CN115964615A - Track smoothness evaluation method based on centerline point cloud data - Google Patents

Track smoothness evaluation method based on centerline point cloud data Download PDF

Info

Publication number
CN115964615A
CN115964615A CN202211482834.1A CN202211482834A CN115964615A CN 115964615 A CN115964615 A CN 115964615A CN 202211482834 A CN202211482834 A CN 202211482834A CN 115964615 A CN115964615 A CN 115964615A
Authority
CN
China
Prior art keywords
track
data
optimal
frequency
follows
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202211482834.1A
Other languages
Chinese (zh)
Inventor
刘长睿
董振川
金卫锋
谭社会
杨兴旺
胡荣耀
尚金光
罗保林
白晓明
张献州
刘龙
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southwest Jiaotong University
Original Assignee
Southwest Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southwest Jiaotong University filed Critical Southwest Jiaotong University
Priority to CN202211482834.1A priority Critical patent/CN115964615A/en
Publication of CN115964615A publication Critical patent/CN115964615A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

  • Length Measuring Devices With Unspecified Measuring Means (AREA)

Abstract

The invention discloses a track smoothness evaluation method based on centerline point cloud data, which comprises the following steps of: acquiring track line shape data, acquiring transverse deviation and vertical deviation of the left track and the right track relative to a designed line shape, and eliminating abnormal data to obtain original data; setting an interception wavelength range, and filtering noise in original data by using a filter to obtain processed track irregularity data; VMD multi-scale decomposition is carried out on the track irregularity data to obtain a plurality of IMF components; performing parameter optimization on K and alpha components in the VMD by using a PSO algorithm, and performing VMD decomposition according to the optimization parameters to obtain a plurality of optimal IMF components; and performing Hilbert transformation on the plurality of IMF optimal components to obtain the unsmooth instantaneous energy density distribution of the track, and evaluating the smoothness of the track. The method is suitable for the track data collected under most track traffic scenes, and provides technical support for improving the track maintenance efficiency.

Description

Track smoothness evaluation method based on centerline point cloud data
Technical Field
The invention belongs to the technical field of linear measurement data processing of railway and subway rail engineering, and particularly relates to a rail smoothness evaluation method based on centerline point cloud data.
Background
The track is the basis of train operation, and in the long-term high-speed running process of train, the track receives the influence of factors such as interaction force and external environment between the wheel rail for the ride comfort of track becomes poor. The actual geometry, spatial position and dimensions of the track deviate from the ideal position of the designed track or track, which is the track irregularity. The wheel rail system is disturbed by the irregularity of the rail and vibrates randomly, so that riding comfort and stability are reduced, rail structural components and train structural components are damaged, the irregularity of the rail is further worsened, if the track can not be maintained timely, rail traffic safety accidents are likely to be caused, and the high smoothness of the maintained rail becomes one of the core problems of various railway offices and subway companies.
The current track smooth state detection is divided into two modes of static track irregularity detection and dynamic track irregularity detection according to different detection means. The static detection means that track geometric states are detected by using track measuring instruments, a track gauge, a chord line and other detection tools or equipment in a time period when no train runs. However, when static detection data is processed, a string measurement method is mostly adopted to process the static detection data, but a measurement datum line of the string measurement method changes along with changes of track height and track direction to generate a false waveform, so that a real track irregularity state is difficult to reflect.
Disclosure of Invention
Aiming at the defects in the prior art, the method for evaluating the smoothness of the track based on the central line point cloud data solves the problem that track measurement methods such as a track geometric state measuring instrument, a railway track inertia measuring instrument, three-dimensional laser scanning mobile measurement and high-precision GNSS dynamic track measurement are difficult to reflect the irregularity state of the track.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: the method for evaluating the smoothness of the track based on the center line point cloud data comprises the following steps:
s1, collecting track linear data, acquiring transverse deviation and vertical deviation of a left track and a right track relative to a designed linear shape, and eliminating abnormal data to obtain original data;
s2, setting a cut wavelength range, and filtering noise in original data by using a filter to obtain processed track irregularity data;
s3, VMD multi-scale decomposition is carried out on the track irregularity data to obtain a plurality of IMF components;
s4, performing parameter optimization on the IMF components by using a PSO algorithm, and performing VMD decomposition according to the optimization parameters to obtain a plurality of optimal IMF components;
and S5, performing Hilbert transformation on the optimal IMF components to obtain the unsmooth instantaneous energy density distribution of the track, and evaluating the smoothness of the track.
Further, the method comprises the following steps: in the step S1, the following sub-steps are included:
s11, acquiring the transverse deviation of the left rail and the right rail relative to the design line shaped h Deviation from vertical d e The expression is as follows:
d e ＝d de -d me
d h ＝d dh -d mh
wherein d is de Design elevation coordinates for the center of the line, d me Actually measuring an elevation coordinate for the track; d is a radical of dh Design of transverse coordinates for the center of the line, d mh Actually measuring transverse coordinates for the track;
s12, regarding the transverse deviation and the vertical deviation of the left rail and the right rail as rail direction and height irregularity measurement data of the rails, screening and rejecting abnormal data by adopting a rail change rate method, regarding irregularity data of two adjacent points of the rails exceeding 3 per mill as abnormal data, and removing the abnormal data from the rail line shape data, wherein the expression is as follows:
Figure SMS_1
where Δ s =0.25m, is the data sampling interval and Z (.) is the magnitude of the irregularity.
Further, the method comprises the following steps: in the step S2, the following steps are included:
s21, regarding high-frequency components with the wavelength less than 1m and low-frequency components with the wavelength more than 200m as noise, and filtering the noise through band-pass filtering to obtain dynamic irregularity data;
and S22, carrying out secondary filtering processing on the dynamic irregularity data, setting the cut-off frequency of a lower pass band as 0.005HZ and the cut-off frequency of an upper pass band as 1HZ according to the condition that the spatial wavelength is the reciprocal of the spatial frequency, and obtaining filtered track irregularity data.
The beneficial effects of the above further scheme are: the secondary filtering process can remove noise in the original data as much as possible.
Further: in the step S3, the following sub-steps are included:
s31, defining the number K of modal components and initializing the modal components u k (t), the expression is:
Figure SMS_2
wherein f (t) is an original signal, namely track irregularity data, the independent variable is the mileage, and the dependent variable is the track irregularity component; r (t) is a remainder term;
s32, for each modal component u k (t) performing Hilbert transform to obtain modal component u k (t) single-sided spectrum
Figure SMS_3
Wherein δ (t) is a dirac function, and j is a modal imaginary part;
s33, moving the single-side frequency spectrum of each mode to the center frequency of the single-side frequency spectrum to obtain a center frequency function F (t), wherein the calculation formula is as follows;
Figure SMS_4
wherein, ω is k Is the center frequency of the frequency band,
Figure SMS_5
is a center frequency exponential function;
s34, estimating modal component u by using square L2 norm of demodulation signal for F (t) k (t) bandwidth, whose VMD variation model is as follows:
Figure SMS_6
wherein, { omega } k }＝{ω 1 ,ω 2 ,ω 3 ,...ω K Denotes a center frequency corresponding to each modal component, { u } k }＝{u 1 ,u 2 ,u 3 ,...u K Represents all modal components after decomposition of the signal f (t);
s35, introducing a penalty factor alpha and a Lagrange multiplier lambda (t) to the mode component u k (t) performing extended Lagrange numerical optimization on the VMD variation model of the bandwidth, wherein the extended Lagrange expression is：
Figure SMS_7
S36, solving the variational constraint problem by adopting an alternative method multiplier algorithm, and iteratively updating
Figure SMS_8
λ n+1 Determining the saddle point of the augmented Lagrange expression, and determining the ^ or the fifth of the second optimization problem of the saddle point in the frequency domain through Fourier equidistant transformation>
Figure SMS_9
The expression is as follows:
Figure SMS_10
wherein, ω represents the frequency,
Figure SMS_11
respectively correspond to +>
Figure SMS_12
f (t), lambda (t), device for selecting or keeping>
Figure SMS_13
For the mode component after the f (t) decomposition, i.e. the IMF component>
Figure SMS_14
Is->
Figure SMS_15
The residual amount by wiener filtering.
S37, according to each modal component u k And (t) updating the center frequency by the mode center until the output condition is met, and outputting a plurality of IMF components.
Further: the step S37 center frequency updating method specifically includes:
initialization
Figure SMS_16
Let n have an initial value of 1, and execute a loop cycle: adding 1 to the value of n, if ω is greater than 0, then make the value of n based on the formula in step S36>
Figure SMS_17
Updating and updating omega according to the following formula k ：
Figure SMS_18
According to the following formula
Figure SMS_19
Updating:
Figure SMS_20
repeating the steps until the following conditions are met, and stopping iteration:
Figure SMS_21
wherein n is the cycle number, and e is a natural constant.
The further method has the beneficial effects that: final wiener filtering obtained under multiple cycles
Figure SMS_22
The accuracy of the data in the subsequent steps is ensured, so that the method can reflect the real track irregularity better.
Further: in the step S4, the following sub-steps are included:
s41, initializing and setting parameters of the PSO, and taking the average value of correlation coefficients of adjacent IMF components of the output optimal IMF components multiplied by the average value of bandwidth of each IMF component as a fitness function, wherein the calculation formula of the correlation coefficient R is as follows:
Figure SMS_23
wherein, cov (x) i ,y i ) Is the covariance, σ, of the components x 、σ y Is the variance of the component;
modal component u k The bandwidth calculation formula of each IMF component of (t) is as follows:
Figure SMS_24
wherein, the first and the second end of the pipe are connected with each other,
Figure SMS_25
for resolving the frequency bandwidth of the signal>
Figure SMS_26
Represents energy;
the fitness function is calculated as follows:
Figure SMS_27
in the formula (I), the compound is shown in the specification,
Figure SMS_28
for the average value of the bandwidth of each IMF component, <' > H>
Figure SMS_29
Is the average of the correlation coefficients of adjacent IMF components;
s42, randomly endowing the flight speed K and different positions alpha to each particle in the particle population, wherein the K corresponds to the decomposition number of modal decomposition, and the alpha corresponds to a penalty factor;
s43, taking different [ K, alpha ] values as VMD input parameters, performing signal decomposition, calculating fitness function values of corresponding particles, and updating individual optimal values and group optimal values of K and alpha;
s44, updating the flight speed K and the position alpha of the particles;
s45, the steps S41 to S44 are repeated until the fitness function is minimum, the corresponding K and alpha are the optimal parameter combination, the optimal particle position [ K, alpha ] is output, and then VMD decomposition is carried out by using the optimal [ K, alpha ] to obtain a plurality of final optimal IMF components.
Further: the method for updating the moving speed and the position of the particles in step S44 specifically includes:
initializing a particle swarm in a space, wherein the flight direction and the distance of each particle are determined by an individual fitness value and an adjacent particle fitness value, the particle swarm can continuously iterate until an optimal function solution is searched according to the optimal particle position of the current generation, and the specific mathematical formula is as follows:
Figure SMS_30
wherein the content of the first and second substances,
Figure SMS_31
for the particle flight speed of the ith particle in dimension D, for>
Figure SMS_32
For the D-th particle coordinate of the i-th particle in the D-dimensional space, for>
Figure SMS_33
For the optimal position coordinate of the ith particle in the D-dimensional space for the entire flight, the ^ h>
Figure SMS_34
I =1,2,3 …, N, which is the optimal position coordinate of the particle group in the D-dimension space in the whole flight process; d =1,2,3, …, D; n is the total number of particles; k is the current iteration number; c. C 1 ,c 2 The acceleration constant is more than or equal to 0, and the maximum learning step length is adjusted; eta ∈ [0,1]Is a random number, ω>0 is the inertial weight.
The further method has the beneficial effects that: the specific algorithm of the K, alpha group and the individual optimal value is updated when the fitness function does not meet the requirement, and the updating of the optimal value of a single particle is determined by the individual fitness value and the fitness value of the adjacent particle together, so the group optimal value is needed to be used, and the accuracy of the algorithm is ensured.
Further: the step S5 comprises the following sub-steps:
s51, carrying out inverse Fourier transform on each decomposed optimal IMF component to obtain modal component u k (t) for each u k (t) Hilbert transform is performed to obtain a Hilbert transform result H [ u ] k (t)]The formula is as follows:
Figure SMS_35
wherein tau is a differential operator;
and based on the Hilbert transform result H u k (t)]Structure analysis signal Z k (t) having the formula:
wherein, a k (t) is a function of the amplitude,. Phi., k (t) is a phase function, and the calculation formula is as follows:
Figure SMS_36
Figure SMS_37
s52 according to the phase function phi k (t) calculating the instantaneous frequency ω k (t), the formula is as follows:
Figure SMS_38
s53, according to the instantaneous frequency omega k (t) calculating the Hilbert spectral expression H (omega, t) according to the following formula:
Figure SMS_39
wherein Re represents a real part;
s54, obtaining a time-frequency-amplitude three-dimensional function of a Hilbert spectrum after Hilbert conversion processing, calculating the square of the amplitude, integrating in a frequency domain to obtain the unsmooth instantaneous energy density distribution of the track,
Figure SMS_40
and evaluating the geometric linear smoothness of the track according to the unsmooth instantaneous energy density distribution of the track. The invention has the beneficial effects that:
1. the method is suitable for track linear measurement data in most track traffic scenes, comprises track central line data processing of track geometric state measuring instruments, railway track inertia measuring instruments, three-dimensional laser scanning mobile measurement and high-precision GNSS dynamic track measurement track measuring methods, and has strong universality;
2. the method mainly uses a variational modal decomposition method to establish a track irregularity data processing analysis model, and from two angles of a frequency domain and a mileage domain, the wavelength information and mileage position of track irregularity diseases are mined, and the geometric state of the track is scientifically and reasonably evaluated;
3. the invention provides technical support for improving the maintenance efficiency of the track and formulating the maintenance operation scheme of the track.
Drawings
Fig. 1 is a flow chart provided by an embodiment of the present invention.
FIG. 2 is a comparison of the method with a left high-low instantaneous energy map of static inspection data generated by a chord survey method. Wherein, (a) is the left high and low instantaneous energy diagram of the embodiment of the invention, and (b) is the left high and low instantaneous energy diagram of static inspection data generated by a chord measuring method.
FIG. 3 is a left high and low instantaneous energy plot generated from motion detection data.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, in one embodiment of the present invention, the following steps are included:
s1, acquiring track line shape data, acquiring transverse deviation and vertical deviation of a left track and a right track relative to a designed line shape, and eliminating abnormal data to obtain original data;
s2, setting a cut wavelength range, and filtering noise in original data by using a filter to obtain processed track irregularity data;
s3, VMD multi-scale decomposition is carried out on the track irregularity data to obtain a plurality of IMF components;
s4, performing parameter optimization on the IMF components by using a PSO algorithm, and performing VMD decomposition according to the optimization parameters to obtain a plurality of optimal IMF components;
and S5, carrying out Hilbert transformation on the optimal IMF components to obtain the unsmooth instantaneous energy density distribution of the track, and evaluating the smoothness of the track.
Specifically, in the step S1 of this embodiment, the following sub-steps are included:
s11, acquiring the transverse deviation d of the left rail and the right rail relative to the designed line shape h Deviation from vertical d e The expression is as follows:
d e ＝d de -d me
d h ＝d dh -d mh
wherein d is de Designing the elevation coordinates for the center of the track, d me Actually measuring an elevation coordinate for the track; d dh Design of transverse coordinates for the center of the line, d mh Actually measuring a transverse coordinate for the track;
s12, regarding the transverse deviation and the vertical deviation of the left rail and the right rail as rail direction and height irregularity measurement data of the rails, screening and rejecting abnormal data by adopting a rail change rate method, regarding irregularity data of two adjacent points of the rails exceeding 3 per mill as abnormal data, and removing the abnormal data from the rail line shape data, wherein the expression is as follows:
Figure SMS_41
where Δ s =0.25m, is the data sampling interval, Z (.) is the irregularity amplitude, and i is the track linear data acquisition point.
In this embodiment, the step S2 includes the following sub-steps:
s21, regarding high-frequency components with the wavelength less than 1m and low-frequency components with the wavelength more than 200m as noise, and filtering the noise through band-pass filtering to obtain dynamic irregularity data;
and S22, carrying out secondary filtering processing on the dynamic irregularity data, setting the cut-off frequency of a lower pass band as 0.005HZ and the cut-off frequency of an upper pass band as 1HZ according to the condition that the spatial wavelength is the reciprocal of the spatial frequency, and obtaining filtered track irregularity data.
In this embodiment, the step S3 includes the following sub-steps:
s31, defining the number K of modal components and initializing the modal components u k (t), the expression is:
Figure SMS_42
wherein f (t) is an original signal, namely track irregularity data, the independent variable is mileage, and the dependent variable is a track irregularity component; r (t) is a remainder term;
s32, for each modal component u k (t) carrying out Hilbert transformation to obtain modal component u k (t) single-sided spectrum
Figure SMS_43
Wherein, δ (t) is a Dirac function, and j is a modal imaginary part;
s33, moving the single-side frequency spectrum of each mode to the center frequency of the single-side frequency spectrum to obtain a center frequency function F (t), wherein the calculation formula is as follows;
Figure SMS_44
wherein, ω is k Is the center frequency of the frequency band,
Figure SMS_45
is a center frequency exponential function;
s34, estimating modal component u by using square L2 norm of demodulation signal for F (t) k (t) bandwidth, whose VMD variation model is as follows:
Figure SMS_46
wherein, { omega } k }＝{ω 1 ,ω 2 ,ω 3 ,...ω K Denotes a center frequency corresponding to each modal component, { u } k }＝{u 1 ,u 2 ,u 3 ,...u K Represents all modal components after the decomposition of the signal f (t);
s35, introducing a penalty factor alpha and a Lagrange multiplier lambda (t) to the mode component u k (t) performing augmented Lagrangian numerical optimization on the VMD variational model of the bandwidth, wherein the augmented Lagrangian expression is as follows:
Figure SMS_47
s36, solving the variational constraint problem by adopting an alternative method multiplier algorithm, and iteratively updating
Figure SMS_48
λ n+1 Determining the saddle point of the augmented Lagrange expression, and obtaining the ^ whether the saddle point is based on the quadratic optimization problem in the frequency domain through Fourier equidistant transformation>
Figure SMS_49
The expression is as follows:
Figure SMS_50
/>
wherein ω represents frequency，
Figure SMS_51
Respectively correspond to +>
Figure SMS_52
Fourier transformation of f (t), lambda (t), based on the signal strength of the signal>
Figure SMS_53
For the mode component after the f (t) decomposition, i.e. the IMF component>
Figure SMS_54
Is->
Figure SMS_55
The residual amount by wiener filtering.
S37, according to each modal component u k And (t) updating the center frequency by the modal center until the output condition is met, and outputting a plurality of optimal IMF components.
The center frequency updating method in step S37 specifically includes:
initialization
Figure SMS_56
Let n have an initial value of 1, and execute a loop cycle: adding 1 to the value of n, if ω is greater than 0, then combining ^ based on the formula in step S36>
Figure SMS_57
Updating is carried out, and omega is updated according to the following formula k ：
Figure SMS_58
According to the following formula pair
Figure SMS_59
And (3) updating:
Figure SMS_60
repeating the steps until the following conditions are met, and stopping iteration:
Figure SMS_61
wherein n is the cycle number, and e is a natural constant.
In this embodiment, the step S4 includes the following sub-steps:
s41, initializing and setting parameters of the PSO, and multiplying the average value of correlation coefficients of adjacent IMF components of the output optimal IMF component by the average value of bandwidth of each IMF component to serve as a fitness function, wherein the calculation formula of the correlation coefficient R is as follows:
Figure SMS_62
wherein, cov (x) i ,y i ) Is the covariance, σ, of the components x 、σ y Is the variance of the component;
modal component u k The bandwidth calculation formula of each IMF component of (t) is as follows:
Figure SMS_63
wherein the content of the first and second substances,
Figure SMS_64
for resolving the frequency bandwidth of the signal>
Figure SMS_65
Represents energy;
the fitness function is calculated as follows:
in the formula (I), the compound is shown in the specification,
Figure SMS_66
for the mean value of the bandwidth of the IMF components>
Figure SMS_67
Is the average of the correlation coefficients of adjacent IMF components;
s42, randomly endowing the flying speed K and different positions alpha to each particle in the particle population, wherein the K corresponds to the decomposition number of modal decomposition, and the alpha corresponds to a penalty factor;
s43, taking different [ K, alpha ] values as VMD input parameters, performing signal decomposition, calculating fitness function values of corresponding particles, and updating individual optimal values and group optimal values of K and alpha;
s44, updating the flight speed K and the position alpha of the particles;
and S45, the steps S41 to S44 are repeated until the fitness function is minimum, the corresponding K and alpha are the optimal parameter combination, the optimal particle position [ K, alpha ] is output, and then VMD decomposition is carried out by using the optimal [ K, alpha ] to obtain a plurality of final optimal IMF components.
The method for updating the moving speed and the position of the particles in step S44 is specifically as follows:
initializing a particle swarm in a space, wherein the flight direction and the distance of each particle are determined by an individual fitness value and a neighboring particle fitness value, the particle swarm can continuously iterate until an optimal function solution is searched according to the optimal particle position of the current generation, and the specific mathematical formula of the particle swarm is as follows:
Figure SMS_68
wherein the content of the first and second substances,
Figure SMS_69
for the D-dimension particle flight speed of the i-th particle in the D-dimension space, the->
Figure SMS_70
For the particle coordinate of the ith particle in the D-dimension space, for [, ] H>
Figure SMS_71
For the optimal position coordinate of the ith particle in the D-dimensional space in the whole flight process, the position coordinate is determined according to the position coordinate of the ith particle in the D-dimensional space>
Figure SMS_72
I =1,2,3 …, N, which is the optimal position coordinate of the particle group in the D-dimension space in the whole flight process; d =1,2,3, …, D; n is the total number of particles; k is the current iteration number; c. C 1 ,c 2 The acceleration constant is more than or equal to 0, and the maximum learning step length is adjusted; eta E [0,1]Is a random number, ω>0 is the inertial weight.
The specific algorithm of the K, alpha group and the individual optimal value is updated when the fitness function does not meet the requirement, and the updated optimal value of a single particle in the formula is determined by the individual fitness value and the fitness value of adjacent particles, so the group optimal value is needed.
In the present embodiment, the step S5 includes the following sub-steps:
s51, carrying out inverse Fourier transform on each decomposed optimal IMF component to obtain modal component u k (t) for each u k (t) Hilbert transform is performed to obtain a Hilbert transform result H [ u [ [ u ] k (t)]The formula is as follows:
Figure SMS_73
wherein tau is a differential operator;
and based on the Hilbert transform result H u k (t)]Structure analysis signal Z k (t) having the formula:
wherein, a k (t) is a function of the amplitude,. Phi., k (t) is a phase function, and the calculation formula is as follows:
Figure SMS_74
Figure SMS_75
s52 according to the phase function phi k (t) calculating the instantaneous frequency ω k (t) having the formula:
Figure SMS_76
s53, according to the instantaneous frequency omega k (t) calculating the Hilbert spectral expression H (omega, t) according to the following formula:
Figure SMS_77
wherein Re represents a real part;
s54, obtaining a time-frequency-amplitude three-dimensional function of a Hilbert spectrum after Hilbert transformation processing, calculating the square of the amplitude, integrating in a frequency domain to obtain the instantaneous energy density distribution of the track irregularity,
Figure SMS_78
and evaluating the geometric linear smoothness of the track according to the unsmooth instantaneous energy density distribution of the track.
The track irregularity instantaneous energy spectrum describes the distribution condition of energy along with mileage, and when the energy fluctuation of a space point is large, the track irregularity state at the position is relatively weak; when the energy fluctuation of the spatial point is small, the orbital smoothness here can be considered to be relatively firm. The effect of the present invention will be further described below with reference to a specific experimental example.
In order to verify the effectiveness of the invention, the static rail detection data are respectively tested by adopting a chord measuring method and the method, and the track irregularity calculation results of the two groups of tests are shown in figure 2.
The orbit dynamic inspection data obtained by measuring the orbit of the same mileage section at the same time point is selected to be subjected to data processing based on VMD-PSO, and a generated left high-low instantaneous energy spectrum image is shown in FIG. 3.
As shown in fig. 2 and fig. 3, the difference between the amplitudes after the two methods are used for processing is obvious, the energy density peak value of the Hilbert energy density spectrum of the experimental example of the present invention always appears at both ends, and the energy density peak value of the Hilbert energy density spectrum of the comparative example adopting the chord measurement method always appears at K17+500-K17+700, so that the Hilbert energy density distribution generated based on the horizontal and vertical deviations is closer to the dynamic detection result, which shows that the method for evaluating the smoothness of the track based on the center line point cloud data can reflect the real track irregularity better than the chord measurement method.
In the description of the present invention, it is to be understood that the terms "center", "thickness", "upper", "lower", "horizontal", "top", "bottom", "inner", "outer", "radial", and the like, indicate orientations and positional relationships based on the orientations and positional relationships shown in the drawings, and are used merely for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or an implicit indication of the number of technical features. Thus, features defined as "first", "second", "third" may explicitly or implicitly include one or more of the features.
The track smoothness evaluation method based on the center line point cloud data provided by the invention emphatically establishes a track irregularity data processing analysis model by using a variational modal decomposition method, and from two angles of a frequency domain and a mileage domain, wavelength information and mileage positions of track irregularity diseases are mined, the geometric state of the track is scientifically and reasonably evaluated, and technical support is provided for improving track maintenance efficiency and formulating a track maintenance operation scheme.

Claims (8)

1. A track smoothness evaluation method based on centerline point cloud data is characterized by comprising the following steps:
s1, acquiring track line shape data, acquiring transverse deviation and vertical deviation of a left track and a right track relative to a designed line shape, and eliminating abnormal data to obtain original data;
s2, setting an interception wavelength range, and filtering noise in original data by using a filter to obtain processed rail irregularity data;
s3, performing VMD multi-scale decomposition on the track irregularity data to obtain a plurality of IMF components;
s4, performing parameter optimization on the IMF components by using a PSO algorithm, and performing VMD decomposition according to the optimization parameters to obtain a plurality of optimal IMF components;
and S5, carrying out Hilbert transformation on the optimal IMF components to obtain the unsmooth instantaneous energy density distribution of the track, and evaluating the smoothness of the track.
2. The method for evaluating the smoothness of the track based on the centerline point cloud data as claimed in claim 1, wherein the step S1 comprises the following sub-steps:
s11, acquiring linear data of the track, and acquiring the transverse deviation d of the left track and the right track relative to the designed linear shape h Deviation from vertical d e The expression is as follows:
d e ＝d de -d me
d h ＝d dh -d mh
wherein d is de Design elevation coordinates for the center of the line, d me Actually measuring an elevation coordinate for the track; d dh Design of transverse coordinates for the center of the line, d mh Actually measuring a transverse coordinate for the track;
s12, regarding the transverse deviation and the vertical deviation of the left rail and the right rail as rail direction and height irregularity measurement data of the rails, screening and rejecting abnormal data by adopting a rail change rate method, regarding irregularity data of two adjacent points of the rails exceeding 3 per mill as abnormal data, and removing the abnormal data from the rail line shape data, wherein the expression is as follows:
Figure FDA0003962427000000011
where Δ s =0.25m is the data sampling interval, Z (.) is the irregularity amplitude, and i is the track linear data acquisition point.
3. The method for evaluating the smoothness of the track based on the centerline point cloud data of claim 1, wherein the step S2 comprises the following sub-steps:
s21, regarding high-frequency components with the wavelength less than 1m and low-frequency components with the wavelength more than 200m as noise, and filtering the noise through band-pass filtering to obtain dynamic irregularity data;
and S22, carrying out secondary filtering processing on the dynamic irregularity data, setting the cut-off frequency of a lower pass band as 0.005HZ and the cut-off frequency of an upper pass band as 1HZ according to the condition that the spatial wavelength is the reciprocal of the spatial frequency, and obtaining filtered track irregularity data.
4. The method for evaluating the smoothness of the track based on the centerline point cloud data of claim 1, wherein the step S3 comprises the following sub-steps:
s31, defining the number K of modal components and initializing the modal components u k (t), the expression is:
Figure FDA0003962427000000021
wherein f (t) is an original signal, namely track irregularity data, the independent variable is the mileage, and the dependent variable is the track irregularity component; r (t) is a remainder term;
s32, for each modal component u k (t) carrying out Hilbert transformation to obtain modal component u k (t) single-sided spectrum
Figure FDA0003962427000000022
/>
Wherein, δ (t) is a Dirac function, and j is a modal imaginary part;
s33, moving the single-side frequency spectrum of each mode to the center frequency of each mode to obtain a center frequency function F (t), wherein the calculation formula is as follows;
Figure FDA0003962427000000023
wherein, ω is k Is the center frequency of the frequency band,
Figure FDA0003962427000000024
is a center frequency exponential function;
s34, estimating modal component u by using square L2 norm of demodulation signal for F (t) k (t) a VMD variation model as follows:
Figure FDA0003962427000000031
wherein, { ω k }＝{ω 1 ,ω 2 ,ω 3 ,...ω K Denotes a center frequency corresponding to each modal component, { u } k }＝{u 1 ,u 2 ,u 3 ,...u K Represents all modal components after the decomposition of the signal f (t);
s35, introducing a penalty factor alpha and a Lagrange multiplier lambda (t) to the mode component u k (t) performing augmented Lagrangian numerical optimization on the VMD variational model of the bandwidth, wherein the augmented Lagrangian expression is as follows:
Figure FDA0003962427000000032
s36, solving the variational constraint problem by adopting an alternative method multiplier algorithm, and iteratively updating
Figure FDA0003962427000000033
λ n+1 Determining the saddle point of the augmented Lagrange expression, and obtaining the ^ whether the saddle point is based on the quadratic optimization problem in the frequency domain through Fourier equidistant transformation>
Figure FDA0003962427000000034
The expression is as follows:
Figure FDA0003962427000000035
wherein, ω represents the frequency,
Figure FDA0003962427000000036
respectively correspond to->
Figure FDA0003962427000000037
Fourier transformation of f (t), lambda (t), based on the signal strength of the signal>
Figure FDA0003962427000000038
For the mode component after the f (t) decomposition, i.e. the IMF component>
Figure FDA0003962427000000039
Is->
Figure FDA00039624270000000310
The residual amount by wiener filtering.
S37, according to each modal component u k And (t) updating the center frequency by the modal center until the output condition is met, and outputting a plurality of optimal IMF components.
5. The method for evaluating the smoothness of an orbit based on centerline point cloud data of claim 4, wherein the step S37 of updating the center frequency is as follows:
initialization
Figure FDA0003962427000000041
Let n have an initial value of 1, and execute a loop cycle: adding 1 to the value of n, if ω is greater than 0, then make the value of n based on the formula in step S36>
Figure FDA0003962427000000042
Updating and updating omega according to the following formula k ：
Figure FDA0003962427000000043
According to the following formula
Figure FDA0003962427000000044
Updating:
Figure FDA0003962427000000045
repeating the steps until the following conditions are met, and stopping iteration:
Figure FDA0003962427000000046
wherein n is the cycle number, and e is a natural constant.
6. The method for evaluating the smoothness of the track based on the centerline point cloud data as claimed in claim 4, wherein the step S4 comprises the following sub-steps:
s41, initializing and setting parameters of the PSO, and taking the average value of correlation coefficients of adjacent IMF components of the output optimal IMF components multiplied by the average value of bandwidth of each IMF component as a fitness function, wherein the calculation formula of the correlation coefficient R is as follows:
Figure FDA0003962427000000047
wherein, cov (x) i ,y i ) Is the covariance, σ, of the component x 、σ y Is the variance of the component;
modal component u k The bandwidth formula of each IMF component of (t) is as follows:
Figure FDA0003962427000000048
wherein the content of the first and second substances,
Figure FDA0003962427000000049
for resolving the frequency bandwidth of the signal>
Figure FDA00039624270000000410
Represents energy;
the fitness function is calculated as follows:
Figure FDA00039624270000000411
in the formula (I), the compound is shown in the specification,
Figure FDA0003962427000000051
for the average value of the bandwidth of each IMF component, <' > H>
Figure FDA0003962427000000052
Is the average of the correlation coefficients of adjacent IMF components;
s42, randomly endowing the flying speed K and different positions alpha to each particle in the particle population, wherein the K corresponds to the decomposition number of modal decomposition, and the alpha corresponds to a penalty factor;
s43, taking different [ K, alpha ] values as VMD input parameters, performing signal decomposition, calculating fitness function values of corresponding particles, and updating individual optimal values and group optimal values of K and alpha;
s44, updating the flight speed K and the position alpha of the particles;
s45, the steps S41 to S44 are repeated until the fitness function is minimum, the corresponding K and alpha are the optimal parameter combination, the optimal particle position [ K, alpha ] is output, and then VMD decomposition is carried out by using the optimal [ K, alpha ] to obtain a plurality of final optimal IMF components.
7. The method as claimed in claim 6, wherein the step S44 is specifically performed by updating the moving speed and position of the particles as follows:
initializing a particle swarm in a space, wherein the flight direction and the distance of each particle are determined by an individual fitness value and an adjacent particle fitness value, the particle swarm can continuously iterate until an optimal function solution is searched according to the optimal particle position of the current generation, and the specific mathematical formula is as follows:
Figure FDA0003962427000000053
wherein the content of the first and second substances,
Figure FDA0003962427000000054
for the particle flight speed of the ith particle in dimension D, for>
Figure FDA0003962427000000055
For the particle coordinate of the ith particle in the D-dimension space, for [, ] H>
Figure FDA0003962427000000056
For the optimal position coordinate of the ith particle in the D-dimensional space in the whole flight process,
Figure FDA0003962427000000057
i =1,2,3 …, N, which is the optimal position coordinate of the particle group in the D-dimension space in the whole flight process; d =1,2,3, …, D; n is the total number of particles; k is the current iteration number; c. C 1 ,c 2 The acceleration constant is more than or equal to 0, and the maximum learning step length is adjusted; eta ∈ [0,1]Is a random number, ω>0 is the inertial weight.
8. The method for evaluating the smoothness of the track based on the centerline point cloud data as claimed in claim 4, wherein the step S5 comprises the following sub-steps:
s51, carrying out inverse Fourier transform on each decomposed optimal IMF component to obtain modal component u k (t) for each u k (t) Hilbert transform to HilbertResult of the special transformation H [ u ] k (t)]The formula is as follows:
Figure FDA0003962427000000061
wherein tau is a differential operator;
and based on the Hilbert transform result H u k (t)]Structure analytic signal Z k (t), the formula is as follows:
Figure FDA0003962427000000062
wherein, a k (t) is a function of the amplitude,. Phi., k (t) is a phase function, and the calculation formula is as follows:
Figure FDA0003962427000000063
Figure FDA0003962427000000064
s52 according to the phase function phi k (t) calculating the instantaneous frequency ω k (t), the formula is as follows:
Figure FDA0003962427000000065
s53, according to the instantaneous frequency omega k (t) calculating Hilbert spectrum expression H (omega, t) which is as follows:
Figure FDA0003962427000000066
wherein Re represents a real part;
s54, obtaining a time-frequency-amplitude three-dimensional function of a Hilbert spectrum after Hilbert transformation processing, calculating the square of the amplitude, and integrating in a frequency domain to obtain the instantaneous energy density distribution of the track irregularity:
Figure FDA0003962427000000067
and evaluating the geometric linear smoothness of the track according to the distribution of the unsmooth instantaneous energy density of the track.
CN202211482834.1A 2022-11-24 2022-11-24 Track smoothness evaluation method based on centerline point cloud data Pending CN115964615A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211482834.1A CN115964615A (en) 2022-11-24 2022-11-24 Track smoothness evaluation method based on centerline point cloud data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211482834.1A CN115964615A (en) 2022-11-24 2022-11-24 Track smoothness evaluation method based on centerline point cloud data

Publications (1)

Publication Number Publication Date
CN115964615A true CN115964615A (en) 2023-04-14

Family

ID=87360680

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211482834.1A Pending CN115964615A (en) 2022-11-24 2022-11-24 Track smoothness evaluation method based on centerline point cloud data

Country Status (1)

Country Link
CN (1) CN115964615A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116905297A (en) * 2023-09-12 2023-10-20 大秦铁路股份有限公司大同工务段 Dislocation track homing method, dislocation track homing device, dislocation track homing equipment and readable storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116905297A (en) * 2023-09-12 2023-10-20 大秦铁路股份有限公司大同工务段 Dislocation track homing method, dislocation track homing device, dislocation track homing equipment and readable storage medium
CN116905297B (en) * 2023-09-12 2023-12-12 大秦铁路股份有限公司大同工务段 Dislocation track homing method, dislocation track homing device, dislocation track homing equipment and readable storage medium

Similar Documents

Publication Publication Date Title
CN106021710B (en) Satellite orbit abnormality recognition method before shake based on atmospheric ionization layer parameter
CN113009566B (en) Local earthquake motion prediction model and construction method thereof
CN104792937A (en) Bridge head bump detection evaluation method based on vehicle-mounted gravitational acceleration sensor
CN110285877B (en) Train real-time positioning tracking and speed calculating method based on Spark Streaming
CN114692473B (en) Method for solving response of uncertain axle coupling vibration system
CN104915534A (en) Deformation analysis and decision-making method of electric power tower based on sequence learning
CN115964615A (en) Track smoothness evaluation method based on centerline point cloud data
CN113251914A (en) Surface deformation prediction method combining InSAR technology and long-term memory neural network
CN115758289B (en) Rail wave mill identification method based on multitask learning neural network
CN110457795A (en) High-rise charming appearance and behaviour displacement state appraisal procedure neural network based
CN105588599B (en) The adaptive corrective method of vehicle-mounted mobile measuring system vibration displacement error
CN113415308B (en) Rail corrugation detection method and device
CN110399683A (en) Bridge Impact Coefficient extracting method based on frequency domain amplitude spectrum similitude filtering technique
CN108920766A (en) A kind of Bridge Influence Line recognition methods indicated based on basic function with sparse regularization
CN102519577A (en) Method and system for identifying road surface in a road
Liu et al. Two-level W-ESMD denoising for dynamic deflection measurement of railway bridges by microwave interferometry
CN116678576A (en) Bridge vibration mode correction system based on vehicle vibration signals
JP2000121743A (en) Evaluating method for seismic shake distribution
CN114298106A (en) Characteristic wave identification method in roadbed rolling, rolling state discrimination method and application thereof
CN107679349B (en) A kind of accurate method for building up of high-speed railway vehicle bridge resonance characteristics characterization function
CN110569608B (en) Method and device for determining vibration severity of vehicle-mounted platform
Dumitriu et al. Experimental analysis of vertical vibrations of a railway bogie
Wu et al. Fast calibration for vibration-based pavement roughness measurement based on model updating of vehicle dynamics
Murai et al. Numerical Experiments of Bridge Position Estimation for On-Going Monitoring
Bleakely et al. Autonomous time frequency analysis of wagon body accelerations

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination