CN116198559A - Rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis - Google Patents
Rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis Download PDFInfo
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Abstract
The invention discloses a rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis, which comprises the steps of determining an acceleration effective value threshold value by fitting the relation between an axle box vibration acceleration effective value and a train running speed; acquiring the vibration acceleration of the axle box in real time, and calculating the train running distance corresponding to the continuous overrun of the acquired effective value of the vibration acceleration of the axle box in real time; according to the running distance of the train and the threshold value of the overrun distance, primarily judging whether a rail wave grinding section exists or not; when the existence of the rail wave grinding section is primarily judged, converting the real-time axle box vibration acceleration time course from a time-frequency domain to a time-quasi-wave number domain, extracting wave grinding characteristic wavelength according to preset conditions, and determining whether the rail wave grinding exists or not; when the rail wave mill exists, the acceleration signal corresponding to the effective value of the vibration acceleration of the axle box is subjected to secondary integration, and the wave mill amplitude parameter is obtained through detection. The method can rapidly extract the wave grinding section, the wave grinding characteristic wavelength and the amplitude of the steel rail in real time, and improves the subway operation and maintenance efficiency.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis.
Background
Urban rail transit is becoming the core of urban public transportation at present as a traffic mode with small floor area, large passenger traffic volume and low environmental pollution. In the train running process, the rail wave mill is inevitably generated on the rail, and the rail wave mill almost exists on all subway lines, is one of the important reasons for generating impact vibration between wheels and rails during the running of a subway vehicle, can not only increase noise generated by the running of the train, but also possibly cause fatigue damage to part of structural components of a vehicle-rail system, reduce riding comfort of passengers and even traffic safety at crisis, and as the running mileage and running speed of urban rail transit are increased, the damage condition between a train wheel pair and the rail is also more and more serious due to the rail wave mill, so that the influence of the rail wave mill on the subway running is more and more emphasized.
When the train runs to the rail corrugation section, the train not only can generate severe wheel rail howling, and the surrounding environment of the subway along the line is influenced, but also can generate high-frequency vibration, so that riding comfort is influenced; in addition, the dynamic action between the wheel rails can be aggravated by the rail wave mill, so that the mechanical damage of the bogie and the rail parts of the train is caused, and the running of the train is not ensured.
The existing subway rail wave mill detection means mainly comprises two methods of manual sampling detection and continuous measurement of a portable rail wave mill detection trolley, and the two methods have the problems of low detection efficiency, high labor cost and the like, and cannot meet the requirements of real-time rapid detection of subway wave mill.
In addition, in the related art, liu Jinchao and the like, in order to realize detection of shortwave irregularity of high-speed rails, it is proposed to describe the high-frequency correspondence of axle box acceleration caused by wheel rail impact from the angle of energy, calculate the moving effective value of the axle box acceleration, normalize the moving effective value to obtain the rail impact index, and judge whether shortwave irregularity such as uneven wear, wave and ripple wear of steel rail joints and joints exists or not by setting a threshold value. The method can only be used for high-speed railways with stable running speed, and lacks a rail wave grinding state discrimination threshold value directly related to the speed of the rail, so that real-time discrimination of rail wave grinding cannot be realized.
Although scholars at home and abroad develop a great deal of researches on a real-time identification and detection method of rail wave grinding, the prior art has the following defects:
(1) The existing method for identifying and detecting the rail grinds is only suitable for detecting the rail grinds when the train runs at a constant speed, and cannot accurately identify and detect the rail grinds when the running speed changes greatly;
(2) At present, the subway lacks means for automatically identifying the rail corrugation which can be widely applied, and the identification and detection of the rail corrugation are still mainly carried out in a manual inspection mode, so that the detection speed is low and the efficiency is low;
(3) The interference frequency of vibration acceleration detection data of the axle box of the urban rail transit train is high, and the accuracy of the detection result is affected;
(4) The real-time identification and detection of the rail corrugation in the running process of the train cannot be realized.
Therefore, a new method for real-time detection and monitoring of rail wave mill is needed.
Disclosure of Invention
The invention aims to provide a rail wave mill identification method based on axle box vibration acceleration quasi-wave number domain analysis, which solves the problem that subway wave mill real-time rapid and accurate detection cannot be met in the prior art; the method can accurately and real-timely identify the rail wave grinding section and extract the characteristic wavelength and amplitude of rail wave grinding.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the invention provides a rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis, which comprises the following steps:
determining an acceleration effective value threshold by fitting the relation between the vibration acceleration effective value of the axle box and the running speed of the train;
acquiring the vibration acceleration of the axle box in real time according to the acceleration effective value threshold value, and calculating the train running distance corresponding to continuous overrun of the acquired real-time vibration acceleration effective value of the axle box;
according to the running distance of the train and the threshold value of the overrun distance, primarily judging whether a rail wave grinding section exists or not;
when the rail wave grinding section is primarily judged to exist, converting real-time axle box vibration acceleration of the rail wave grinding section from a time-frequency domain to a time-quasi-wave number domain, extracting wave grinding characteristic wavelength according to preset conditions, and determining whether rail wave grinding exists or not;
when the existence of the rail corrugation is determined, carrying out secondary integration on an acceleration signal corresponding to the axle box vibration acceleration effective value of the rail corrugation section, and detecting to obtain the corrugation amplitude parameter.
Further, determining an acceleration effective value threshold value by fitting the relation between the effective value of the vibration acceleration of the axle box and the running speed of the train; comprising the following steps:
calculating an effective value of the vibration acceleration of the axle box through the formula (1), and fitting a relational expression of the effective value of the vibration acceleration of the axle box and the running speed of the train through the formula (2) to serve as a judging threshold value;
(1) Wherein a is i The measured acceleration value in the length of the sampling window is obtained, and m is the sampling point number;
a RMS,limited ＝k×v+b+δ (2)
(2) Wherein k is the fit slope, v is the train running speed, b is the fit intercept, delta is the standard deviation of the fit residual error, a RMS，limited Is the acceleration effective value threshold.
Further, according to the acceleration effective value threshold, the axle box vibration acceleration is collected in real time, and the train running distance corresponding to continuous overrun of the collected real-time axle box vibration acceleration effective value is extracted; comprising the following steps:
in one sampling period, detecting the vibration acceleration of the axle box, the running speed of the train and the driving mileage in real time;
calculating the effective value of the vibration acceleration of the axle box in real time from the sampling starting time, and comparing the effective value of the acceleration with a threshold value of the effective value of the acceleration;
when the effective value of the vibration acceleration of the axle box is larger than the threshold value of the effective value of the acceleration, the axle boxThe time point corresponding to the vibration acceleration effective value is taken as the initial time t' n ；
And continuing to cyclically compare until the effective value of the vibration acceleration of the axle box is smaller than the threshold value of the effective value of the acceleration, wherein the time point corresponding to the effective value of the vibration acceleration of the axle box is taken as the ending time t' n ’n；
At t' n ～t’ n The train travel distance is calculated over the duration of' n.
Further, according to the train running distance and the overrun distance threshold, primarily judging whether a rail wave grinding section exists or not; comprising the following steps:
when DeltaS > (N Number of vehicle groups +2)×l Single-section vehicle length Preliminarily judging that a rail wave-milling section exists;
when DeltaS < (N) Number of vehicle groups +2)×l Single-section vehicle length Preliminarily judging that a rail wave grinding section does not exist;
ΔS is at t' n ～t’ n Calculating the length of the train running section within the duration of' n; n is the number of vehicle groups; l is the length of a single vehicle.
Further, when the rail wave mill section is judged to exist initially, real-time axle box vibration acceleration of the rail wave mill section is converted into a time-quasi-wave number domain from a time-frequency domain, and wave mill characteristic wavelength is extracted: comprising the following steps:
preliminary judgment of the existence of the rail wave-milling section at t 'by applying short-time Fourier transform' n ～t’ n The raw time-frequency response analysis for the acceleration signal for the duration of' n;
calculating the average speed of the vehicle in each window length according to the window length and the frequency characteristic of the time-frequency analysis, and converting the acceleration detection result from the time-frequency analysis to a time-quasi-wave number domain according to the formula (3);
(3) In the method, in the process of the invention,
is the average wave number, f is the frequency, ">
Is the average vehicle speed;
synchronous compression transformation is carried out on analysis results of time-quasi wave number domain, energy ridge lines are extracted, characteristic wave numbers are calculated, and the characteristic wave numbers are converted into characteristic wave lengths
According to the extraction
Energy ridge in the wavenumber range, if wavenumbers are present at about +.>
Is determined to exist at a wavelength of about +.>
Is a rail wave mill.
Further, when it is determined that rail corrugation exists, performing secondary integration on an acceleration signal corresponding to an axle box vibration acceleration effective value of the rail corrugation section, and detecting to obtain a corrugation amplitude parameter; comprising the following steps:
extracting vibration signals corresponding to the collected real-time axle box vibration acceleration effective values in a segmented mode, removing low-frequency trend items in the signals through filtering, and setting
In the form of the expression of Fourier transform, ω is a frequency variable; t is time;
according to the integral theorem of Fourier transformation, the axle box vibration acceleration time domain signal has a relation as shown in the formula (4) with the frequency domain signal;
converting the axle box vibration acceleration time domain signal into a frequency domain, and setting the Fourier component of any frequency as We jωt According to formula (5)The frequency domain signal of the axle box vibration acceleration is subjected to secondary integration and converted into a displacement signal, and a wave grinding amplitude parameter is obtained;
wherein:
in the formulas (4) - (5), t is time, a is axle box vibration acceleration,
omega is a frequency variable; s (t) is a displacement signal, W is a wave grinding amplitude parameter, and e is a natural constant.
Compared with the prior art, the invention has the following beneficial effects:
according to the rail wave grinding identification method based on axle box vibration acceleration quasi-wave number domain analysis, provided by the embodiment of the invention, the relation between the acceleration movement effective value and the real-time speed is statistically analyzed by combining the positive correlation characteristic between the axle box acceleration and the vehicle speed, and whether the rail is subjected to wave grinding diseases is judged by providing a new evaluation threshold value, a line design principle and the like; the frequency spectrum analysis of the axle box vibration acceleration signal is converted from frequency and into a quasi-wave number domain by combining the characteristic of relatively fixed wave grinding wavelength of the steel rail of the same track type. The method can extract the rail corrugation section rapidly in real time, detect the corrugation characteristic wavelength and amplitude, provide reliable data support for urban rail operation and maintenance, improve subway operation and maintenance efficiency, and further help to provide guidance for rail work management.
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FIG. 1 is a flow chart of a rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis, which is provided by the embodiment of the invention;
FIG. 2 is a schematic diagram of a rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis provided by an embodiment of the invention;
FIG. 3a is a schematic diagram of a real-time acquisition of left rail axle box vibration acceleration signals;
FIG. 3b is a schematic diagram of a real-time acquired right-rail-box vibration acceleration signal;
FIG. 3c is a schematic diagram of real-time acquisition of the effective value of the vibratory acceleration of the axle box;
FIG. 4a is a schematic diagram of a real-time collected train speed effective value;
FIG. 4b is a schematic diagram of an acceleration discrimination threshold fit;
FIG. 5a is a left rail wave mill segment identification schematic diagram;
FIG. 5b is a schematic diagram of right rail wave mill segment identification;
FIG. 6a is a graph showing the results of the frequency domain extraction of the vibratory acceleration of the axle boxes;
FIG. 6b is a graph showing the results of the quasi-wavenumber domain extraction of the axle box vibration acceleration;
fig. 7 is a diagram of the results of the on-site investigation of the left rail and the right rail of an actual rail by wave grinding.
Detailed Description
The invention is further described in connection with the following detailed description, in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific direction, be configured and operated in the specific direction, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "provided," "connected," and the like are to be construed broadly, and may be fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis provided by the invention can be used for rapidly identifying the rail wave mill section in real time and detecting the wave mill characteristic wavelength and amplitude.
Because the vibration acceleration signal of the train axle box caused by the subway wave mill has the characteristics of high frequency and nonlinearity, the factors such as the shape and the material of the wheels and the tread of the steel rail, the suspension parameters of the vehicle, the roughness of the contact surface of the wheel and the rail and the like have great influence on the vibration acceleration of the train axle box except the rail wave mill; in addition, the train operating speed is positively correlated with the axle box vibration acceleration. Therefore, from the aspect of energy, the method provided by the invention realizes the detection of the rail wave mill section and the extraction of the wave mill wavelength amplitude by the following steps, and referring to fig. 1, the method specifically comprises the following steps:
s1, determining an acceleration effective value threshold value by fitting a relation between an axle box vibration acceleration effective value and a train running speed;
s2, acquiring the vibration acceleration of the axle box in real time according to the acceleration effective value threshold, and calculating the train running distance corresponding to the continuous overrun of the acquired real-time vibration acceleration effective value of the axle box;
s3, primarily judging whether a rail wave grinding section exists or not according to the train running distance and the overrun distance threshold value;
s4, when the rail wave grinding section exists in the initial step, converting the real-time axle box vibration acceleration of the rail wave grinding section into a time-quasi-wave number domain from a time-frequency domain, extracting wave grinding characteristic wavelength according to preset conditions, and determining whether rail wave grinding exists or not;
and S5, when the existence of the rail corrugation is determined, carrying out secondary integration on an acceleration signal corresponding to the axle box vibration acceleration effective value of the rail corrugation section, and detecting to obtain the corrugation amplitude parameter.
In the embodiment, the relation between the effective acceleration value and the running speed of the train is fitted, and the threshold value of the effective acceleration value is determined, so that the rail wave grinding section and the rail wave grinding section are identified; in addition, the vehicle speed is acquired in real time, the average speed in each window is calculated by dividing small windows, and the acquisition result of the frequency domain is converted into the quasi-wave number domain according to the relation of the frequency, the speed and the wavelength, so that the extraction of the characteristic wavelength of the wave mill is realized; the actual amplitude of the wave mill can be detected by a high-pass filtering and acceleration secondary integration method, so that the automatic identification and analysis of the wave mill section of the steel rail are realized.
The following describes the technical solution of the present invention in detail by way of an example with reference to fig. 2:
1) According to the existing history measured data of subway operation, for example, 0.125s is used for analyzing the window length of data, the overlapping coefficient is 3/4, and the time-varying characteristics of the effective acceleration value are statistically analyzed; because the actual axle box acceleration signal is easily influenced by the shapes of wheels and treads, vehicle suspension parameters, the roughness of wheel and rail contact surfaces and other short wave irregularity factors of the steel rail, the axle box vertical vibration acceleration amplitude is directly utilized for judgment, and the problems of large randomness of judgment results and difficult determination of threshold values can occur.
Because the energy of the rail wave grinding section is concentrated, from the angle of energy, fitting the relation between the effective acceleration value and the train speed, and combining the fitting residual error and the effective vibration acceleration value of the wave grinding section axle box to obtain the judging threshold value of the effective vibration acceleration value of the rail wave grinding section axle box which changes along with the speed; calculating an acceleration effective value through a formula (1), and fitting a relation between the acceleration effective value and the vehicle speed through a formula (2) to be used as a judging threshold;
(1) Wherein a is i The measured acceleration value in the length of the sampling window is obtained, and m is the sampling point number;
a RMS,limited ＝k×v+b+δ (2)
(2) Wherein k is the fit slope, v is the train running speed, b is the fit intercept, delta is the standard deviation of the fit residual error, a RMS，limited Is the acceleration effective value threshold.
2) A vibration acceleration sensor and an axle end encoder are arranged on an axle box of the subway vehicle, and the vibration acceleration of the axle box, the running speed of a train and the driving mileage are detected in real time;
3) Calculating the effective acceleration value a in real time from the starting time by using 0.125s as the data analysis window length and the overlapping coefficient of 3/4 RMS And comparing the discrimination threshold, when a RMS (t n ′)＞a RMS,limited Return a RMS (t n ') the corresponding time point is taken as the initial time t n ' continue the cycle contrast until a RMS (t n ") is smaller than the discrimination threshold, and the corresponding returning time point is the ending time t n "when the effective value of the axle box vibration acceleration is greater than the threshold duration (t n ′，t n Train operation mileage delta S > (N) Number of vehicle groups +2)×l Single-section vehicle length When the continuous laying length of a railway is generally longer than the length of a whole train and two sections of vehicles, the railway is initially judged to be a rail wave grinding section, an original axle box vibration acceleration signal in a corresponding time period is extracted, and the cycle comparison is continued until the detection is finished; if DeltaS < (N) Number of vehicle groups +2)×l Single-section vehicle length When it is determined that no wave grinding has occurred, the starting and ending time points t are not returned n ' and t n And (c) continuing to circularly compare, wherein n is the number of the primary judging rail wave mill generation section, and the value is a positive integer greater than or equal to 1: 1,2,3, … …; the superscript "'" indicates the start time of the wave mill occurrence section, and the superscript "" indicates the end time of the wave mill occurrence section.
4) Preliminary determination of rail wave grinding occurrence section (t) n ′，t n "time-frequency" response analysis is carried out on the original axle box vibration acceleration signals in "the above), and synchronous compression transformation is adopted to concentrate the signal energy at the gravity center position of the energy, so as to avoid the problem of time-frequency ambiguity.
5) For the primarily judged rail corrugation section, the metro vehicle is frequently accelerated and decelerated, so that the vibration frequency characteristic of the axle box is complex, but the wavelength of rail corrugation is relatively fixed, so that the average speed of each window length is calculated by combining the window length and the frequency characteristic of time-frequency analysis, and the axle box vibration acceleration detection result is converted from time-frequency analysis to a time-quasi-wavenumber domain according to the formula (3);
(3) In the method, in the process of the invention,
is the average wave number, f is the frequency, ">
Is the average vehicle speed;
the speed is not the instantaneous speed and the average speed in the window length, and the wave number calculation result is not consistent with the actual wave number, so the speed is called a quasi-wave number domain.
Then synchronously compressing and transforming the analysis result, extracting energy ridge line, calculating characteristic wave number, and finally converting into characteristic wavelength
Furthermore, extract->
Energy ridge in the wavenumber range, if wavenumbers are present at about +.>
If the energy ridge line of (2) is determined to exist at a wavelength of about +.>
Is a rail wave mill.
6) Extracting the axle box vibration acceleration vibration signal in the step 2) in a segmented way,removing low-frequency trend term in signal by filtering
In the expression form of Fourier transform, omega is frequency variable, t is time, s is unit, according to the integral theorem of Fourier transform, the axle box vibration acceleration time domain signal has the relation as in the expression (4) with the frequency domain signal, the time domain acceleration signal is converted into the frequency domain, and the Fourier component of any frequency is set as We jωt Wherein W is the amplitude value,
the calculation formula of the wave grinding amplitude is shown as formula (5), the acceleration signal is integrated for the second time according to formula (5), the acceleration signal is converted into a displacement signal, and the wave grinding amplitude is obtained.
Wherein:
in the formulas (4) - (5), t is time, a is axle box vibration acceleration,
omega is a frequency variable; s (t) is a displacement signal, W is a wave grinding amplitude parameter, e is a natural constant, and dt represents differentiation.
Taking Beijing subway as an example, FIG. 3a shows a left axle box vibration acceleration signal acquired in real time on a new palace-peony garden line, and FIG. 3a shows a right axle box vibration acceleration signal acquired in real time, wherein the abscissa represents train driving mileage and the ordinate represents acceleration; the effective value of the axle box vibration acceleration is shown in fig. 3c, with the ordinate indicating acceleration and the abscissa indicating time.
According to the axle box vibration acceleration effective value calculation result, an acceleration effective value fitting discriminant as shown in fig. 4a-4b can be obtained; and detecting the rail corrugation segment according to the discrimination threshold value to obtain the corrugation segment identification result shown in fig. 5a-5 b.
Then according to the above steps 4) and 5), the axle box vibration acceleration signal is converted from the time-frequency domain to the time-quasi-wave number domain, so as to extract the characteristic wavelength of the wave mill, and the result is shown in fig. 6a-6b, and the extracted wave number is about 40, then
The wavelength is about 25mm. Finally, in combination with the illustration of fig. 7, the results of the on-site investigation are shown, wherein rail wave grinding occurs in the K47+000 section and the K47+300 section respectively, and the wavelength is about 25mm. Thus, the detection accuracy of the technical scheme of the invention is described.
According to the invention, a more proper judgment threshold value is given by fitting the relation between the effective acceleration value and the running speed, so that the rail wave grinding section identification under different vehicle speed conditions is realized; and (3) calculating the average speed in each window length, and converting the detection result of the frequency domain of the acceleration signal to the wave number domain to realize the extraction of the characteristic wavelength of the wave mill. Compared with the prior art, the method can accurately, efficiently and real-timely identify the rail corrugation section in the axle box acceleration signal aiming at the characteristics of frequent acceleration and deceleration of the subway and various track forms, extract corrugation characteristic wavelength and amplitude, provide reliable data support for subway rail polishing, milling and vibration noise control, and improve subway operation and maintenance efficiency.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (6)
1. The rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis is characterized by comprising the following steps:
determining an acceleration effective value threshold by fitting the relation between the vibration acceleration effective value of the axle box and the running speed of the train;
acquiring the vibration acceleration of the axle box in real time according to the acceleration effective value threshold value, and calculating the train running distance corresponding to continuous overrun of the acquired real-time vibration acceleration effective value of the axle box;
according to the running distance of the train and the threshold value of the overrun distance, primarily judging whether a rail wave grinding section exists or not;
when the rail wave grinding section is primarily judged to exist, converting real-time axle box vibration acceleration of the rail wave grinding section from a time-frequency domain to a time-quasi-wave number domain, extracting wave grinding characteristic wavelength according to preset conditions, and determining whether rail wave grinding exists or not;
when the existence of the rail corrugation is determined, carrying out secondary integration on an acceleration signal corresponding to the axle box vibration acceleration effective value of the rail corrugation section, and detecting to obtain the corrugation amplitude parameter.
2. The rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis according to claim 1, wherein the method is characterized in that an acceleration effective value threshold value is determined by fitting the relation between the axle box vibration acceleration effective value and the train running speed; comprising the following steps:
calculating an effective value of the vibration acceleration of the axle box through the formula (1), and fitting a relational expression of the effective value of the vibration acceleration of the axle box and the running speed of the train through the formula (2) to serve as a judging threshold value;
(1) Wherein a is i The measured acceleration value in the length of the sampling window is obtained, and m is the sampling point number;
a RMS,limited ＝k×v+b+δ(2)
(2) Wherein k is the fit slope, v is the train running speed, b is the fit intercept, delta is the standard deviation of the fit residual error, a RMS，limited Is the acceleration effective value threshold.
3. The rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis according to claim 1, wherein the axle box vibration acceleration is collected in real time according to the acceleration effective value threshold, and the train running distance corresponding to continuous overrun of the collected real-time axle box vibration acceleration effective value is extracted; comprising the following steps:
in one sampling period, detecting the vibration acceleration of the axle box, the running speed of the train and the driving mileage in real time;
calculating the effective value of the vibration acceleration of the axle box in real time from the sampling starting time, and comparing the effective value of the acceleration with a threshold value of the effective value of the acceleration;
when the effective value of the vibration acceleration of the axle box is larger than the threshold value of the effective value of the acceleration, the time point corresponding to the effective value of the vibration acceleration of the axle box is taken as the initial time t' n ；
And continuing to cyclically compare until the effective value of the vibration acceleration of the axle box is smaller than the threshold value of the effective value of the acceleration, wherein the time point corresponding to the effective value of the vibration acceleration of the axle box is taken as the ending time t' n ’；
At t' n ～t’ n The train travel distance is calculated over the duration of'.
4. The rail wave grinding identification method based on axle box vibration acceleration quasi wave number domain analysis according to claim 3, wherein whether a rail wave grinding section exists is primarily judged according to the train running distance and an overrun distance threshold; comprising the following steps:
when DeltaS > (N Number of vehicle groups +2)×l Single-section vehicle length Preliminarily judging that a rail wave-milling section exists;
when DeltaS < (N) Number of vehicle groups +2)×l Single-section vehicle length Preliminarily judging that a rail wave grinding section does not exist;
ΔS is at t' n ～t’ n Calculating the length of the train driving section within the duration of'; n is the number of vehicle groups; l is the length of a single vehicle.
5. The rail wave mill identification method based on axle box vibration acceleration quasi-wave number domain analysis according to claim 4, wherein the rail wave mill section is initially judged to exist, real-time axle box vibration acceleration of the rail wave mill section is converted from a time-frequency domain to a time-quasi-wave number domain, and wave mill characteristic wavelengths are extracted: comprising the following steps:
preliminary judgment of the existence of the rail wave-milling section at t 'by applying short-time Fourier transform' n ～t’ n The raw time-frequency response analysis for the acceleration signal for the duration of' time;
calculating the average speed of the vehicle in each window length according to the window length and the frequency characteristic of the time-frequency analysis, and converting the acceleration detection result from the time-frequency analysis to a time-quasi-wave number domain according to the formula (3);
(3) In the method, in the process of the invention,
average wave number, f is frequency, v is average vehicle speed;
synchronous compression transformation is carried out on analysis results of time-quasi wave number domain, energy ridge lines are extracted, characteristic wave numbers are calculated, and the characteristic wave numbers are converted into characteristic wave lengths
According to the extraction
If an energy ridge line in the wave number range exists, the existence of a wavelength of about +.>
Is a rail wave mill.
6. The rail wave mill identification method based on axle box vibration acceleration quasi wave number domain analysis according to claim 5, wherein when it is determined that rail wave mill exists, the acceleration signal corresponding to the axle box vibration acceleration effective value of the rail wave mill section is subjected to secondary integration, and the wave mill amplitude parameter is obtained through detection; comprising the following steps:
extracting vibration signals corresponding to the collected real-time axle box vibration acceleration effective values in a segmented mode, removing low-frequency trend items in the signals through filtering, and setting
In the form of the expression of Fourier transform, ω is a frequency variable; t is time;
according to the integral theorem of Fourier transformation, the axle box vibration acceleration time domain signal has a relation as shown in the formula (4) with the frequency domain signal;
converting the axle box vibration acceleration time domain signal into a frequency domain, and setting the Fourier component of any frequency as We jωt Performing secondary integration on the frequency domain signal of the axle box vibration acceleration according to the formula (5), and converting the frequency domain signal into a displacement signal to obtain a wave grinding amplitude parameter;
wherein:
in the formulas (4) - (5), t is time, a is axle box vibration acceleration,
omega is a frequency variable; s (t) is a displacement signal, W is a wave grinding amplitude parameter, and e is a natural constant. />
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