic fastener, wherein the method is combined with actual excitation force data and a lossless finite element model, can eliminate the influence of uncertain factors, and accurately identifies the damage position and the damage degree of the elastic fastener, so that the method is particularly suitable for the detection of ballastless tracks.
In order to achieve the purpose, the invention provides a method for identifying the damage of an elastic fastener of a ballastless track, which comprises the following steps:
s1, establishing a lossless finite element model and optimizing the lossless finite element model aiming at the ballastless track in a lossless state, and then determining the lossless stiffness coefficient of the elastic fastener;
s2, exciting the ballastless track to be tested, and measuring actual exciting force data and actual acceleration time-course data of each data acquisition point;
s3, substituting the actual excitation force data into the lossless finite element model to obtain simulated acceleration time-course data;
s4, calculating the actual stiffness coefficient of each elastic fastener by using an MCMC method according to the actual acceleration time course data obtained in the step S2 and the simulated acceleration time course data obtained in the step S3;
s5, judging whether the ratio of the actual stiffness coefficient to the lossless stiffness coefficient of each elastic fastener is 1, if so, the elastic fastener is not damaged, and if not, the elastic fastener is damaged.
As a further preference, the step S1 includes the following sub-steps:
s11, exciting the ballastless track in a lossless state to obtain lossless excitation force data and lossless acceleration time-course data of each data acquisition point;
s12, establishing a lossless finite element model for the ballastless track in a lossless state;
s13, substituting the lossless excitation force data obtained in the step S11 into the lossless finite element model to obtain simulated acceleration time-course data;
s14, comparing the simulated acceleration time-course data with the lossless acceleration time-course data obtained in the substep S11, judging whether the error is within a preset range, if so, directly switching to the substep S15, otherwise, correcting the lossless finite element model until the error between the simulated acceleration time-course data and the lossless acceleration time-course data is within the preset range, and then switching to the step S15;
s15 determining the lossless stiffness coefficient of the elastic fastener according to the lossless acceleration time course data obtained in the step S11.
As a further preferred, the specific process of the sub-step S11 is: determining the positions of data acquisition points on a ballastless track, installing a sensor at the corresponding position, selecting any one data acquisition point to apply excitation in a nondestructive state, and obtaining nondestructive excitation force data and nondestructive acceleration time-course data of each data acquisition point through the sensor.
As a further preference, the sensor in the substep S11 is mounted on the steel rail and the rail plate.
As a further preferred method, the method for establishing the lossless finite element model in the sub-step S12 is: the method comprises the steps of carrying out simulation on a steel rail, an elastic fastener, a base plate, cement asphalt mortar, a track plate and a foundation of a ballastless track in a lossless state, carrying out simulation on the relationship between the track plate and the cement asphalt mortar and the relationship between the cement asphalt mortar and the track plate, simulating the elastic fastener by using a spring, and establishing the lossless finite element model.
As a further preference, the step S4 includes the following sub-steps:
s41 calculates the nuclear density function κ at the first sampling, i.e., g ═ 1, using the following equationg：
Figure GDA0002920369900000031
Wherein g is the number of sampling times, NssIs the total number of samples, W(j)Is the weight of the jth sample, N (θ)j(i),σ(j)) Is a Gaussian function;
s42 Kernel density function κ according togUpdating to obtain g ═ grKernel density function of time kgr：
Figure GDA0002920369900000032
A is the coefficient at which the kernel density function is calculated, grIs the iteration number;
s43 calculates the objective fitting function J (θ) using the following equation:
Figure GDA0002920369900000033
wherein J (theta) is a target fitting functionNumber, N0For the total data point, k is the number of data acquisition points, NdIs the total number of data acquisition points, m is the serial number in the data, NSIs the maximum value of the sequence number, N is the number of the stroke, NfIs the total number of taps that are to be taken,
Figure GDA0002920369900000041
actual acceleration time-course data of the mth time step of the kth data acquisition point,
Figure GDA0002920369900000042
the simulation acceleration time-course data of the mth time step of the kth data acquisition point is shown, theta is the rigidity coefficient of the elastic fastener, and | | represents a norm;
s44 kappa obtained according to the step S42grAnd J (θ) obtained in step S43, g ═ g being calculated by the following formularEdge probability density p (θ | D):
Figure GDA0002920369900000043
in the formula, theta is the rigidity coefficient of each elastic fastener, D is actual acceleration time-course data, and c is a normalization constant;
s45 back-calculates the actual stiffness coefficient of the elastic fastener according to p (theta | D) obtained in the step S44.
As a further preference, the number of iterations grThe value of (A) is preferably 10 to 20 times.
Generally, compared with the prior art, the above technical solution conceived by the present invention mainly has the following technical advantages:
1. according to the method, various uncertain factors of the ballastless track can be comprehensively considered by establishing the lossless finite element model, so that the influence of the uncertain factors can be eliminated when the ballastless track to be tested is tested, only the damage condition of the elastic fastener is considered, the precision of damage identification of the elastic fastener of the ballastless track is effectively improved, the damage position and the damage degree of the elastic fastener are determined by the MCMC method, and the method has a prospective effect on later-stage operation of the ballastless track, damage identification, maintenance and reinforcement of the elastic fastener and other works;
2. meanwhile, when a lossless finite element model is constructed, the simulated acceleration time-course data and the lossless acceleration time-course data are compared, so that the error is ensured to be within a preset range, and the accuracy of the lossless finite element model can be effectively improved;
3. particularly, the actual damage stiffness coefficient of the elastic fastener is optimized by a Bayes correction method based on Monte Carlo, and a relatively accurate joint probability density can be obtained by performing iterative optimization on a kernel density function, so that the accuracy of damage identification is ensured, and meanwhile, the detection can be effectively and quickly performed without performing early-stage model selection.
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Fig. 1 is a flow chart of a method for identifying damage to an elastic fastener of a ballastless track provided by the invention;
FIG. 2 is a flowchart of step S4 of the present invention;
FIG. 3 is a schematic diagram of model verification based on time course acceleration data in a preferred embodiment of the present invention;
fig. 4 is a diagram illustrating the distribution of the actual stiffness coefficients calculated by the MCMC method according to the preferred embodiment of the present invention, wherein (a) is an elastic fastening member 1, (b) is an elastic fastening member 2, (c) is an elastic fastening member 3, and (d) is an elastic fastening member 4;
FIG. 5 is a schematic diagram of the comparison of actual excitation force data to model input forces in a preferred embodiment of the present invention;
FIG. 6 is a graph showing the edge probability density distribution of elastic fasteners according to a preferred embodiment of the present invention, wherein (a) is elastic fastener 1, (b) is elastic fastener 2, (c) is elastic fastener 3, and (d) is elastic fastener 4;
FIG. 7 is a schematic plan view of the elastic clip and the track plate according to the preferred embodiment of the present invention;
FIG. 8 is an axial view of the resilient clip and track plate in the preferred embodiment of the invention;
FIG. 9 is a diagram illustrating recognition results in accordance with a preferred embodiment of the present invention;
FIG. 10 is a code flow diagram for the interactive use of the MATLAB, Python language, and ABAQUS in the preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the invention provides a method for identifying damage to an elastic fastener of a ballastless track, which comprises the following steps:
s1, establishing a lossless finite element model and optimizing the lossless finite element model aiming at the ballastless track in a lossless state, and then determining the lossless stiffness coefficient of the elastic fastener;
s2, exciting the ballastless track to be tested, and measuring actual exciting force data and actual acceleration time-course data of each data acquisition point;
s3, substituting the actual excitation force data into the lossless finite element model to obtain simulated acceleration time-course data;
s4, calculating the actual rigidity coefficient of each elastic fastener by using an MCMC (Monte Carlo) method according to the actual acceleration time course data obtained in the step S2 and the simulated acceleration time course data obtained in the step S3;
s5, judging whether the ratio of the actual rigidity coefficient to the lossless rigidity coefficient of each elastic fastener is 1, if so, the elastic fastener is not damaged, and if not, the elastic fastener is damaged.
Further, step S1 includes the following sub-steps:
s11, comprehensively considering the size, the recognition accuracy and economic factors of a ballastless track structure, determining the positions and the number of the sensors mounted on the steel rail, mounting the sensors on the steel rail and a track plate to ensure the accuracy of the result, selecting a certain data acquisition point A on the steel rail in a nondestructive state to carry out dynamic characteristic test, exciting the data, and acquiring nondestructive excitation force data and nondestructive acceleration time-course data of each data acquisition point through the arranged sensors;
s12, carrying out simulation on a steel rail, an elastic fastener, a base plate, cement asphalt mortar, a track plate and a foundation of the ballastless track in a lossless state, carrying out simulation on the interlayer relations between the track plate and the cement asphalt mortar and between the cement asphalt mortar and the track plate, simulating the elastic fastener by using a spring, and establishing a lossless finite element model;
s13, substituting the lossless excitation force data obtained in the step S11 into the lossless finite element model to obtain simulated acceleration time-course data;
s14, comparing the simulated acceleration time-course data with the lossless acceleration time-course data obtained in the substep S11, judging whether the error is within a preset range, if so, directly switching to the substep S15, otherwise, correcting the lossless finite element model until the error between the simulated acceleration time-course data and the lossless acceleration time-course data is within the preset range, and then switching to the step S15;
s15 determining the lossless stiffness coefficient of the elastic fastener according to the lossless acceleration time course data obtained in the step S11.
Further, as shown in fig. 2, step S4 includes the following sub-steps:
s41 calculates the kernel density function κ when g is 1, which is the first sampling, using equation (1)g：
Figure GDA0002920369900000071
Wherein g is the number of sampling times, NssIs the total number of samples, W(j)Is the weight of the jth sample, N (θ)j(i),σ(j)) Is a Gaussian function, θj(i) For the jth sample, σ(j)The variance of the Gaussian function of the jth sample;
s42 Kernel density function kappa according to equation (2)gUpdating to obtain g ═ grKernel density function of time kgr：
Figure GDA0002920369900000072
grFor the number of iterations, g is determined empiricallyrPreferably 10 to 20 times;
s43 calculates an objective fitting function J (θ) using equation (3):
Figure GDA0002920369900000073
wherein J (theta) is an objective fitting function, N0For the total data point, k is the number of data acquisition points, NdIs the total number of data acquisition points, m is the serial number in the data, NSIs the maximum value of the sequence number, N is the number of the stroke, NfIs the total number of taps that are to be taken,
Figure GDA0002920369900000074
actual acceleration time-course data of the mth time step of the kth data acquisition point,
Figure GDA0002920369900000075
the simulation acceleration time-course data of the mth time step of the kth data acquisition point is shown, theta is the rigidity coefficient of the elastic fastener, and | | represents a norm;
s44 kappa obtained according to step S42grAnd J (θ) obtained in step S43, and g-g is calculated using equation (4)rEdge probability density p (θ | D):
Figure GDA0002920369900000081
in the formula, p (θ | D) is a probability that the stiffness coefficient is θ in the case of the field measurement response data D. Theta is the stiffness coefficient of each elastic fastener, D is actual acceleration time-course data, and c is a normalization constant, so that the integral of the probability density on the parameter space is equal to 1;
s45 inversely calculates the actual stiffness coefficient of the elastic fastener from p (theta | D) obtained in step S44.
The invention is further illustrated below with reference to specific examples.
S1, mounting sensors on a steel rail and a railway plate of a ballastless track, as shown in figures 7 and 8, exciting the ballastless track in a lossless state to obtain lossless excitation force data and lossless acceleration time-course data of each data acquisition point, establishing a lossless finite element model aiming at the ballastless track in the lossless state, and referring to figure 3, comparing simulated acceleration time-course data and lossless acceleration time-course data in the lossless finite element model, wherein an error is within a preset range, which indicates that the model has higher accuracy, and then determining a lossless rigidity coefficient of an elastic fastener according to the lossless acceleration time-course data;
s2, exciting the ballastless track to be tested, and measuring actual exciting force data and actual acceleration time-course data of each data acquisition point;
s3, importing the actual acceleration time-course data as target data into mathematic commercial software MATLAB, substituting the actual excitation force data into a lossless finite element model, and obtaining simulated acceleration time-course data;
s4, calculating the actual stiffness coefficient of each elastic fastener by using an MCMC method according to the actual acceleration time course data obtained in the step S2 and the simulated acceleration time course data obtained in the step S3;
more specifically, the present invention is to provide a novel,
Figure GDA0002920369900000082
the error satisfies the mean value of 0 and the variance of kgFig. 10 is a code flow diagram for MATLAB, Python language, and ABAQUS interactive usage, detailed as follows:
constructing a CRTS plate type ballastless track model containing elastic fastener damage on an existing ballastless track model, generating a modeling database INP file, and searching the number of rows of the rigidity coefficient of the damaged elastic fastener in the INP file;
setting a certain number of initial values aiming at the elastic modulus range of the elastic fastener, modifying the parameters of the searched spring corresponding to the simulated elastic fastener in the INP file according to the iteration initial values, and generating a new INP file;
submitting ABAQUS calculation aiming at the newly generated INP file to generate a result database ODB file;
compiling Python language, extracting acceleration time-course data of a preset data acquisition point in an ODB file of a result database, and storing the acceleration time-course data in a TXT file;
calculating the acceleration data of the data acquisition points and preset target data according to the formula (3), wherein J (theta) is a target fitting function, and the probability density distribution probability of the identification result of the elastic fastener can be obtained according to the MCMC Bayesian probability theory method;
fig. 4 is a schematic distribution diagram of an actual stiffness coefficient calculated by the MCMC method, where based on the damage position and degree of the ballastless track elastic fastener identified by the MCMC bayes model correction method, a finite element model having a corresponding damage condition is constructed by using finite element software ABAQUS, an excitation force corresponding to a field is applied, as shown in fig. 5, a field test force is compared with a model input force, and an acceleration time course corresponding to a data acquisition point is extracted by using a Python language;
s5, determining whether the ratio of the actual stiffness coefficient to the lossless stiffness coefficient of each elastic fastener is 1, if yes, the elastic fastener is not damaged, if no, the elastic fastener is damaged, and as a result, as shown in fig. 6 and 9, the ratio of the elastic fastener 4 is 0.65, and the damage degree is 35%.
According to the invention, a Bayesian probability theory method based on MCMC is deeply combined with engineering, and commercial mathematical software MATLAB, Python language and general finite element software ABAQUS are interactively used, so that the damage position and degree of an elastic fastener of a ballastless track structure can be effectively and quickly measured, the corresponding probability distribution can be solved, and the method has a prospective effect on later-stage operation of the track structure and damage maintenance and reinforcement of the ballastless track structure and has a strong engineering application value.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (6)

1. A ballastless track elastic fastener damage identification method is characterized by comprising the following steps:
s1, establishing a lossless finite element model and optimizing the lossless finite element model aiming at the ballastless track in a lossless state, and then determining the lossless stiffness coefficient of the elastic fastener;
s2, exciting the ballastless track to be tested, and measuring actual exciting force data and actual acceleration time-course data of each data acquisition point;
s3, substituting the actual excitation force data into the lossless finite element model to obtain simulated acceleration time-course data;
s4, calculating the actual stiffness coefficient of each elastic fastener by using an MCMC method according to the actual acceleration time course data obtained in the step S2 and the simulated acceleration time course data obtained in the step S3;
s5, judging whether the ratio of the actual stiffness coefficient to the lossless stiffness coefficient of each elastic fastener is 1, if so, the elastic fastener is not damaged, and if not, the elastic fastener is damaged;
wherein the step S4 includes the following sub-steps:
s41 calculates the nuclear density function κ at the first sampling, i.e., g ═ 1, using the following equationg：
Figure FDA0002920369890000011
Wherein g is the number of sampling times, NssIs the total number of samples, W(j)Is the weight of the jth sample, N (θ)j(i),σ(j)) Is a Gaussian function, θj(i) For the jth sample, σ(j)The variance of the Gaussian function of the jth sample;
s42 Kernel density function κ according togUpdating to obtain g ═ grKernel density function of time kgr：
Figure FDA0002920369890000012
A is the coefficient at which the kernel density function is calculated, grIs the iteration number;
s43 calculates the objective fitting function J (θ) using the following equation:
Figure FDA0002920369890000021
wherein J (theta) is an objective fitting function, N0For the total data point, k is the number of data acquisition points, NdIs the total number of data acquisition points, m is the serial number in the data, NSIs the maximum value of the sequence number, N is the number of the stroke, NfIs the total number of taps that are to be taken,
Figure FDA0002920369890000023
actual acceleration time-course data of the mth time step of the kth data acquisition point,
Figure FDA0002920369890000024
the simulation acceleration time-course data of the mth time step of the kth data acquisition point is shown, theta is the rigidity coefficient of the elastic fastener, and | | represents a norm;
s44 kappa obtained according to the step S42grAnd J (θ) obtained in step S43, g ═ g being calculated by the following formularEdge probability density p (θ | D):
Figure FDA0002920369890000022
in the formula, theta is the rigidity coefficient of each elastic fastener, D is actual acceleration time-course data, and c is a normalization constant;
s45 back-calculates the actual stiffness coefficient of the elastic fastener according to p (theta | D) obtained in the step S44.
2. The ballastless track elastic fastener damage identification method of claim 1, wherein the step S1 includes the following substeps:
s11, exciting the ballastless track in a lossless state to obtain lossless excitation force data and lossless acceleration time-course data of each data acquisition point;
s12, establishing a lossless finite element model for the ballastless track in a lossless state;
s13, substituting the lossless excitation force data obtained in the step S11 into the lossless finite element model to obtain simulated acceleration time-course data;
s14, comparing the simulated acceleration time-course data with the lossless acceleration time-course data obtained in the substep S11, judging whether the error is within a preset range, if so, directly switching to the substep S15, otherwise, correcting the lossless finite element model until the error between the simulated acceleration time-course data and the lossless acceleration time-course data is within the preset range, and then switching to the step S15;
s15 determining the lossless stiffness coefficient of the elastic fastener according to the lossless acceleration time course data obtained in the step S11.
3. The ballastless track elastic fastener damage identification method of claim 2, wherein the specific process of the substep S11 is as follows: determining the positions of data acquisition points on a ballastless track, installing a sensor at the corresponding position, selecting any one data acquisition point to apply excitation in a nondestructive state, and obtaining nondestructive excitation force data and nondestructive acceleration time-course data of each data acquisition point through the sensor.
4. The ballastless track elastic fastener damage identification method of claim 3, wherein in the substep S11, the sensor is mounted on the steel rail and the track slab.
5. The method for identifying the damage to the ballastless track elastic fastener according to any one of claims 2 to 4, wherein the method for establishing the lossless finite element model in the substep S12 is as follows: the method comprises the steps of carrying out simulation on a steel rail, an elastic fastener, a base plate, cement asphalt mortar, a track plate and a foundation of a ballastless track in a lossless state, carrying out simulation on the relationship between the track plate and the cement asphalt mortar and the relationship between the cement asphalt mortar and the track plate, simulating the elastic fastener by using a spring, and establishing the lossless finite element model.
6. The ballastless track elastic fastener damage identification method of claim 1, wherein the iteration number g isrThe value of (A) is preferably 10 to 20 times.
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