CN113032907A - Method and system for correcting vehicle shaking disease data deviation based on waveform correlation - Google Patents

Method and system for correcting vehicle shaking disease data deviation based on waveform correlation Download PDF

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CN113032907A
CN113032907A CN202110324213.XA CN202110324213A CN113032907A CN 113032907 A CN113032907 A CN 113032907A CN 202110324213 A CN202110324213 A CN 202110324213A CN 113032907 A CN113032907 A CN 113032907A
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vehicle shaking
waveform data
disease
vehicle
shaking
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CN113032907B (en
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刘仍奎
王福田
白磊
唐源洁
李擎
王志鹏
安茹
常艳艳
庄勇
胡志远
吴霞
韩丛
高嘉蕾
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Beijing Jiaotong University
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Abstract

The invention provides a method and a system for correcting vehicle shaking disease data deviation based on waveform correlation, wherein the method comprises the steps of calculating correlation coefficients between vehicle shaking data waveforms obtained by each historical detection in a set mileage range and vehicle shaking disease data waveforms by acquiring vehicle shaking disease data waveforms corresponding to vehicle shaking disease points to be corrected and vehicle shaking data waveforms corresponding to the mileage range in multiple historical detections, and judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value; if so, judging that the vehicle shaking defect point to be corrected is invalid; if not, judging that the vehicle shaking defect point to be corrected is effective. By the method, effective vehicle shaking disease points can be identified, invalid vehicle shaking disease points are eliminated, the reliability of the detection data is improved, meanwhile, the disease rechecking is not required to be carried out on the site through an instrument, the workload of site personnel is reduced, and the working efficiency is improved.

Description

Method and system for correcting vehicle shaking disease data deviation based on waveform correlation
Technical Field
The invention relates to the field of track state detection data deviation correction, in particular to a method and a system for correcting vehicle shaking disease data deviation based on waveform correlation.
Background
The rolling stock and the rail are coupled vibration systems, and the vertical acceleration and the horizontal acceleration of a car body can represent the geometric unsmooth state of the rail. The line quality inspection instrument obtains the horizontal acceleration and vertical acceleration data (referred to as 'vehicle shaking data') of the vehicle body by measuring the vibration condition of the locomotive and the vehicle in the running process so as to judge the geometric irregularity state of the railway track. The railway line repair rule of the general railway company clearly defines the vehicle body vertical acceleration and horizontal acceleration allowable deviation management value, and the vehicle shaking damage data refers to vehicle body vertical acceleration and horizontal acceleration data exceeding the specified allowable deviation management value.
Different from a large track inspection vehicle, the line quality inspection instrument has the advantages of small volume, low detection cost, high detection frequency and simple and convenient use, and is a real-time on-the-way monitoring means commonly adopted by a railway system. Due to the short detection period, the line quality inspection instrument can generate abundant line vehicle shaking data, for example, the vehicle-mounted line quality inspection instrument can detect the quality state of the track for many times in one day. The proportion of rail vehicle shaking data in rail equipment detection data is large, and the rail vehicle shaking data is one of key data for comprehensively evaluating the rail irregularity state.
The vehicle body shakes while affected by the track irregularity state and the characteristics of the rolling stock: (1) the vertical acceleration and the horizontal acceleration of a vehicle body can exceed threshold values due to self reasons or abnormal operation of the rolling stock, and the problem caused by the geometric overrun of the track is solved, so that corresponding vehicle shaking disease data are false alarm data, measurement deviation exists, and the vehicle shaking disease data need to be removed from detection data. (2) The geometric irregularity of the track is a disturbance source of a wheel track system and is a main cause of vibration of rolling stock, and the corresponding car shaking disease data is effective car shaking disease data.
Due to the limitation of the instrument and the influence of the external environment, the current vehicle shaking data of the line quality inspection instrument has the problems of serious mileage deviation and measurement deviation, and the data is frequently misreported, which brings great trouble to the field work. In the actual work of the railway, generally, workers are arranged to go to the site and use other detection instruments such as a rail inspection instrument and the like to recheck the car shaking disease detected by the line quality inspection instrument, judge whether the car shaking disease is a real track geometric overrun disease, determine the accurate disease mileage position and diagnose the corresponding car shaking disease reason. When the worker rechecks the car shaking disease, a great deal of time and energy are needed, so that the working efficiency is reduced, and the maintenance cost is increased. Therefore, it is necessary to provide a method for detecting vehicle shaking accident data with reliable detection data and high working efficiency.
Disclosure of Invention
The invention aims to provide a method and a system for correcting vehicle shaking disease data deviation based on waveform correlation, which can improve the reliability of detection data and improve the working efficiency.
In order to achieve the purpose, the invention provides the following scheme:
a method for correcting vehicle shaking disease data deviation based on waveform correlation is characterized by comprising the following steps:
acquiring a vehicle shaking disease data waveform corresponding to a vehicle shaking disease point to be corrected, wherein the vehicle shaking disease data waveform is a mileage-acceleration value waveform, a mileage range constraint in the vehicle shaking disease data waveform is within a preset mileage range C _ FD before and after the vehicle shaking disease point to be corrected, and an acceleration value in the vehicle shaking disease data waveform is an acceleration measurement value of each measurement point within the mileage range C _ FD constraint;
acquiring a vehicle shaking data waveform corresponding to the vehicle shaking defect point to be corrected in multiple historical detections, wherein the vehicle shaking data waveform is a mileage-acceleration value waveform, a mileage range constraint in the vehicle shaking data waveform is within a preset mileage range before and after the vehicle shaking defect point to be corrected, an acceleration value in the vehicle shaking data waveform is an acceleration measurement value of each measurement point within the mileage range constraint, and a time constraint C _ FT of the historical detections is within a preset time before the measurement time of the vehicle shaking defect point to be corrected;
calculating a correlation coefficient between the vehicle shaking data waveform and the vehicle shaking disease data waveform obtained by each historical detection;
judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value or not;
if so, judging that the vehicle shaking defect point to be rectified is invalid;
and if not, judging that the vehicle shaking defect point to be rectified is effective.
Optionally, the method for correcting the vehicle shaking accident data deviation based on the waveform correlation further includes:
dividing a railway track into a plurality of sections with equal length;
determining the total damage deduction of the section according to the damage grade determined by the peak value of each effective vehicle shaking damage point in the section and the number of the effective vehicle shaking damage points;
and determining the first n sections with higher total disease deduction as weak sections.
Optionally, the total number of the vehicle shaking defect points to be corrected is I, and the ith defect point α in the vehicle shaking defect points to be corrected isiThe corresponding shaking waveform data sequence is
Figure BDA0002993947880000031
Figure BDA0002993947880000032
Indicates the disease point alphaiA vehicle shaking waveform data sequence set in the range of the front and rear C _ FD mileage; collection
Figure BDA0002993947880000033
Wherein n is 2 × C _ FD × f, f is the sampling frequency of the line quality tester,
Figure BDA0002993947880000034
data sequence for representing waveform of vehicle shaking
Figure BDA0002993947880000035
The peak point in (1) is determined,
Figure BDA0002993947880000036
the mileage position of (a) indicates a disease point alphaiThe position of the mileage to be corrected, the ith fault point alpha in the fault points of the shaking vehicleiCorresponding vehicle shaking waveform data
Figure BDA0002993947880000037
Has a mileage range of
Figure BDA0002993947880000038
Optionally, the calculating a correlation coefficient between the vehicle shaking data waveform obtained by each historical detection and the vehicle shaking disease data waveform specifically includes:
obtaining historical P (P is more than or equal to 1 and less than or equal to P) detectionThe method comprises the steps of collecting vehicle shaking waveform data points corresponding to vehicle shaking defect points to be corrected
Figure BDA0002993947880000039
Represents;
using a formula
Figure BDA00029939478800000310
Calculating a correlation coefficient between the vehicle shaking waveform data obtained by the historical p-th detection and the vehicle shaking disease waveform data;
using a formula
Figure BDA00029939478800000311
Obtaining a minimum correlation coefficient;
wherein P represents the total historical detection times of the vehicle shaking defect point to be rectified in the time constraint C _ FT,
Figure BDA00029939478800000312
representing the historical p-th detection obtained vehicle shaking waveform data and the ith disease point alphaiCorrelation coefficient between the corresponding car sloshing fault waveform data, cov (M)0 i,Mp i) Representing variable M0 iAnd Mp iThe covariance of (a) of (b),
Figure BDA00029939478800000313
representing variable M0 i、Mp iMin (-) represents taking the minimum value of the variable (-) and,
Figure BDA00029939478800000314
and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained by each historical detection.
In order to achieve the above object, the present invention further provides a system for correcting a vehicle shaking trouble data deviation based on a waveform correlation, including:
the system comprises a vehicle shaking disease waveform data acquisition module, a vehicle shaking disease waveform data processing module and a vehicle shaking disease waveform data processing module, wherein the vehicle shaking disease waveform data corresponding to a vehicle shaking disease point to be corrected is mileage-acceleration value waveform data, a mileage range constraint in the vehicle shaking disease waveform data is within a preset mileage range C _ FD before and after the vehicle shaking disease point to be corrected, and an acceleration value in the vehicle shaking disease waveform data is an acceleration measurement value of each measurement point within the mileage range constraint;
the historical vehicle shaking waveform data acquisition module is used for acquiring vehicle shaking waveform data corresponding to the vehicle shaking defect point to be corrected in multiple historical detections, wherein the vehicle shaking waveform data is mileage-acceleration value waveform data, a mileage range constraint in the vehicle shaking waveform data is within a preset mileage range C _ FD before and after the vehicle shaking defect point to be corrected, an acceleration value in the vehicle shaking waveform data is an acceleration measurement value of each measurement point within the mileage range constraint, and a time constraint C _ FT of the historical detections is within a preset time before the measurement time of the vehicle shaking defect point to be corrected;
the correlation coefficient calculation module is used for calculating a correlation coefficient between the vehicle shaking waveform data and the vehicle shaking defect waveform data obtained by each historical detection;
the vehicle shaking defect point correcting module is used for judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value or not; if so, judging that the vehicle shaking defect point to be rectified is invalid; and if not, judging that the vehicle shaking defect point to be rectified is effective.
Optionally, the system for correcting vehicle shaking accident data deviation based on the waveform correlation further includes a weak section determination module, which specifically includes:
dividing a railway track into a plurality of sections with equal length;
determining the total damage deduction of the section according to the damage grade determined by the peak value of each effective vehicle shaking damage point in the section and the number of the effective vehicle shaking damage points;
and determining the first n sections with higher total disease deduction as weak sections.
Optionally, the historical vehicle shaking waveform data acquiring module specifically includes:
the total number of the vehicle shaking disease points to be corrected is I, and the ith disease point alpha in the vehicle shaking disease points to be corrected isiThe corresponding shaking waveform data sequence is
Figure BDA0002993947880000041
Figure BDA0002993947880000042
Indicates the disease point alphaiA vehicle shaking waveform data sequence set in the range of the front and rear C _ FD mileage; collection
Figure BDA0002993947880000043
Wherein n is 2 × C _ FD × f, f is the sampling frequency of the line quality tester,
Figure BDA0002993947880000044
data sequence for representing waveform of vehicle shaking
Figure BDA0002993947880000045
The peak point in (1) is determined,
Figure BDA0002993947880000046
the mileage position of (a) indicates a disease point alphaiThe position of the mileage to be corrected, the ith fault point alpha in the fault points of the shaking vehicleiCorresponding vehicle shaking waveform data
Figure BDA0002993947880000047
Has a mileage range of
Figure BDA0002993947880000048
Optionally, the correlation coefficient calculating module specifically includes:
acquiring vehicle shaking waveform data corresponding to the vehicle shaking defect point to be rectified in the historical P-th detection (P is more than or equal to 1 and less than or equal to P), and collecting the vehicle shaking waveform data points
Figure BDA0002993947880000049
Represents;
using a formula
Figure BDA0002993947880000051
Calculating a correlation coefficient between the vehicle shaking waveform data obtained by the historical p-th detection and the vehicle shaking disease waveform data;
using a formula
Figure BDA0002993947880000052
Obtaining a minimum correlation coefficient;
wherein P represents the total historical detection times of the vehicle shaking defect point to be rectified in the time constraint C _ FT,
Figure BDA0002993947880000053
representing the historical p-th detection obtained vehicle shaking waveform data and the ith disease point alphaiCorrelation coefficient between the corresponding car sloshing fault waveform data, cov (M)0 i,Mp i) Representing variable M0 iAnd Mp iThe covariance of (a) of (b),
Figure BDA0002993947880000054
representing variable M0 i、Mp iMin (-) represents taking the minimum value of the variable (-) and,
Figure BDA0002993947880000055
and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained by each historical detection.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to a method and a system for correcting vehicle shaking disease data deviation based on waveform correlation, wherein the method comprises the steps of calculating correlation coefficients between vehicle shaking data waveforms obtained by each historical detection in a set mileage range and vehicle shaking disease data waveforms by acquiring vehicle shaking disease data waveforms corresponding to vehicle shaking disease points to be corrected and vehicle shaking data waveforms corresponding to the mileage range in multiple historical detections, and judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value; if so, judging that the vehicle shaking defect point to be corrected is invalid; otherwise, judging that the vehicle shaking defect point to be corrected is effective. The method can identify the effective vehicle shaking disease points, eliminate the invalid vehicle shaking disease points and improve the reliability of detection data, and meanwhile, the method does not need to carry out the disease rechecking again on site through an instrument, thereby reducing the workload of site personnel and improving the working efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for correcting vehicle shaking disease data deviation based on waveform correlation according to the present invention;
FIG. 2 shows a disease point αiThe overrun evaluation diagram of (1);
FIG. 3 is a schematic diagram of an algorithm principle of the vehicle shaking disease data deviation correction method based on waveform correlation;
fig. 4 is a schematic diagram of the vehicle shaking accident data deviation correction system based on the waveform correlation.
Description of the symbols:
the method comprises the steps of 1-vehicle shaking disease waveform data acquisition module, 2-historical vehicle shaking waveform data acquisition module, 3-correlation coefficient calculation module and 4-vehicle shaking disease point correction module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for correcting vehicle shaking disease data deviation based on waveform correlation, which can improve the reliability of detection data and improve the working efficiency.
The correction of the data deviation refers to the identification of effective vehicle shaking disease points, and false vehicle shaking disease data with measurement deviation caused by the self-reason or abnormal operation of a vehicle body and the like are removed from the original vehicle shaking disease data.
"correlation" means: and the correlation degree of the vehicle shaking disease data waveform corresponding to the vehicle shaking disease point to be corrected and the vehicle shaking data waveform corresponding to the vehicle shaking disease point to be corrected in multiple historical detections within a set mileage range and a given time range.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the method for correcting the vehicle shaking accident data deviation based on the waveform correlation includes the following steps:
step 101: acquiring vehicle shaking disease waveform data corresponding to a vehicle shaking disease point to be corrected, wherein the vehicle shaking disease waveform data is mileage-acceleration value waveform data, the mileage range constraint in the vehicle shaking disease waveform data is within a preset mileage range C _ FD before and after the vehicle shaking disease point to be corrected, and the acceleration value in the vehicle shaking disease waveform data is an acceleration measurement value of each measurement point within the mileage range constraint. The vehicle shaking disease data refers to vehicle body horizontal acceleration and vertical acceleration data which are detected by a line quality detector and exceed a certain management threshold.
Step 102: acquiring vehicle shaking waveform data corresponding to the vehicle shaking defect point to be corrected in multiple historical detections, wherein the vehicle shaking waveform data is mileage-acceleration value waveform data, the mileage range constraint in the vehicle shaking waveform data is within a set mileage range C _ FD before and after the vehicle shaking defect point to be corrected, the acceleration value in the vehicle shaking waveform data is an acceleration measurement value of each measurement point within the mileage range constraint, and the time constraint C _ FT of the historical detections is within a set time before the measurement time of the vehicle shaking defect point to be corrected. Generally, the time constraint C _ FT is within 1-3 days before the latest vehicle shaking damage detection, and the corresponding vehicle shaking damage in the time interval is ensured not to be remedied.
Step 103: and calculating a correlation coefficient between the vehicle shaking waveform data obtained by each historical detection and the vehicle shaking disease waveform data. Because the detection period of the line quality detector is short, the same track irregularity can be repeatedly detected for many times before the defect is not rectified, and the original vehicle shaking waveform data detected for many times has higher similarity.
Step 104: judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value, wherein the set correlation coefficient threshold value gamma isbaseCan be obtained according to expert experience and statistical historical data.
Step 105: if so, judging that the vehicle shaking disease point to be rectified is invalid and the disease point is not caused by the track irregularity disease, and further rejecting the invalid vehicle shaking disease point.
Step 106: and if not, judging that the vehicle shaking defect point to be rectified is effective.
Further, the method for correcting the vehicle shaking accident data deviation based on the waveform correlation relationship further includes:
step 107: the railway track is divided into a plurality of sections with equal length, and the sections can be divided according to the length of 200 meters of each section according to the actual situation.
And determining the total damage deduction of the section according to the damage grade determined by the peak value of each effective vehicle shaking damage point in the section and the number of the effective vehicle shaking damage points.
And determining the first n sections with higher total disease deduction as weak sections, and arranging corresponding maintenance activities in time by workers aiming at the first n weak sections.
Specifically, the vehicle shaking diseases are generally divided into 4 disease grades, and the higher the disease grade value is, the more serious the vehicle shaking diseases are. Suppose that the single 4-grade disease score is 10, the single 3-grade disease score is 5, the single 2-grade disease score is 3, and the single 1-grade disease score is 1. If there are 3 effective vehicle shaking defect points at level 1 and 2 effective vehicle shaking defect points at level 3 in a specific section, the total deduction of the defects in the section is divided into the number of the effective vehicle shaking defect points and the weighted average value of the defect grades determined by the peak value of each effective vehicle shaking defect point: 3 x 1+2 x 5 ═ 13.
More closely, the total number of the vehicle shaking disease points to be corrected is I, and the ith disease point alpha in the vehicle shaking disease points to be corrected isiThe corresponding shaking waveform data sequence is
Figure BDA0002993947880000081
Figure BDA0002993947880000082
Indicates the disease point alphaiA vehicle shaking waveform data sequence set in the range of the front and rear C _ FD mileage; collection
Figure BDA0002993947880000083
Wherein n is 2 × C _ FD × f, f is the sampling frequency of the line quality tester,
Figure BDA0002993947880000084
data sequence for representing waveform of vehicle shaking
Figure BDA0002993947880000085
The peak point in (1) is determined,
Figure BDA0002993947880000086
the mileage position of (a) indicates a disease point alphaiThe position of the mileage to be corrected, the ith fault point alpha in the fault points of the shaking vehicleiCorresponding vehicle shaking waveform data
Figure BDA0002993947880000087
Has a mileage range of
Figure BDA0002993947880000088
Disease point alphaiThe overrun evaluation diagram of (a) is shown in fig. 2.
Preferably, the calculating a correlation coefficient between the vehicle shaking waveform data obtained by each historical detection and the vehicle shaking fault waveform data specifically includes:
acquiring vehicle shaking waveform data corresponding to the vehicle shaking defect point to be rectified in the historical P-th detection (P is more than or equal to 1 and less than or equal to P), and collecting the vehicle shaking waveform data points
Figure BDA0002993947880000089
Represents;
using a formula
Figure BDA00029939478800000810
Calculating a correlation coefficient between the vehicle shaking waveform data obtained by the historical p-th detection and the vehicle shaking disease waveform data;
using a formula
Figure BDA00029939478800000811
Obtaining a minimum correlation coefficient;
wherein P represents the total historical detection times of the vehicle shaking defect point to be rectified in the time constraint C _ FT,
Figure BDA00029939478800000812
representing the historical p-th detection obtained vehicle shaking waveform data and the ith disease point alphaiCorrelation coefficient between the corresponding car sloshing fault waveform data, cov (M)0 i,Mp i) Representing variable M0 iAnd Mp iThe covariance of (a) of (b),
Figure BDA00029939478800000813
representing variable M0 i、Mp iMin (-) represents taking the minimum value of the variable (-) and,
Figure BDA00029939478800000814
representing the vehicle shaking obtained from each historical detectionAnd the minimum correlation coefficient between the waveform data and the vehicle shaking defect waveform data.
Similarly, the calculation steps 101 to 107 are sequentially performed on the I vehicle shaking defect points in the vehicle shaking defect point set to be corrected, and finally, an effective vehicle shaking defect point set B ═ B of the latest detection in a section of mileage can be obtained1,b2,…,bS]And S represents the total number of the effective vehicle shaking disease points detected by the latest line quality inspection instrument.
In order to achieve the above object, as shown in fig. 4, the present invention further provides a system for correcting a vehicle shaking trouble data deviation based on a waveform correlation, wherein the system for correcting a vehicle shaking trouble data deviation based on a waveform correlation comprises: the system comprises a vehicle shaking disease waveform data acquisition module 1, a historical vehicle shaking waveform data acquisition module 2, a correlation coefficient calculation module 3 and a vehicle shaking disease point correction module 4.
The vehicle shaking disease waveform data acquisition module 1 is used for acquiring vehicle shaking disease waveform data corresponding to a vehicle shaking disease point to be corrected, wherein the vehicle shaking disease waveform data is mileage-acceleration value waveform data, a mileage range constraint in the vehicle shaking disease waveform data is within a preset mileage range C _ FD around the vehicle shaking disease point to be corrected, and an acceleration value in the vehicle shaking disease waveform data is an acceleration measurement value of each measurement point within the mileage range constraint.
The historical vehicle shaking waveform data acquisition module 2 is used for acquiring vehicle shaking waveform data corresponding to the vehicle shaking defect point to be corrected in multiple historical detections, wherein the vehicle shaking waveform data is mileage-acceleration value waveform data, a mileage range constraint in the vehicle shaking waveform data is within a set mileage range C _ FD before and after the vehicle shaking defect point to be corrected, an acceleration value in the vehicle shaking waveform data is an acceleration measurement value of each measurement point within the mileage range constraint, and a time constraint C _ FT of the historical detections is within a set time before the measurement time of the vehicle shaking defect point to be corrected.
And the correlation coefficient calculation module 3 is used for calculating a correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained by each historical detection.
The vehicle shaking defect point correcting module 4 is used for judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value or not; if so, judging that the vehicle shaking defect point to be rectified is invalid; and if not, judging that the vehicle shaking defect point to be rectified is effective.
Further, the system for correcting the vehicle shaking accident data deviation based on the waveform correlation relationship further includes: a weak section determination module (not shown) for determining a weak section in a railway track section, specifically comprising:
the railway track is divided into a plurality of sections of equal length.
And determining the total damage deduction of the section according to the damage grade determined by the peak value of each effective vehicle shaking damage point in the section and the number of the effective vehicle shaking damage points.
And determining the first n sections with higher total disease deduction as weak sections.
More further, the historical vehicle shaking waveform data acquisition module 2 specifically includes:
the total number of the fault points of the vehicle to be rectified is I, and the ith fault point alpha in the fault points of the vehicle to be rectifiediThe corresponding shaking waveform data sequence is
Figure BDA0002993947880000101
Figure BDA0002993947880000102
Indicates the disease point alphaiA vehicle shaking waveform data sequence set in the range of the front and rear C _ FD mileage; collection
Figure BDA0002993947880000103
Wherein n is 2 × C _ FD × f, f is the sampling frequency of the line quality tester,
Figure BDA0002993947880000104
data sequence for representing waveform of vehicle shaking
Figure BDA0002993947880000105
The peak point in (1) is determined,
Figure BDA0002993947880000106
the mileage position of (a) indicates a disease point alphaiThe position of the mileage to be corrected, the ith fault point alpha in the fault points of the shaking vehicleiCorresponding vehicle shaking waveform data
Figure BDA0002993947880000107
Has a mileage range of
Figure BDA0002993947880000108
Preferably, the correlation coefficient calculating module 3 specifically includes:
acquiring vehicle shaking waveform data corresponding to the vehicle shaking defect point to be rectified in the historical P-th detection (P is more than or equal to 1 and less than or equal to P), and collecting the vehicle shaking waveform data points
Figure BDA0002993947880000109
Represents;
using a formula
Figure BDA00029939478800001010
Calculating a correlation coefficient between the vehicle shaking waveform data obtained by the historical p-th detection and the vehicle shaking disease waveform data;
using a formula
Figure BDA00029939478800001011
Obtaining a minimum correlation coefficient;
wherein P represents the total historical detection times of the vehicle shaking defect point to be rectified in the time constraint C _ FT,
Figure BDA00029939478800001012
representing the historical p-th detection obtained vehicle shaking waveform data and the ith disease point alphaiCorrelation coefficient between the corresponding car sloshing fault waveform data, cov (M)0 i,Mp i) Representing variable M0 iAnd Mp iThe covariance of (a) of (b),
Figure BDA00029939478800001013
representing variable M0 i、Mp iMin (-) represents taking the minimum value of the variable (-) and,
Figure BDA00029939478800001014
and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained by each historical detection.
The method and the system for correcting the vehicle shaking disease data deviation based on the waveform correlation can be used for correcting the deviation of the vehicle shaking disease data detected at the latest time, realizing the real-time detection of effective vehicle shaking disease points and also correcting the deviation of the vehicle shaking disease data in the past several days.
The invention realizes the integration of data acquisition and deviation correction by integrating the corresponding program on the line quality detector. However, the scope of the present invention is not limited to a line quality tester, a vehicle shaking tester, a vehicle adding tester, a subway operation service detection device, etc., and any device for measuring the vertical acceleration and the horizontal acceleration of the vehicle body of the rolling stock based on the acceleration sensor is suitable.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (8)

1. A method for correcting vehicle shaking disease data deviation based on waveform correlation is characterized by comprising the following steps:
acquiring vehicle shaking disease waveform data corresponding to a vehicle shaking disease point to be corrected, wherein the vehicle shaking disease waveform data is mileage-acceleration value waveform data, a mileage range constraint in the vehicle shaking disease waveform data is within a preset mileage range C _ FD before and after the vehicle shaking disease point to be corrected, and an acceleration value in the vehicle shaking disease waveform data is an acceleration measurement value of each measurement point within the mileage range constraint;
acquiring vehicle shaking waveform data corresponding to the vehicle shaking defect point to be corrected in multiple historical detections, wherein the vehicle shaking waveform data is mileage-acceleration value waveform data, the mileage range constraint in the vehicle shaking waveform data is within a set mileage range C _ FD before and after the vehicle shaking defect point to be corrected, the acceleration value in the vehicle shaking waveform data is an acceleration measurement value of each measurement point within the mileage range constraint, and the time constraint C _ FT of the historical detections is within a set time before the measurement time of the vehicle shaking defect point to be corrected;
calculating a correlation coefficient between the vehicle shaking waveform data obtained by each historical detection and the vehicle shaking disease waveform data;
judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value or not;
if so, judging that the vehicle shaking defect point to be rectified is invalid;
and if not, judging that the vehicle shaking defect point to be rectified is effective.
2. The method for correcting the vehicle shaking trouble data deviation based on the waveform correlation according to claim 1, wherein the method for correcting the vehicle shaking trouble data deviation based on the waveform correlation further comprises:
dividing a railway track into a plurality of sections with equal length;
determining the total damage deduction of the section according to the damage grade determined by the peak value of each effective vehicle shaking damage point in the section and the number of the effective vehicle shaking damage points;
and determining the first n sections with higher total disease deduction as weak sections.
3. The method for correcting the vehicle shaking fault data deviation based on the waveform correlation relationship as claimed in claim 1, wherein the total number of the vehicle shaking fault points to be corrected is I, and the ith fault point α in the vehicle shaking fault points to be corrected isiThe corresponding shaking waveform data sequence is
Figure FDA0002993947870000011
Figure FDA0002993947870000012
Indicates the disease point alphaiA vehicle shaking waveform data sequence set in the range of the front and rear C _ FD mileage; collection
Figure FDA0002993947870000013
Wherein n is 2 × C _ FD × f, f is the sampling frequency of the line quality tester,
Figure FDA0002993947870000021
data sequence for representing waveform of vehicle shaking
Figure FDA0002993947870000022
The peak point in (1) is determined,
Figure FDA0002993947870000023
the mileage position of (a) indicates a disease point alphaiThe position of the mileage to be corrected, the ith fault point alpha in the fault points of the shaking vehicleiCorresponding vehicle shaking waveform data
Figure FDA0002993947870000024
Has a mileage range of
Figure FDA0002993947870000025
4. The method for correcting the vehicle shaking disease data deviation based on the waveform correlation according to claim 3, wherein the calculating of the correlation coefficient between the vehicle shaking waveform data obtained from each historical detection and the vehicle shaking disease waveform data specifically comprises:
acquiring vehicle shaking waveform data corresponding to the vehicle shaking defect point to be rectified in the historical P-th detection (P is more than or equal to 1 and less than or equal to P), and collecting the vehicle shaking waveform data points
Figure FDA0002993947870000026
Represents;
using a formula
Figure FDA0002993947870000027
Calculating a correlation coefficient between the vehicle shaking waveform data obtained by the historical p-th detection and the vehicle shaking disease waveform data;
using a formula
Figure FDA0002993947870000028
Obtaining a minimum correlation coefficient;
wherein P represents the total historical detection times of the vehicle shaking defect point to be rectified in the time constraint C _ FT,
Figure FDA0002993947870000029
representing the historical p-th detection obtained vehicle shaking waveform data and the ith disease point alphaiCorrelation coefficients between the corresponding vehicle shaking defect waveform data,
Figure FDA00029939478700000210
representing variable M0 iAnd Mp iThe covariance of (a) of (b),
Figure FDA00029939478700000211
representing variable M0 i、Mp iMin (-) represents taking the minimum value of the variable (-) and,
Figure FDA00029939478700000212
and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained by each historical detection.
5. The vehicle shaking disease data deviation correcting system based on the waveform correlation is characterized by comprising the following components:
the system comprises a vehicle shaking disease waveform data acquisition module, a vehicle shaking disease waveform data processing module and a vehicle shaking disease waveform data processing module, wherein the vehicle shaking disease waveform data corresponding to a vehicle shaking disease point to be corrected is mileage-acceleration value waveform data, a mileage range constraint in the vehicle shaking disease waveform data is within a preset mileage range C _ FD before and after the vehicle shaking disease point to be corrected, and an acceleration value in the vehicle shaking disease waveform data is an acceleration measurement value of each measurement point within the mileage range constraint;
the historical vehicle shaking waveform data acquisition module is used for acquiring vehicle shaking waveform data corresponding to the vehicle shaking defect point to be corrected in multiple historical detections, wherein the vehicle shaking waveform data is mileage-acceleration value waveform data, a mileage range constraint in the vehicle shaking waveform data is within a preset mileage range C _ FD before and after the vehicle shaking defect point to be corrected, an acceleration value in the vehicle shaking waveform data is an acceleration measurement value of each measurement point within the mileage range constraint, and a time constraint C _ FT of the historical detections is within a preset time before the measurement time of the vehicle shaking defect point to be corrected;
the correlation coefficient calculation module is used for calculating a correlation coefficient between the vehicle shaking waveform data and the vehicle shaking defect waveform data obtained by each historical detection;
the vehicle shaking defect point correcting module is used for judging whether the minimum correlation coefficient in the correlation coefficients is smaller than a set correlation coefficient threshold value or not; if so, judging that the vehicle shaking defect point to be rectified is invalid; and if not, judging that the vehicle shaking defect point to be rectified is effective.
6. The system for correcting vehicle shaking trouble data deviation based on waveform correlation according to claim 5, wherein the system for correcting vehicle shaking trouble data deviation based on waveform correlation further comprises a weak section determination module, specifically comprising:
dividing a railway track into a plurality of sections with equal length;
determining the total damage deduction of the section according to the damage grade determined by the peak value of each effective vehicle shaking damage point in the section and the number of the effective vehicle shaking damage points;
and determining the first n sections with higher total disease deduction as weak sections.
7. The system for correcting vehicle shaking disease data deviation based on waveform correlation according to claim 5, wherein the historical vehicle shaking waveform data acquisition module specifically comprises:
the total number of the vehicle shaking disease points to be corrected is I, and the ith disease point alpha in the vehicle shaking disease points to be corrected isiThe corresponding shaking waveform data sequence is
Figure FDA0002993947870000031
Figure FDA0002993947870000032
Indicates the disease point alphaiA vehicle shaking waveform data sequence set in the range of the front and rear C _ FD mileage; collection
Figure FDA0002993947870000033
Wherein n is 2 × C _ FD × f, f is the sampling frequency of the line quality tester,
Figure FDA0002993947870000034
data sequence for representing waveform of vehicle shaking
Figure FDA0002993947870000035
The peak point in (1) is determined,
Figure FDA0002993947870000036
the mileage position of (a) indicates a disease point alphaiThe position of the mileage to be corrected, the ith fault point alpha in the fault points of the shaking vehicleiCorresponding vehicle shaking waveform data
Figure FDA0002993947870000037
Has a mileage range of
Figure FDA0002993947870000038
8. The system for correcting vehicle shaking disease data deviation based on waveform correlation according to claim 7, wherein the correlation coefficient calculation module specifically comprises:
acquiring vehicle shaking waveform data corresponding to the vehicle shaking defect point to be rectified in the historical P-th detection (P is more than or equal to 1 and less than or equal to P), and collecting the vehicle shaking waveform data points
Figure FDA0002993947870000039
Represents;
using a formula
Figure FDA0002993947870000041
Calculating a correlation coefficient between the vehicle shaking waveform data obtained by the historical p-th detection and the vehicle shaking disease waveform data;
using a formula
Figure FDA0002993947870000042
Obtaining a minimum correlation coefficient;
wherein P represents the total historical detection times of the vehicle shaking defect point to be rectified in the time constraint C _ FT,
Figure FDA0002993947870000043
representing the number of the waveform of the vehicle shaking obtained by the p detection in historyAccording to the ith disease point alphaiCorrelation coefficient between the corresponding car sloshing fault waveform data, cov (M)0 i,Mp i) Representing variable M0 iAnd Mp iThe covariance of (a) of (b),
Figure FDA0002993947870000044
representing variable M0 i、Mp iMin (-) represents taking the minimum value of the variable (-) and,
Figure FDA0002993947870000045
and representing the minimum correlation coefficient between the vehicle shaking waveform data and the vehicle shaking disease waveform data obtained by each historical detection.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114592388A (en) * 2022-03-07 2022-06-07 中国铁道科学研究院集团有限公司铁道建筑研究所 Ride comfort detection method and system for long and large tunnel rail transportation track
CN117907512A (en) * 2024-03-20 2024-04-19 杭州臻稀生物科技有限公司 Sewage detection method constructed based on relation between solid phase extraction flow rate and internal standard selection

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101900708A (en) * 2010-08-18 2010-12-01 哈尔滨工业大学 Vibration and audio signal-based high-speed train track defect detecting method
CN102496064A (en) * 2011-12-01 2012-06-13 北京交通大学 Method for acquiring unevenness of track
CN102518012A (en) * 2011-11-18 2012-06-27 北京交通大学 Track detection method integrating detection data of various devices
DE102011101226A1 (en) * 2011-05-11 2012-11-15 Deutsche Bahn Ag Method for describing position deviation of railway track, involves determining amplitude error from track position measurement signal, and determining curvature of track position deviation in linear relationship with vehicle travel
CN107864667A (en) * 2014-12-27 2018-03-30 贾迪安光学技术有限公司 For the system and method for the multiple vibrations for detecting surface
CN110274778A (en) * 2019-08-01 2019-09-24 成都博仕腾科技有限公司 Rail truck shakes vehicle test method and system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101900708A (en) * 2010-08-18 2010-12-01 哈尔滨工业大学 Vibration and audio signal-based high-speed train track defect detecting method
DE102011101226A1 (en) * 2011-05-11 2012-11-15 Deutsche Bahn Ag Method for describing position deviation of railway track, involves determining amplitude error from track position measurement signal, and determining curvature of track position deviation in linear relationship with vehicle travel
CN102518012A (en) * 2011-11-18 2012-06-27 北京交通大学 Track detection method integrating detection data of various devices
CN102496064A (en) * 2011-12-01 2012-06-13 北京交通大学 Method for acquiring unevenness of track
CN107864667A (en) * 2014-12-27 2018-03-30 贾迪安光学技术有限公司 For the system and method for the multiple vibrations for detecting surface
CN110274778A (en) * 2019-08-01 2019-09-24 成都博仕腾科技有限公司 Rail truck shakes vehicle test method and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
侯博文等: "《基于深度残差网络的轨道结构病害识别》", 《铁道学报》 *
白磊: "《铁路轨道健康管理网格化分析决策模型研究》", 《中国博士学位论文全文数据库（工程科技Ⅱ辑）》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114592388A (en) * 2022-03-07 2022-06-07 中国铁道科学研究院集团有限公司铁道建筑研究所 Ride comfort detection method and system for long and large tunnel rail transportation track
CN117907512A (en) * 2024-03-20 2024-04-19 杭州臻稀生物科技有限公司 Sewage detection method constructed based on relation between solid phase extraction flow rate and internal standard selection
CN117907512B (en) * 2024-03-20 2024-05-31 杭州臻稀生物科技有限公司 Sewage detection method constructed based on relation between solid phase extraction flow rate and internal standard selection

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