阅读说明：本技术 钢轨波磨检测方法及装置 (Rail corrugation detection method and device ) 是由 孙宪夫 魏子龙 杨飞 柯在田 支洋 邢梦婷 高雅 刘贵宪 于 2021-06-15 设计创作，主要内容包括：本发明提供了一种钢轨波磨检测方法及装置,该方法包括：对初始车速集合进行积分计算,获得第一里程集合；基于三维坐标集合,对第一里程集合和初始车速集合进行修正,获得第三里程集合和第一车速集合；根据第一车速集合和车体摇头角速度集合,对初始车体垂向加速度集合、初始车体横向加速度集合和初始噪声集合进行数据融合,计算钢轨波磨评价指数；根据第三里程集合和钢轨波磨评价指数,计算钢轨波磨特征系数,判断疑似钢轨波磨的区段；根据第三里程集合和初始车体垂向加速度集合计算周期性特征系数,从疑似钢轨波磨的区段中筛选出确定存在钢轨波磨的区段。本发明可在铁路线路运营期间实时检测出钢轨波磨的分布和特征,可搭载于运营列车车厢内部。(The invention provides a rail corrugation detection method and a rail corrugation detection device, wherein the method comprises the following steps: performing integral calculation on the initial vehicle speed set to obtain a first mileage set; correcting the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set; according to the first vehicle speed set and the vehicle body shaking angular speed set, performing data fusion on an initial vehicle body vertical acceleration set, an initial vehicle body transverse acceleration set and an initial noise set, and calculating a rail corrugation evaluation index; calculating a rail corrugation characteristic coefficient according to the third mileage set and the rail corrugation evaluation index, and judging a suspected rail corrugation zone; and calculating a periodic characteristic coefficient according to the third mileage set and the initial vehicle body vertical acceleration set, and screening out a section determined to have the rail corrugation from the suspected rail corrugation sections. The invention can detect the distribution and the characteristics of the rail corrugation in real time during the operation of the railway line and can be carried in the carriage of the operation train.)

1. A rail corrugation detection method is characterized by comprising the following steps:

acquiring a three-dimensional coordinate set, an initial vehicle speed set, an initial vehicle vertical acceleration set, an initial vehicle transverse acceleration set, an initial noise set and a vehicle shaking angle speed set of an operating train in real time;

performing integral calculation on the initial vehicle speed set to obtain a first mileage set;

correcting the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set;

according to the first vehicle speed set and the vehicle body shaking angular speed set, performing data fusion on an initial vehicle body vertical acceleration set, an initial vehicle body transverse acceleration set and an initial noise set, and calculating a rail corrugation evaluation index;

calculating a rail corrugation characteristic coefficient according to the third mileage set and the rail corrugation evaluation index, and judging a suspected rail corrugation zone;

and calculating a periodic characteristic coefficient according to the third mileage set and the initial vehicle body vertical acceleration set, and screening out a section determined to have the rail corrugation from suspected rail corrugation sections according to the periodic characteristic coefficient.

2. The rail corrugation detection method of claim 1, wherein the obtaining of the three-dimensional coordinate set, the initial vehicle speed set, the initial vehicle vertical acceleration set, the initial vehicle lateral acceleration set, the initial noise set, and the vehicle oscillation angular velocity set of the operating train in real time comprises:

acquiring three-dimensional coordinate data and initial speed data of an operating train in real time by adopting a GNSS positioning module;

acquiring an initial train body vertical acceleration set and an initial train body transverse acceleration set of an operating train in real time by adopting an acceleration sensor;

acquiring an initial noise set of an operating train in real time by adopting a sound pressure sensor, wherein the sound pressure sensor vertically downwards measures noise transmitted to a carriage bottom plate from a wheel-rail contact surface;

and acquiring a vehicle body shaking angular velocity set of the operation train in real time by adopting a fiber optic gyroscope.

3. The rail corrugation detection method of claim 1, wherein the correcting the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set comprises:

calibrating the first mileage set according to the three-dimensional coordinate set and the coordinate set of the line center line coordinate point library to obtain a second mileage set;

and performing linear interpolation on the second mileage set and the initial vehicle speed set according to the sampling frequency of the vehicle body vibration data to generate a third mileage set and a first vehicle speed set.

4. The rail corrugation detection method of claim 3, wherein calibrating the first mileage set based on the three-dimensional coordinate set and the coordinates set of the line neutral coordinate point library to obtain a second mileage set comprises:

screening out a coordinate point group which is closest to a coordinate set of a line center line coordinate point library in the three-dimensional coordinate set and meets the condition that the distance between plane coordinates of two points is smaller than a mileage calibration error threshold value, and forming a first mileage calibration point group;

correcting a mileage set of a first mileage calibration point group into a mileage set consistent with the mileage of a corresponding line center line coordinate point, wherein the mileage set of the first mileage calibration point group comprises mileage errors of a plurality of calibration points;

correcting first mileage data between the starting point and the 2 nd calibration point by using a mileage error of the 1 st calibration point from the starting point of a first mileage set including a plurality of first mileage data;

repeatedly executing the following steps until the first mileage data in the first mileage set is corrected, and forming a second mileage set:

and correcting the first mileage data between the nth calibration point and the (n + 1) th calibration point by using the mileage error of the nth calibration point, wherein n is more than or equal to 2.

5. The rail corrugation detection method of claim 1, wherein the step of performing data fusion on the initial vertical car body acceleration set, the initial lateral car body acceleration set and the initial noise set according to the first car speed set and the car body oscillation angular speed set to calculate a rail corrugation evaluation index comprises the steps of:

performing band-pass filtering processing on the initial vehicle body vertical acceleration set, the initial vehicle body transverse acceleration set and the initial noise set according to a preset cut-off frequency range to generate a first vehicle body vertical acceleration set, a first vehicle body transverse acceleration set and a first noise set;

respectively calculating sliding effective values of a first vehicle body vertical acceleration set, a first vehicle body transverse acceleration set and a first noise set to generate a vehicle body vertical acceleration sliding effective value set, a vehicle body transverse acceleration sliding effective value set and a noise sliding effective value set;

and performing data fusion on the vehicle body vertical acceleration sliding effective value set, the vehicle body transverse acceleration sliding effective value set and the noise sliding effective value set based on the first vehicle speed set and the vehicle body shaking head angular speed set, and calculating the rail corrugation evaluation index.

6. The rail corrugation detection method of claim 5, wherein the calculating of the rail corrugation evaluation index by performing data fusion on the set of vehicle body vertical acceleration sliding valid values, the set of vehicle body lateral acceleration sliding valid values and the set of noise sliding valid values based on the first vehicle speed set and the set of vehicle body yaw angular velocities comprises:

carrying out low-pass filtering on the vehicle body shaking angular velocity set at a first cut-off frequency to generate a first vehicle body shaking angular velocity set;

sampling the first vehicle speed set and the first vehicle body yaw angular speed set, and extracting data corresponding to a point at the center position of each sliding window to obtain a second vehicle speed set and a second vehicle body yaw angular speed set;

determining a vehicle body lateral acceleration weight coefficient according to the second vehicle body yaw angular velocity set;

and calculating the rail corrugation evaluation index according to the second vehicle speed set, the vehicle body transverse acceleration weight coefficient, the vehicle body vertical acceleration sliding effective value set, the vehicle body transverse acceleration sliding effective value set and the noise sliding effective value set.

7. The rail corrugation detection method of claim 6, wherein the rail corrugation evaluation index is calculated based on the second vehicle speed set, the lateral acceleration weight coefficient of the car body, the vertical acceleration sliding valid value set of the car body, the lateral acceleration sliding valid value set of the car body, and the noise sliding valid value set by using the following formula:

wherein, V_jThe evaluation index of the rail corrugation is obtained; s_z,j＝1-s_y,jThe weight coefficient is the vertical acceleration of the vehicle body; s_y,jThe weight coefficient is the lateral acceleration of the vehicle body; s_aIs a vibration index weight coefficient; s_noIs a noise index weight coefficient;the effective value of the vertical acceleration sliding of the vehicle body is;the sliding effective value of the transverse acceleration of the vehicle body is;is a noise sliding effective value; v. of_jIs the third vehicle speed.

8. The rail corrugation detection method of claim 1, wherein calculating a rail corrugation feature coefficient based on the third mileage set and the rail corrugation evaluation index, and determining a suspected rail corrugation segment comprises:

sampling the third mileage set, and extracting data corresponding to the point of the center position of each sliding window to obtain a fourth mileage set;

taking the fourth mileage set as an independent variable and the rail corrugation evaluation index as a dependent variable, and performing equal-space resampling on the rail corrugation evaluation index at a first set sampling interval to generate a fifth mileage set and a second rail corrugation evaluation index;

performing low-pass filtering on the second rail corrugation evaluation index at a second cut-off frequency to generate a third rail corrugation evaluation index;

calculating the average value of the third rail corrugation evaluation index;

calculating a rail corrugation characteristic coefficient according to the third rail corrugation evaluation index and the average value of the third rail corrugation evaluation index;

and determining the section which is more than the preset rail corrugation characteristic coefficient threshold value and longer than the continuous set length as the suspected rail corrugation section.

9. The rail corrugation detection method of claim 8, wherein the rail corrugation feature coefficient is calculated from the third rail corrugation evaluation index and an average of the third rail corrugation evaluation indices using the following formula:

wherein E is_kThe characteristic coefficient of the rail corrugation is taken as the coefficient; v_kThe third rail corrugation evaluation index; v₀The average value of the third rail corrugation evaluation index is shown.

10. The rail corrugation detection method of claim 1, wherein calculating periodic feature coefficients from the third mileage set and the initial body vertical acceleration set comprises:

taking the third mileage set as an independent variable and the first vehicle body vertical acceleration set as a dependent variable, performing equal-space resampling on the first vehicle body vertical acceleration set at a second set sampling interval, and generating a fifth mileage set and a second vehicle body vertical acceleration set, wherein the first vehicle body vertical acceleration set is generated by performing band-pass filtering processing on the initial vehicle body vertical acceleration set according to a preset cut-off frequency range;

calculating a power spectrum of the vertical acceleration of the second vehicle body by adopting an average periodogram method;

rounding the wavelength of the power spectrum which is also a maximum value and a maximum value within a first set wavelength range to obtain the characteristic wavelength of the suspected rail corrugation;

and calculating a periodic characteristic coefficient according to the power spectrum and the characteristic wavelength.

11. The rail corrugation detection method of claim 10, wherein calculating periodic feature coefficients from the power spectrum and feature wavelengths comprises:

recording a wavelength which is the same as a maximum value and is the maximum value of the power spectrum in each unit length within a second set wavelength range to form a wavelength set;

determining the serial number f of the characteristic wavelength in the wavelength set₂；

Of characteristic wavelengthThe number multiplied and integrated in the wavelength set is f₁；

The number of the characteristic wavelength 2 times in the wavelength set is f₃；

The periodic characteristic coefficient is calculated according to the following formula:

wherein F is a periodic characteristic coefficient;is the f-th in the wavelength set₁A wavelength;is the f-th in the wavelength set₂A wavelength;is the f-th in the wavelength set₃A wavelength; s_xx,kIs the kth wavelength in the wavelength set.

12. The rail corrugation detection method of claim 1, wherein screening out sections for which rail corrugation is determined to exist from the sections suspected of rail corrugation based on the periodic characterization coefficients comprises:

if the periodic characteristic coefficient of the suspected rail corrugation zone is larger than or equal to the preset periodic characteristic coefficient threshold value, the suspected rail corrugation zone is a zone determined to have rail corrugation, otherwise, the suspected rail corrugation zone is a zone determined to have no rail corrugation.

13. A rail corrugation detection apparatus, comprising:

the data acquisition module is used for acquiring a three-dimensional coordinate set, an initial vehicle speed set, an initial vehicle body vertical acceleration set, an initial vehicle body transverse acceleration set, an initial noise set and a vehicle body shaking head angular velocity set of the operation train in real time;

the first mileage set obtaining module is used for performing integral calculation on the initial vehicle speed set to obtain a first mileage set;

the correction module is used for correcting the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set;

the rail corrugation evaluation index calculation module is used for carrying out data fusion on the initial vehicle body vertical acceleration set, the initial vehicle body transverse acceleration set and the initial noise set according to the first vehicle speed set and the vehicle body shaking angular speed set and calculating a rail corrugation evaluation index;

the suspected rail corrugation zone judgment module is used for calculating a rail corrugation characteristic coefficient according to the third mileage set and the rail corrugation evaluation index and judging a suspected rail corrugation zone;

and the rail corrugation zone determining module is used for calculating a periodic characteristic coefficient according to the third mileage set and the initial vehicle body vertical acceleration set, and screening out a zone for determining the existence of rail corrugation from suspected rail corrugation zones according to the periodic characteristic coefficient.

14. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 12 when executing the computer program.

15. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 12.

Technical Field

The invention belongs to the technical field of railway track detection, and relates to a rail corrugation detection method and device.

Background

The wave-shaped abrasion of the steel rail, called steel rail corrugation for short, is the irregularity of the top surface of the steel rail which is distributed periodically along the longitudinal direction of a line and is similar to a wave shape. The wave length range of the rail corrugation is generally 30-300 mm, which is an important reason for aggravating wheel rail vibration and noise level and causing fatigue damage of structural components of a vehicle-track system and seriously harms driving safety. Therefore, scientific and efficient rail corrugation detection is one of key technologies for accurately mastering the service state of a line and further guaranteeing continuous, safe and stable operation of a train.

At present, the detection method of rail corrugation can be divided into indirect measurement method and direct measurement method according to the measurement principle. The indirect measurement method has high measurement efficiency by collecting indexes such as wheel track noise, wheel track force, axle box vibration acceleration and the like in the running process of the train, but because the wave depth of the rail corrugation is in the micron level, the collected signals are greatly interfered by external factors, so the detection precision is low, and the method is mainly used for qualitatively judging the influence of the rail corrugation on the running quality. In addition, such detection equipment is often mounted on an axle box or adopts a force measuring wheel pair, and the development, maintenance and repair costs are high; and has strict equipment installation requirements, and the test environment is relatively severe. The direct measurement method can be divided into two methods, firstly, the unevenness of the rail surface is directly measured by adopting an electronic straight ruler and a corrugation trolley on a line manually. Its advantages are high visual and precision; however, manual operation is required to be carried out on line in a 'skylight' period of line maintenance, and the labor cost is high; and only a short interval can be measured, the whole line cannot be measured, and the coverage and the efficiency of detection are very low. And the other method is to detect the rail corrugation characteristic parameters by means of a special detection train based on an inertia reference method, an axle box acceleration integral method, a chord measurement method or a machine vision method. The inertia reference method is that the position of the inertia reference is obtained by carrying out secondary integration on the signals collected by the accelerometer at the bottom plate of the carriage, and then the displacement of the bottom plate of the carriage relative to the axle box of the train measured by the displacement meter is superposed, so that the measured value of the rail corrugation is obtained. And directly carrying out secondary integration on the signals acquired by the accelerometer at the axle box of the train by using an axle box acceleration integration method to obtain the rail corrugation measurement value. The chord measuring method constructs a measuring chord on the surface of the steel rail and measures the distance between the chord and the surface of the steel rail. The machine vision method combines laser shooting and machine learning to measure the corrugation characteristics, but is easily influenced by factors such as image quality, external illumination conditions, surface dirt of the steel rail and the like, and the measurement is unstable and has poor precision.

More importantly, the various vehicle-mounted detection devices need to be installed outside the carriages such as axle boxes and bogies of special detection trains, have high installation requirements and certain safety risks, have strict requirements on maintenance and need to replace sensors frequently; in addition, the special detection vehicle needs to occupy the running time of an operation train, so that the transportation capacity of the line is reduced, the inspection is only performed once every quarter, and the development state of the rail corrugation is difficult to grasp in time.

Disclosure of Invention

The embodiment of the invention provides a rail corrugation detection method, which is used for grasping rail corrugation in real time during railway line operation and can be carried in an operation train carriage, and the method comprises the following steps:

acquiring a three-dimensional coordinate set, an initial vehicle speed set, an initial vehicle vertical acceleration set, an initial vehicle transverse acceleration set, an initial noise set and a vehicle shaking angle speed set of an operating train in real time;

performing integral calculation on the initial vehicle speed set to obtain a first mileage set;

correcting the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set;

according to the first vehicle speed set and the vehicle body shaking angular speed set, performing data fusion on an initial vehicle body vertical acceleration set, an initial vehicle body transverse acceleration set and an initial noise set, and calculating a rail corrugation evaluation index;

calculating a rail corrugation characteristic coefficient according to the third mileage set and the rail corrugation evaluation index, and judging a suspected rail corrugation zone;

and calculating a periodic characteristic coefficient according to the third mileage set and the initial vehicle body vertical acceleration set, and screening out a section determined to have the rail corrugation from suspected rail corrugation sections according to the periodic characteristic coefficient.

An embodiment of the present invention provides a rail corrugation detection apparatus, which is used for grasping rail corrugation in real time during railway line operation and can be loaded in an operating train carriage, and the apparatus includes:

the data acquisition module is used for acquiring a three-dimensional coordinate set, an initial vehicle speed set, an initial vehicle body vertical acceleration set, an initial vehicle body transverse acceleration set, an initial noise set and a vehicle body shaking head angular velocity set of the operation train in real time;

the first mileage set obtaining module is used for performing integral calculation on the initial vehicle speed set to obtain a first mileage set;

the correction module is used for correcting the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set;

the rail corrugation evaluation index calculation module is used for carrying out data fusion on the initial vehicle body vertical acceleration set, the initial vehicle body transverse acceleration set and the initial noise set according to the first vehicle speed set and the vehicle body shaking angular speed set and calculating a rail corrugation evaluation index;

the suspected rail corrugation zone judgment module is used for calculating a rail corrugation characteristic coefficient according to the third mileage set and the rail corrugation evaluation index and judging a suspected rail corrugation zone;

and the rail corrugation zone determining module is used for calculating a periodic characteristic coefficient according to the third mileage set and the initial vehicle body vertical acceleration set, and screening out a zone for determining the existence of rail corrugation from suspected rail corrugation zones according to the periodic characteristic coefficient.

The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores a computer program for executing the rail corrugation detection method.

In the embodiment of the invention, a three-dimensional coordinate set, an initial vehicle speed set, an initial vehicle body vertical acceleration set, an initial vehicle body transverse acceleration set, an initial noise set and a vehicle body shaking head angular velocity set of an operation train are obtained in real time; performing integral calculation on the initial vehicle speed set to obtain a first mileage set; correcting the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set; according to the first vehicle speed set and the vehicle body shaking angular speed set, performing data fusion on an initial vehicle body vertical acceleration set, an initial vehicle body transverse acceleration set and an initial noise set, and calculating a rail corrugation evaluation index; calculating a rail corrugation characteristic coefficient according to the third mileage set and the rail corrugation evaluation index, and judging a suspected rail corrugation zone; and calculating a periodic characteristic coefficient according to the third mileage set and the initial vehicle body vertical acceleration set, and screening out a section determined to have the rail corrugation from suspected rail corrugation sections according to the periodic characteristic coefficient. In the embodiment, the three-dimensional coordinate set, the initial vehicle speed set, the initial vehicle body vertical acceleration set, the initial vehicle body transverse acceleration set, the initial noise set and the vehicle body shaking angular velocity set of the operation train can be obtained in real time without modifying the operation train, and the rail corrugation evaluation index and the rail corrugation characteristic coefficient are provided for evaluating the influence of the rail corrugation on the vehicle vibration and noise response, so that the problem that the absolute threshold is difficult to determine is solved; and proposing a periodic characteristic coefficient to represent periodic characteristics of the rail corrugation, and determining a rail corrugation section. Finally, the rail corrugation state can be accurately detected, and the problems that the conventional detection equipment is high in research, development, manufacturing and maintenance cost and long in detection period are solved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:

FIG. 1 is a flow chart of a rail corrugation detection method in an embodiment of the present invention;

FIG. 2 is a flow chart of data modification in an embodiment of the present invention;

FIG. 3 is a flow chart of mileage calibration in an embodiment of the invention;

FIG. 4 is a general flow chart of the calculation of the rail corrugation evaluation index in the embodiment of the present invention;

FIG. 5 is a flowchart of calculating a rail corrugation evaluation index by data fusion in an embodiment of the present invention;

FIG. 6 is a flowchart illustrating a method for determining suspected rail corrugation zones in accordance with an embodiment of the present invention;

FIG. 7 is a flow chart of calculating periodic feature coefficients in an embodiment of the present invention;

FIG. 8 is a schematic view of a rail corrugation detection apparatus in an embodiment of the present invention;

FIG. 9 is a schematic diagram of a data acquisition structure according to an embodiment of the present invention;

FIG. 10 is a schematic diagram of a comparison of measured train mileage and vehicle speed with a dynamic inspection vehicle integrated system according to the present invention in an embodiment of the present invention;

FIG. 11 is a schematic diagram of an initial vertical vibration acceleration set, an initial lateral vibration acceleration set and an initial noise set of the high-speed railway K989+ 000-K995 +500 sections, which are acquired in the embodiment of the present invention;

FIG. 12 is a schematic illustration of a first vehicle body vertical acceleration group, a first vehicle body lateral acceleration group, and a first noise group in accordance with an embodiment of the present invention;

FIG. 13 is a schematic representation of a set of vehicle body vertical acceleration glide valid values, a set of vehicle body lateral acceleration glide valid values, and a set of noise glide valid values, in accordance with an embodiment of the present invention;

FIG. 14 is a schematic diagram of the angular velocity of the shaking head of the car body measured by the fiber-optic gyroscope according to the embodiment of the invention;

FIG. 15 is a schematic diagram of the rail corrugation evaluation index calculated in the embodiment of the present invention;

FIG. 16 is a schematic illustration of a suspected rail corrugation segment screened in accordance with an embodiment of the present invention;

FIG. 17 is a diagram illustrating the periodic coefficient of features of section iv in accordance with an embodiment of the present invention;

FIG. 18 is a schematic illustration of rail surface irregularity in an embodiment of the present invention;

FIG. 19 is a spectrum diagram of rail surface irregularity according to an embodiment of the present invention;

FIG. 20 is a diagram of a computer device in an embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.

In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are used in an open-ended fashion, i.e., to mean including, but not limited to. Reference to the description of the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the various embodiments is provided to schematically illustrate the practice of the invention, and the sequence of steps is not limited and can be suitably adjusted as desired.

Fig. 1 is a flowchart of a rail corrugation detection method in an embodiment of the present invention, and as shown in fig. 1, the method includes:

101, acquiring a three-dimensional coordinate set, an initial vehicle speed set, an initial vehicle vertical acceleration set, an initial vehicle transverse acceleration set, an initial noise set and a vehicle shaking angle speed set of an operating train in real time;

102, performing integral calculation on the initial vehicle speed set to obtain a first mileage set;

103, correcting the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set;

104, performing data fusion on the initial vehicle body vertical acceleration set, the initial vehicle body transverse acceleration set and the initial noise set according to the first vehicle speed set and the vehicle body shaking angular speed set, and calculating a rail corrugation evaluation index;

105, calculating a rail corrugation characteristic coefficient according to the third mileage set and the rail corrugation evaluation index, and judging a suspected rail corrugation zone;

and 106, calculating a periodic characteristic coefficient according to the third mileage set and the initial vehicle body vertical acceleration set, and screening out a section determined to have rail corrugation from suspected rail corrugation sections according to the periodic characteristic coefficient.

In the embodiment, the three-dimensional coordinate set, the initial vehicle speed set, the initial vehicle vertical acceleration set, the initial vehicle transverse acceleration set, the initial noise set and the vehicle shaking angular velocity set of the operation train can be obtained in real time, so that the acquisition module can be carried in a carriage of the operation train, a data acquisition method is adopted, then, accurate rail corrugation can be obtained through the calculation process, and the real-time performance is good.

In one embodiment, the method for acquiring a three-dimensional coordinate set, an initial vehicle speed set, an initial vehicle vertical acceleration set, an initial vehicle lateral acceleration set, an initial noise set and a vehicle shaking head angular velocity set of an operating train in real time includes:

acquiring three-dimensional coordinate data and initial speed data of an operating train in real time by adopting a GNSS positioning module;

acquiring an initial train body vertical acceleration set and an initial train body transverse acceleration set of an operating train in real time by adopting an acceleration sensor;

acquiring an initial noise set of an operating train in real time by adopting a sound pressure sensor, wherein the sound pressure sensor vertically downwards measures noise transmitted to a carriage bottom plate from a wheel-rail contact surface;

and acquiring a vehicle body shaking angular velocity set of the operation train in real time by adopting a fiber optic gyroscope.

In the above embodiment, the mileage at the starting position of the operating train is set before data collectionAnd the mileage is increased or decreased according to the running direction of the train. The method comprises the steps of adopting a high-precision GNSS positioning module to obtain a three-dimensional coordinate set of a vehicle in real time in the running process of a train (x)_i,y_i,z_i) I-1, 2, …, M and an initial set of vehicle speeds { v }_iI is 1,2, …, M }. The GNSS positioning module can receive satellite signals of four GNSS systems of GPS, GLONASS, BDS and GALILEO, and simultaneously supports differential positioning, and the positioning precision reaches centimeter level.

In step 102, integral calculation is performed on the initial vehicle speed set to obtain a first mileage set, and in specific implementation, integral calculation is performed on the initial vehicle speed set once according to the following formula to obtain the first mileage setThe increment distance is "+", the decrement distance is "-", and delta t is the sampling time interval:

fig. 2 is a flowchart of data modification in an embodiment of the present invention, and as shown in fig. 2, in an embodiment, the modifying the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set includes:

step 201, calibrating a first mileage set according to a three-dimensional coordinate set and a coordinate set of a line center line coordinate point library to obtain a second mileage set;

and 202, performing linear interpolation on the second mileage set and the initial vehicle speed set according to the sampling frequency of the vehicle body vibration data to generate a third mileage set and a first vehicle speed set.

Fig. 3 is a flowchart of mileage calibration according to an embodiment of the present invention, and as shown in fig. 3, in an embodiment, calibrating a first mileage set according to a three-dimensional coordinate set and a coordinate set of a line center line coordinate point library to obtain a second mileage set includes:

step 301, in a three-dimensional coordinate set { (x)_i,y_i,z_i) And i is 1,2, …, M, screening out a coordinate set { (X) of a line-to-line centerline coordinate point library_j,Y_j,Z_j) J is the closest 1,2, …, N and satisfies the two-point plane coordinate distanceLess than mileage calibration error threshold d₀Forming a first mileage calibration point group, whose coordinates are { (x)_i,y_i,z_i),i＝1,2,…,M₁}，M₁≤M；

Step 302, aggregating the mileage of the first mileage calibration point groupCorrecting the mileage set to be consistent with the mileage of the corresponding line center line coordinate point, wherein the mileage set of the first mileage calibration point group comprises mileage errors of a plurality of calibration points;

step 303, aggregating from the first mileageUsing the mileage error of the 1 st calibration point to correct the first mileage data between the starting point and the 2 nd calibration point, wherein the first mileage set comprises a plurality of first mileage data;

step 304, repeatedly executing the following steps until the first mileage data in the first mileage set is completely corrected to form a second mileage set

And correcting the first mileage data between the nth calibration point and the (n + 1) th calibration point by using the mileage error of the nth calibration point, wherein n is more than or equal to 2.

In the implementation of the invention, equal time interval sampling is adopted, wherein the sampling frequency Fs of the GNSS positioning module_MSampling frequency Fs of specific vehicle body vibration data_PThe GNSS positioning module is low in cost, the number of sampling points of the GNSS positioning module is M, the number of sampling points of the vehicle body vibration data is P, and M is equal to P. In order to endow each acceleration sensor, sound pressure sensor and the sampling point of the later related optical fiber gyro sensor data with mileage and vehicle speed information, a second mileage set is neededAnd initial set of vehicle speeds { v }_iI-1, 2, …, M } at a sampling frequency Fs_PPerforming linear interpolation to generate a third mileage setAnd a first set of vehicle speeds

The process is a process of a high-precision data mileage positioning method.

Fig. 4 is a general flowchart of calculation of a rail corrugation evaluation index in an embodiment of the present invention, and as shown in fig. 4, in an embodiment, according to a first vehicle speed set and a vehicle body yaw angular velocity set, data fusion is performed on an initial vehicle body vertical acceleration set, an initial vehicle body lateral acceleration set, and an initial noise set, so as to calculate the rail corrugation evaluation index, where the method includes:

step 401, performing band-pass filtering processing on the initial vehicle body vertical acceleration set, the initial vehicle body lateral acceleration set and the initial noise set according to a preset cut-off frequency range to generate a first vehicle body vertical acceleration setFirst vehicle body lateral acceleration setFirst set of noise

Step 402, respectively calculating sliding effective values of a first vehicle body vertical acceleration set, a first vehicle body transverse acceleration set and a first noise set, and generating a vehicle body vertical acceleration sliding effective value set, a vehicle body transverse acceleration sliding effective value set and a noise sliding effective value set;

the calculation window length of the sliding effective value, namely the number a of sampling points contained in one sliding window, is specified. And specifying the sliding distance of the sliding effective value, namely the number B of sampling points contained in the phase difference of two adjacent sliding windows. The number of sliding windows included in the P data is C. Assume the raw data set is: { x_iI is 1,2, …, P, the effective sliding value is calculated quickly as follows: calculating the sum of squares of the data in the 1 st sliding windowAnd calculating the 1 st significant valueLet j increment from 2 to C sequentially, the jth sum of squares beingThe jth effective value is

Processing the first vehicle body vertical acceleration set, the first vehicle body transverse acceleration set and the first noise set according to the sliding effective value rapid calculation method to generate a vehicle body vertical acceleration sliding effective value setVehicle body transverse acceleration sliding effective value setNoise sliding valid value set

And 403, performing data fusion on the vehicle body vertical acceleration sliding effective value set, the vehicle body transverse acceleration sliding effective value set and the noise sliding effective value set based on the first vehicle speed set and the vehicle body shaking head angular speed set, and calculating the rail corrugation evaluation index.

Fig. 5 is a flowchart of calculating a rail corrugation evaluation index through data fusion in an embodiment of the present invention, and as shown in fig. 5, in an embodiment, based on a first vehicle speed set and a vehicle body yaw angular velocity set, data fusion is performed on a vehicle body vertical acceleration sliding effective value set, a vehicle body lateral acceleration sliding effective value set, and a noise sliding effective value set, so as to calculate the rail corrugation evaluation index, where the method includes:

in specific implementation, the angular velocity set { r } of the vehicle body swing head can be obtained by adopting fiber-optic gyroscope measurement_z,i,i＝1,2,…,P}。

Step 501, with a first cut-off frequency F_RTo the vehicle body shaking head angular velocity setLow-pass filtering is carried out to generate a first vehicle body yaw rate set

Step 502, sampling the first vehicle speed set and the first vehicle body yaw rate set, extracting the data corresponding to the point of the center position of each sliding window to obtain a second vehicle speed setAnd a second set of body pan angular velocities

Step 503, determining a vehicle body lateral acceleration weight coefficient according to the second vehicle body yaw angular velocity set;

because the corrugation is mostly generated at the small-radius curve, the corrugation on the curve also contributes to the transverse acceleration of the vehicle body. Therefore, the second vehicle body yaw rate is processed to determine the weight coefficient s of the lateral acceleration of the vehicle body in the curve section using the following formula_y,jIn the formula, r₁Is a fixed constant.

And step 504, calculating the rail corrugation evaluation index according to the second vehicle speed set, the vehicle body transverse acceleration weight coefficient, the vehicle body vertical acceleration sliding effective value set, the vehicle body transverse acceleration sliding effective value set and the noise sliding effective value set.

In one embodiment, the following formula is adopted to calculate the rail corrugation evaluation index according to the second vehicle speed set, the vehicle body lateral acceleration weight coefficient, the vehicle body vertical acceleration sliding effective value set, the vehicle body lateral acceleration sliding effective value set and the noise sliding effective value set:

wherein, V_jThe evaluation index of the rail corrugation is obtained; s_z,j＝1-s_y,jThe weight coefficient is the vertical acceleration of the vehicle body; s_y,jThe weight coefficient is the lateral acceleration of the vehicle body; s_aIs a vibration index weight coefficient; s_noIs a noise index weight coefficient;the effective value of the vertical acceleration sliding of the vehicle body is;the sliding effective value of the transverse acceleration of the vehicle body is;is a noise sliding effective value; v. of_jIs the third vehicle speed. s_aAnd s_noAnd calibrating according to the vibration and noise level of the train which has the departure condition and is static. The vibration and noise levels caused by different vehicle speeds are different, where the division isIs for speed normalization.

Fig. 6 is a flowchart illustrating a process of determining a suspected rail corrugation segment according to an embodiment of the present invention, as shown in fig. 6, in an embodiment, calculating a rail corrugation feature coefficient according to a third mileage set and a rail corrugation evaluation index, and determining the suspected rail corrugation segment includes:

step 601, sampling the third mileage set, extracting the data corresponding to the point of each sliding window center position to obtain a fourth mileage set

Step 602, integrating the fourth mileage setThe sum is used as an independent variable, the rail corrugation evaluation index is used as a dependent variable, the rail corrugation evaluation index is subjected to equal space resampling at a first set sampling interval, and a fifth course set is generatedAnd second rail corrugation evaluation index

In the embodiment of the present invention, the first set sampling interval is 1m,

step 603, low-pass filtering the second rail corrugation evaluation index at a second cut-off frequency to generate a third rail corrugation evaluation index

Step 604, calculating the average value of the third rail corrugation evaluation index

605, calculating a rail corrugation characteristic coefficient according to the third rail corrugation evaluation index and the average value of the third rail corrugation evaluation index;

in one embodiment, the rail corrugation characteristic coefficient is calculated from the average of the third rail corrugation evaluation index and the third rail corrugation evaluation index:

wherein E is_kThe characteristic coefficient of the rail corrugation is taken as the coefficient; v_kThe third rail corrugation evaluation index; v₀The average value of the third rail corrugation evaluation index is shown.

Step 606, determining that the continuous set length exceeds a preset rail corrugation characteristic coefficient threshold E₀The section (2) is a section suspected of rail corrugation.

The rail corrugation characteristic coefficient has the advantage that a multi-index comprehensive evaluation index with an absolute threshold value difficult to determine is converted into a relative index with a threshold value easy to determine.

Fig. 7 is a flowchart of calculating a periodic feature coefficient according to an embodiment of the present invention, and as shown in fig. 7, in an embodiment, calculating a periodic feature coefficient according to a third mileage set and an initial vehicle body vertical acceleration set includes:

step 701, taking the third mileage set as an independent variable and the first vehicle body vertical acceleration set as a dependent variable, and performing equal-space resampling on the first vehicle body vertical acceleration set at a second set sampling interval to generate a fifth mileage setAnd a second vehicle body vertical acceleration setThe first vehicle body vertical acceleration set is generated by performing band-pass filtering processing on the initial vehicle body vertical acceleration set according to a preset cut-off frequency range;

in an embodiment of the invention, the second set sampling interval is 0.5 mm.

Step 702, calculating the power spectrum { S ] of the vertical acceleration of the second vehicle body by using an average periodogram method_xx,jJ is 1,2, …, F, and the corresponding spatial frequency is { F }_xx,jJ is 1,2, …, F, and the reciprocal of the spatial frequency is the wavelength, denoted as { l }_xx,j,j＝1,2,…,F}；

703, rounding the wavelength of the power spectrum which is the same as the maximum value and the maximum value in the first set wavelength range to obtain the characteristic wavelength l of the suspected rail corrugation_xx,0；

In the embodiment of the present invention, the first set wavelength range is [30mm,300mm ], and rounding is performed.

Step 704, calculating a periodic characteristic coefficient according to the power spectrum and the characteristic wavelength.

In one embodiment, calculating a periodic characteristic coefficient from the power spectrum and the characteristic wavelength includes:

in the second set wavelength range, a wavelength having the same maximum value and the maximum value of the power spectrum is recorded per unit length, and may be recorded as { S }_xx,kK is 1,2, …,586}, forming a set of wavelengths {15,16, …,600 };

if there is no power spectrum having both the maximum value and the maximum value in a certain wavelength range, S is the maximum value_xx,kAnd recorded as 0.

Determination of the characteristic wavelength l_xx,0Number f in wavelength set₂；

The characteristic wavelength l_xx,0Is/are as followsThe number multiplied and integrated in the wavelength set is f₁；

The characteristic wavelength l_xx,02 times in the wavelength set is numbered f₃；

The periodic characteristic coefficient is calculated according to the following formula:

wherein F is a periodic characteristic coefficient;is the f-th in the wavelength set₁A wavelength;is the f-th in the wavelength set₂A wavelength;is the f-th in the wavelength set₃A wavelength; s_xx,kIs the kth wavelength in the wavelength set.

In specific implementation, the second set wavelength range is [15mm,600mm ]]. The periodic characteristic coefficient expresses the energy sum of the rail corrugation characteristic wavelength and the grinding trace characteristic wavelength at the wavelength [30 ],300]The ratio of the total energy of all characteristic wavelengths in mm range. Therefore, the periodic characteristic coefficient reflects the characteristic wavelength l_xx,0The degree of the periodic characteristics of (a). According to a preset periodic characteristic coefficient threshold value F₀And determining whether the suspected rail corrugation zone has rail corrugation.

In one embodiment, screening out the sections determined to have rail corrugation from the sections suspected of having rail corrugation according to the periodic characteristic coefficients comprises:

if the periodic characteristic coefficient F of the suspected rail corrugation zone is larger than or equal to the preset periodic characteristic coefficient threshold value F₀If the section is a section determined to have rail corrugation, otherwise, the section is a section determined to have no rail corrugation.

In summary, in the method provided by the embodiment of the present invention, a three-dimensional coordinate set, an initial vehicle speed set, an initial vehicle vertical acceleration set, an initial vehicle lateral acceleration set, an initial noise set, and a vehicle oscillation angular velocity set of an operating train are obtained in real time; performing integral calculation on the initial vehicle speed set to obtain a first mileage set; correcting the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set; according to the first vehicle speed set and the vehicle body shaking angular speed set, performing data fusion on an initial vehicle body vertical acceleration set, an initial vehicle body transverse acceleration set and an initial noise set, and calculating a rail corrugation evaluation index; calculating a rail corrugation characteristic coefficient according to the third mileage set and the rail corrugation evaluation index, and judging a suspected rail corrugation zone; and calculating a periodic characteristic coefficient according to the third mileage set and the initial vehicle body vertical acceleration set, and screening out a section determined to have the rail corrugation from suspected rail corrugation sections according to the periodic characteristic coefficient. In the embodiment, the three-dimensional coordinate set, the initial vehicle speed set, the initial vehicle body vertical acceleration set, the initial vehicle body transverse acceleration set, the initial noise set and the vehicle body shaking angular velocity set of the operation train can be obtained in real time without modifying the operation train, and the rail corrugation evaluation index and the rail corrugation characteristic coefficient are provided for evaluating the influence of the rail corrugation on the vehicle vibration and noise response, so that the problem that the absolute threshold is difficult to determine is solved; and proposing a periodic characteristic coefficient to represent periodic characteristics of the rail corrugation, and determining a rail corrugation section. Finally, the rail corrugation state can be accurately detected, and the problems that the conventional detection equipment is high in research, development, manufacturing and maintenance cost and long in detection period are solved.

The embodiment of the invention also provides a rail corrugation detection device, the principle of which is similar to that of a rail corrugation detection method, and the details are not repeated.

Fig. 8 is a schematic diagram of a rail corrugation detection apparatus according to an embodiment of the present invention, and as shown in fig. 8, the apparatus includes:

the data acquisition module 801 is used for acquiring a three-dimensional coordinate set, an initial vehicle speed set, an initial vehicle body vertical acceleration set, an initial vehicle body lateral acceleration set, an initial noise set and a vehicle body shaking angle speed set of the operation train in real time;

a first mileage set obtaining module 802, configured to perform integral calculation on the initial vehicle speed set to obtain a first mileage set;

the correcting module 803 is configured to correct the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set, so as to obtain a third mileage set and a first vehicle speed set;

the rail corrugation evaluation index calculation module 804 is used for performing data fusion on the initial vehicle body vertical acceleration set, the initial vehicle body transverse acceleration set and the initial noise set according to the first vehicle speed set and the vehicle body shaking angular speed set, and calculating a rail corrugation evaluation index;

a suspected rail corrugation zone judgment module 805, configured to calculate a rail corrugation feature coefficient according to the third mileage set and the rail corrugation evaluation index, and judge a suspected rail corrugation zone;

and the rail corrugation zone determining module 806 is configured to calculate a periodic characteristic coefficient according to the third mileage set and the initial vehicle body vertical acceleration set, and screen out a zone where rail corrugation is determined to exist from suspected rail corrugation zones according to the periodic characteristic coefficient.

Fig. 9 is a schematic structural diagram of data acquisition in an embodiment of the present invention, where a data acquisition system includes a plurality of sensors, and in an embodiment, a data acquisition module is specifically configured to:

acquiring three-dimensional coordinate data and initial speed data of an operating train in real time by adopting a GNSS positioning module;

acquiring an initial train body vertical acceleration set and an initial train body transverse acceleration set of an operating train in real time by adopting an acceleration sensor;

acquiring an initial noise set of an operating train in real time by adopting a sound pressure sensor, wherein the sound pressure sensor vertically downwards measures noise transmitted to a carriage bottom plate from a wheel-rail contact surface;

and acquiring a vehicle body shaking angular velocity set of the operation train in real time by adopting a fiber optic gyroscope.

In an embodiment, the modification module is specifically configured to:

calibrating the first mileage set according to the three-dimensional coordinate set and the coordinate set of the line center line coordinate point library to obtain a second mileage set;

and performing linear interpolation on the second mileage set and the initial vehicle speed set according to the sampling frequency of the vehicle body vibration data to generate a third mileage set and a first vehicle speed set.

In an embodiment, the modification module is specifically configured to:

screening out a coordinate point group which is closest to a coordinate set of a line center line coordinate point library in the three-dimensional coordinate set and meets the condition that the distance between plane coordinates of two points is smaller than a mileage calibration error threshold value, and forming a first mileage calibration point group;

correcting a mileage set of a first mileage calibration point group into a mileage set consistent with the mileage of a corresponding line center line coordinate point, wherein the mileage set of the first mileage calibration point group comprises mileage errors of a plurality of calibration points;

correcting first mileage data between the starting point and the 2 nd calibration point by using a mileage error of the 1 st calibration point from the starting point of a first mileage set including a plurality of first mileage data;

repeatedly executing the following steps until the first mileage data in the first mileage set is corrected, and forming a second mileage set:

and correcting the first mileage data between the nth calibration point and the (n + 1) th calibration point by using the mileage error of the nth calibration point, wherein n is more than or equal to 2.

In an embodiment, the rail corrugation evaluation index calculation module is specifically configured to:

performing band-pass filtering processing on the initial vehicle body vertical acceleration set, the initial vehicle body transverse acceleration set and the initial noise set according to a preset cut-off frequency range to generate a first vehicle body vertical acceleration set, a first vehicle body transverse acceleration set and a first noise set;

respectively calculating sliding effective values of a first vehicle body vertical acceleration set, a first vehicle body transverse acceleration set and a first noise set to generate a vehicle body vertical acceleration sliding effective value set, a vehicle body transverse acceleration sliding effective value set and a noise sliding effective value set;

and performing data fusion on the vehicle body vertical acceleration sliding effective value set, the vehicle body transverse acceleration sliding effective value set and the noise sliding effective value set based on the first vehicle speed set and the vehicle body shaking head angular speed set, and calculating the rail corrugation evaluation index.

In an embodiment, the rail corrugation evaluation index calculation module is specifically configured to:

carrying out low-pass filtering on the vehicle body shaking angular velocity set at a first cut-off frequency to generate a first vehicle body shaking angular velocity set;

sampling the first vehicle speed set and the first vehicle body yaw angular speed set, and extracting data corresponding to a point at the center position of each sliding window to obtain a second vehicle speed set and a second vehicle body yaw angular speed set;

determining a vehicle body lateral acceleration weight coefficient according to the second vehicle body yaw angular velocity set;

and calculating the rail corrugation evaluation index according to the second vehicle speed set, the vehicle body transverse acceleration weight coefficient, the vehicle body vertical acceleration sliding effective value set, the vehicle body transverse acceleration sliding effective value set and the noise sliding effective value set.

In an embodiment, the rail corrugation evaluation index calculation module is specifically configured to:

calculating a rail corrugation evaluation index according to a second vehicle speed set, a vehicle body transverse acceleration weight coefficient, a vehicle body vertical acceleration sliding effective value set, a vehicle body transverse acceleration sliding effective value set and a noise sliding effective value set by adopting the following formula:

wherein, V_jThe evaluation index of the rail corrugation is obtained; s_z,j＝1-s_y,jThe weight coefficient is the vertical acceleration of the vehicle body; s_y,jThe weight coefficient is the lateral acceleration of the vehicle body; s_aIs a vibration index weight coefficient; s_noIs a noise index weight coefficient;the effective value of the vertical acceleration sliding of the vehicle body is;the sliding effective value of the transverse acceleration of the vehicle body is;is a noise sliding effective value; v. of_jIs the second vehicle speed.

In one embodiment, the suspected rail corrugation segment determination module is specifically configured to:

sampling the third mileage set, and extracting data corresponding to the point of the center position of each sliding window to obtain a fourth mileage set;

taking the fourth mileage set as an independent variable and the rail corrugation evaluation index as a dependent variable, and performing equal-space resampling on the rail corrugation evaluation index at a first set sampling interval to generate a fifth mileage set and a second rail corrugation evaluation index;

performing low-pass filtering on the second rail corrugation evaluation index at a second cut-off frequency to generate a third rail corrugation evaluation index;

calculating the average value of the third rail corrugation evaluation index;

calculating a rail corrugation characteristic coefficient according to the third rail corrugation evaluation index and the average value of the third rail corrugation evaluation index;

and determining the section which is more than the preset rail corrugation characteristic coefficient threshold value and longer than the continuous set length as the suspected rail corrugation section.

In one embodiment, the suspected rail corrugation segment determination module is specifically configured to:

calculating the rail corrugation characteristic coefficient according to the third rail corrugation evaluation index and the average value of the third rail corrugation evaluation index by adopting the following formula:

wherein E is_kThe characteristic coefficient of the rail corrugation is taken as the coefficient; v_kThe third rail corrugation evaluation index; v₀The average value of the third rail corrugation evaluation index is shown.

In one embodiment, the rail corrugation segment determination module is specifically configured to:

taking the third mileage set as an independent variable and the first vehicle body vertical acceleration set as a dependent variable, performing equal-space resampling on the first vehicle body vertical acceleration set at a second set sampling interval, and generating a fifth mileage set and a second vehicle body vertical acceleration set, wherein the first vehicle body vertical acceleration set is generated by performing band-pass filtering processing on the initial vehicle body vertical acceleration set according to a preset cut-off frequency range;

calculating a power spectrum of the vertical acceleration of the second vehicle body by adopting an average periodogram method;

rounding the wavelength of the power spectrum which is also a maximum value and a maximum value within a first set wavelength range to obtain the characteristic wavelength of the suspected rail corrugation;

and calculating a periodic characteristic coefficient according to the power spectrum and the characteristic wavelength.

In one embodiment, the rail corrugation segment determination module is specifically configured to:

recording a wavelength which is the same as a maximum value and is the maximum value of the power spectrum in each unit length within a second set wavelength range to form a wavelength set;

determining the serial number f of the characteristic wavelength in the wavelength set₂；

Of characteristic wavelengthThe number multiplied and integrated in the wavelength set is f₁；

The number of the characteristic wavelength 2 times in the wavelength set is f₃；

The periodic characteristic coefficient is calculated according to the following formula:

the periodic characteristic coefficient is calculated according to the following formula:

wherein F is a periodic characteristic coefficient;is the f-th in the wavelength set₁A wavelength;is the f-th in the wavelength set₂A wavelength;is the f-th in the wavelength set₃A wavelength; s_xx,kIs the kth wavelength in the wavelength set.

In one embodiment, the rail corrugation segment determination module is specifically configured to:

if the periodic characteristic coefficient of the suspected rail corrugation zone is larger than or equal to the preset periodic characteristic coefficient threshold value, the suspected rail corrugation zone is a zone determined to have rail corrugation, otherwise, the suspected rail corrugation zone is a zone determined to have no rail corrugation.

In summary, in the apparatus provided in the embodiment of the present invention, a three-dimensional coordinate set, an initial vehicle speed set, an initial vehicle vertical acceleration set, an initial vehicle lateral acceleration set, an initial noise set, and a vehicle oscillation angular velocity set of an operating train are obtained in real time; performing integral calculation on the initial vehicle speed set to obtain a first mileage set; correcting the first mileage set and the initial vehicle speed set based on the three-dimensional coordinate set to obtain a third mileage set and a first vehicle speed set; according to the first vehicle speed set and the vehicle body shaking angular speed set, performing data fusion on an initial vehicle body vertical acceleration set, an initial vehicle body transverse acceleration set and an initial noise set, and calculating a rail corrugation evaluation index; calculating a rail corrugation characteristic coefficient according to the third mileage set and the rail corrugation evaluation index, and judging a suspected rail corrugation zone; and calculating a periodic characteristic coefficient according to the third mileage set and the initial vehicle body vertical acceleration set, and screening out a section determined to have the rail corrugation from suspected rail corrugation sections according to the periodic characteristic coefficient. In the embodiment, the three-dimensional coordinate set, the initial vehicle speed set, the initial vehicle body vertical acceleration set, the initial vehicle body transverse acceleration set, the initial noise set and the vehicle body shaking angular velocity set of the operation train can be obtained in real time without modifying the operation train, and the rail corrugation evaluation index and the rail corrugation characteristic coefficient are provided for evaluating the influence of the rail corrugation on the vehicle vibration and noise response, so that the problem that the absolute threshold is difficult to determine is solved; and proposing a periodic characteristic coefficient to represent periodic characteristics of the rail corrugation, and determining a rail corrugation section. Finally, the rail corrugation state can be accurately detected, and the problems that the conventional detection equipment is high in research, development, manufacturing and maintenance cost and long in detection period are solved.

The following description of the embodiments of the present invention is made with reference to the accompanying drawings and examples:

in a first aspect, the high-precision data mileage positioning method provided by the invention is verified.

The rail corrugation detection device provided by the embodiment of the invention is carried in a carriage of a certain train of high-speed railway, and the real-time positioning information of the vehicle acquired from K930+ 000-K1010 +000 sections of a certain high-speed railway is processed to obtain the corresponding relation between the mileage of a dynamic inspection vehicle and the vehicle speed, and fig. 10 is a comparison schematic diagram of the train mileage and the vehicle speed measured by the invention in the embodiment of the invention and a dynamic inspection vehicle comprehensive system, and the two show high consistency, which shows that the train mileage and the speed information measured by the invention have high precision.

In a second aspect, the rail corrugation detection method provided by the invention is verified.

Fig. 11 is a schematic diagram of an initial vertical body vibration acceleration set, an initial lateral body vibration acceleration set and an initial noise set of the high-speed railway K989+ 000-K995 +500 section collected in the embodiment of the present invention, which are respectively shown in (a), (b) and (c) of fig. 11.

Performing band-pass filtering processing on the initial vehicle body vertical acceleration set, the initial vehicle body transverse acceleration set and the initial noise set according to a preset cut-off frequency range to generate a first vehicle body vertical acceleration set, a first vehicle body transverse acceleration set and a first noise set; fig. 12 is a schematic diagram of a first vehicle body vertical acceleration set, a first vehicle body lateral acceleration set and a first noise set in the embodiment of the invention, which are respectively shown in (a), (b) and (c) of fig. 12.

Processing the first vehicle body vertical acceleration set, the first vehicle body lateral acceleration set and the first noise set obtained by band-pass filtering to generate a vehicle body vertical acceleration sliding effective value set, a vehicle body lateral acceleration sliding effective value set and a noise sliding effective value set, where fig. 13 is a schematic diagram of the vehicle body vertical acceleration sliding effective value set, the vehicle body lateral acceleration sliding effective value set and the noise sliding effective value set in the embodiment of the present invention, and is respectively shown in (a), (b) and (c) in fig. 13.

FIG. 14 is a schematic diagram of the angular velocity of the shaking head of the car body measured by the fiber-optic gyroscope according to the embodiment of the invention, which is sampled after being low-pass filtered, and the lateral acceleration of the car body is measuredWeight coefficient s_y,jConstant r in the calculation formula₁Was taken as 5. Fig. 15 is a schematic diagram of the rail corrugation evaluation index calculated in the embodiment of the present invention.

And (3) processing the rail corrugation evaluation index, calculating to obtain a rail corrugation characteristic coefficient, presetting a rail corrugation characteristic coefficient threshold value to be 1, and finally screening 5 sections with continuously set length exceeding the preset length as suspected rail corrugation sections, wherein fig. 16 is a schematic diagram of the suspected rail corrugation sections screened in the embodiment of the invention.

And respectively calculating the periodic characteristic coefficients of the 5 suspected rail corrugation zones, wherein the preset threshold value of the periodic characteristic coefficient is 0.5, and judging that the 5 suspected rail corrugation zones have rail corrugation. Taking the section (r) with the most serious rail corrugation as an example, fig. 17 is a schematic diagram of the periodic characteristic coefficient of the section (r) in the embodiment of the present invention, the characteristic wavelength of the rail corrugation is 59mm, the corresponding periodic characteristic coefficient is 0.58, and the rail corrugation exists in the section when the periodic characteristic coefficient exceeds the threshold value of 0.5.

In order to verify the effectiveness and the precision of the rail corrugation detection method provided by the invention, a corrugation trolley is adopted to measure the rail surface irregularity of a K993+ 000-K994 +640 section, the rail surface irregularity with the wavelength range of [30mm,100mm ], and FIG. 18 is a schematic diagram of the rail surface irregularity in the embodiment of the invention. Fig. 19 is a frequency spectrum diagram of the rail surface irregularity in the embodiment of the present invention, and the characteristic wavelength of the rail corrugation in the section is 59mm obtained by performing frequency spectrum analysis on the rail surface irregularity in the section iv, which is also highly consistent with the detection result of the present invention.

An embodiment of the present invention further provides a computer device, and fig. 20 is a schematic diagram of the computer device in the embodiment of the present invention, where the computer device is capable of implementing all steps in the rail corrugation detection method in the foregoing embodiment, and the computer device specifically includes the following contents:

a processor (processor)2001, a memory (memory)2002, a communication Interface (Communications Interface)2003, and a communication bus 2004;

the processor 2001, the memory 2002 and the communication interface 2003 complete communication with each other through the communication bus 2004; the communication interface 2003 is used for realizing information transmission among related devices such as server-side devices, detection devices, user-side devices and the like;

the processor 2001 is configured to call the computer program in the memory 2002, and when the processor executes the computer program, the processor implements all the steps in the rail corrugation detection method in the above embodiments.

An embodiment of the present invention further provides a computer-readable storage medium, which can implement all the steps of the rail corrugation detection method in the above-mentioned embodiment, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements all the steps of the rail corrugation detection method in the above-mentioned embodiment.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.