Method for on-line detection of rail corrugation fault based on sliding time sequence window

Abstract

The invention belongs to the technical field of rail traffic rail detection, and discloses a method for online detection of a rail corrugation fault based on a sliding time sequence window. The method is realized by adopting a sliding time sequence window analysis processing method, a wavelength range band-pass filtering method and a weighted calculation and normal value ratio method for vibration acceleration signals acquired by a plurality of measuring point sensors on the same side of the train, so that missed judgment caused by value errors can be avoided; and misjudgment caused by non-rail corrugation frequency signal interference, wheel abnormity and the like can be avoided. Meanwhile, sliding window value judgment is carried out according to the preset track resolution length, and refined judgment and positioning can be carried out on the rail corrugation fault in combination with a kilometer post. According to the rail corrugation fault detection method, the monitoring result is accurate and stable, the requirement for system performance is low, and the requirement for a real-time online monitoring system of low-cost embedded equipment is met.

Description

Technical Field

[0001] The invention belongs to the technical field of rail transit track detection, and relates to a method for online detection of rail corrugation faults based on a sliding timing window. Background Art

[0003] During train operation, there are many technical indicators related to economy, comfort and safety that need to be focused on, and rail corrugation is one of them. Rail corrugation refers to the wear phenomenon of the longitudinal regularity of the top surface of the rail. Rail corrugation will affect the service life of the rail, affect the vertical unevenness of the train, increase the vibration of the contact between the train wheels and the rails, generate noise pollution, affect the riding comfort, and the vibration will also affect the service life of the train and rail components. In severe cases, it will cause safety problems and lead to derailment.

[0004] At present, rail corrugation detection is mainly divided into three categories: manual detection, special detection vehicle detection and real-time online monitoring. Manual detection is suitable for lines with obvious corrugation or low detection accuracy requirements. The advantages are simple operation, low cost and no need for complex equipment; the disadvantages are low detection efficiency, reliance on the experience of inspectors, and it is difficult to detect tiny corrugations or corrugations hidden near the internal structure of the rail. Special detection vehicles detect rail corrugation characteristic parameters based on inertial reference method, axle box acceleration integration method, chord measurement method or machine vision method, etc. The detection efficiency is improved compared with manual detection, but special detection vehicles are required, and detection needs to be carried out online during the "window" period of maintenance, which limits the coverage and efficiency of detection. Real-time online monitoring generally refers to the use of vibration acceleration sensors installed on the axle boxes of operating vehicles for detection, and the rail corrugation evaluation indicators are obtained through the detection algorithm for real-time detection.

[0005] At present, the existing real-time online monitoring method (patent number 2022116867393) can cover the entire operating line of the train and has high detection efficiency. However, when the train speed is lower than 200Km / h, due to the influence of factors such as the time difference in collecting rail corrugation fault signals caused by the distribution spacing of sensors and the average effect of the vibration signal in the non-rail corrugation period on the fault signal within the 1s signal period, the existing rail corrugation evaluation index calculation method has a high probability of missing reports, and the fault location positioning accuracy is low when judging according to the 1s data. Therefore, the detection accuracy and positioning accuracy are worse than those of manual and special inspection vehicles. Summary of the invention

[0006] The present invention provides a specific solution to this difficult problem. The present invention adopts a method of sliding time series window analysis and processing of vibration acceleration signals collected by sensors at multiple measuring points on the same side of the train, a wavelength range bandpass filtering method, a weighted calculation and a method of ratio with normal values, which can not only avoid missed judgments caused by value errors, but also avoid misjudgments caused by interference from non-rail corrugation frequency signals and wheel abnormalities. At the same time, the sliding window value judgment is performed according to the preset track resolution length, and the rail corrugation fault can be finely judged and located in combination with the kilometer mark.

[0007] The technical solution of the present invention:

[0008] A method for online detection of rail corrugation faults based on a sliding timing window, the steps are as follows:

[0009] Get the real-time speed information of the train;

[0010] Arrange measuring points on at least two wheels on the same side of the train to obtain vertical vibration acceleration signals of each measuring point;

[0011] Band-pass filtering is performed on the vibration acceleration signal according to the wavelength range of rail corrugation;

[0012] Calculate the time for the measuring point to pass through a preset length track evaluation unit and the time for passing through a preset track resolution length according to the vehicle speed;

[0013] According to the time when the first measuring point passes through the preset length track evaluation unit, the vibration acceleration signal after bandpass filtering is windowed and intercepted, and the interception window is slid backward according to the time of the preset track resolution length to form a vibration acceleration signal sequence;

[0014] Calculate the delay parameters based on the positional relationship, spacing and vehicle speed of each measuring point and the first measuring point;

[0015] According to the delay parameters, the vibration acceleration signals of each measuring point passing through the same track evaluation unit length interval are extracted, the effective values ​​in the time domain are calculated respectively, and the frequency domain transformation is performed respectively;

[0016] According to the effective time-domain values ​​of each measuring point on the same side and the statistical values ​​of effective time-domain values ​​of historically measured tracks without rail corrugation faults under different vehicle speed conditions, the rail corrugation evaluation index is calculated and output;

[0017] According to the frequency domain data obtained by frequency domain transformation, the frequency corresponding to the maximum amplitude in the frequency domain data is first extracted, the rail corrugation wavelength index is calculated, and the rail corrugation wavelength evaluation index is calculated and output;

[0018] When the train speed is greater than the preset value, the rail corrugation evaluation index is compared with the alarm threshold, and the rail corrugation wavelengths of the measuring points on the same side of the train are compared. When the alarm conditions are met, the alarm status is output.

[0019] A method for online detection of rail corrugation faults based on a sliding timing window, the specific implementation steps are as follows:

[0020] Step 1: Get the real-time speed information TSpd of the train;

[0021] Step 2: Obtain the vertical vibration acceleration signals of N wheels on the same side of the train;

[0022] Step 3: According to the wavelength range L low and L High , calculate the cutoff frequency f of the bandpass filter x =TSpd*1000 / 3.6 / L High , filter upper cutoff frequency f s =TSpd*1000 / 3.6 / L low ;

[0023] Step 4: Perform band-pass filtering on the vibration signal according to the band-pass filtering parameters obtained in step 3;

[0024] Step 5: Calculate the measurement point according to the vehicle speed through the preset length track evaluation unit L u Time T u =Lu*3.6 / TSpd and by presetting the track resolution length L f Time T f =L f *3.6 / TSpd;

[0025] Step 6: Based on the T obtained in step 5 u The vibration signal of the first measuring point obtained in step 4 is windowed and intercepted, and the signal is calculated according to T f Slide the window backward to form a vibration acceleration signal sequence;

[0026] Step 7: According to the position relationship between each measuring point and the first measuring point, the spacing L j and vehicle speed TSpd, calculate the delay parameter T d =L j *3.6 / TSpd;

[0027] Step 8: According to T d and T u , extract the vibration acceleration signals of each measuring point passing through the same track evaluation unit length interval, calculate the effective value RMSi in the time domain, and perform frequency domain transformation respectively;

[0028] Step 9: Sort the time domain effective values ​​RMSi of the N measuring points on the same side of the train from large to small, in the order of RMS1, RMS2, RMS3, RMS4, ..., RMS NWeighted calculation, read the time domain effective value statistics RMSv of the historical measured track without rail corrugation fault corresponding to the vehicle speed, calculate and output the rail corrugation evaluation index CI = (a1*RMS1+a2*RMS2+a3*RMS3+a4*RMS4+……+a N *RMS N ) / RMSv.

[0029] Step 10: According to the frequency domain data obtained by the FFTW frequency domain transformation in step 8, first extract the frequency f corresponding to the maximum amplitude in the frequency domain data, and calculate the rail corrugation wavelength index Ln=TSpd*1000 / (3.6*f);

[0030] Step 11: Average the rail corrugation wavelength index Ln of N measuring points on the same side of the train, calculate and output the rail corrugation wavelength evaluation index

[0031] Step 12: When the train speed is greater than the preset value, the rail corrugation evaluation index CI is compared with the alarm threshold, and the rail corrugation wavelength Ln of N measuring points on the same side of the train is compared to determine whether the alarm condition is met and whether to alarm;

[0032] Step 13: Output the alarm status ALTtr and set the alarm status to 0.

[0033] As a preferred method, in step 3, in order to avoid interference from non-rail corrugation frequency signals, the wavelength range L low =30mm, L High =300mm.

[0034] As a preferred method, in step 5, the length track evaluation unit L is preset u It should be shorter than the normal rail corrugation length, ranging from 10-50m, preferably 20m.

[0035] As a preferred method, in step 5, the track resolution length L is preset f , with a value range of 0.1-5m, preferably 1m. In order to make a refined judgment on rail corrugation faults, the present invention introduces a sliding window value selection method, which can improve the positioning accuracy of rail corrugation faults in combination with kilometer marks.

[0036] As a preferred method, in step 9, due to the difference in wheel states, the wheel-rail excitation force is also different. In order to avoid misjudgment caused by abnormal state of individual wheels, the present invention introduces a method of weighted calculation of the effective values ​​of the time domain of N measuring points on the same side of the train to improve the reliability of the rail corrugation evaluation index; the number of measuring points N ranges from 2 to 16, preferably N = 4; the weighting coefficients a1, a2, a3, a4..., a NThe value range is 0.01-0.99, and a1+a2+a3+a4+……+a N =1.

[0037] As a preferred method, in step 12, the method for determining whether to alarm is: when the vehicle speed is greater than the preset value, the rail corrugation evaluation index CI is compared with the alarm threshold. When CI < 1st level alarm threshold, the operation is normal, and the state value ALTst = 0; when the 1st level alarm threshold ≤ CI < 2nd level alarm threshold, the state value ALTst = 1; when the M-1 level alarm threshold ≤ CI < M-level alarm threshold, the state value ALTst = M-1; when CI ≥ M-level alarm threshold, the state value ALTst = M; when the alarm is continuously for n seconds and the maximum deviation of the Ln value of N measuring points ≤ the deviation coefficient, the alarm state ALTtr = ALTst, and an alarm prompt is issued; otherwise, the alarm state ALTtr = 0, and no alarm prompt is issued; when the vehicle speed is less than the preset value, the judgment is terminated. If only the rail corrugation evaluation index is used for judgment, when multiple wheels are abnormal, a large number of false alarms will be caused. However, when multiple wheels are abnormal, the possibility of presenting a rail corrugation wavelength at the same time is extremely small. Therefore, the present invention introduces whether the rail corrugation wavelength indexes of N measuring points on the same side of the train are consistent as an alarm condition, that is, when the rail corrugation evaluation index exceeds the alarm threshold for n seconds continuously, and the maximum deviation of the rail corrugation wavelength indexes of the N measuring points on the same side of the train is ≤ the deviation coefficient, an alarm prompt is issued; further, the alarm level M has a value range of 1-10; preferably, the alarm level M=2, ALTst=1 is early warning, and ALTst=2 is alarm; further, the value range of n is 1-5 seconds; further, the value range of the deviation coefficient is 0-10 mm.

[0038] Beneficial effects of the present invention: The rail corrugation fault detection method of the present invention introduces a method of sliding time series window analysis and processing of the vibration acceleration signals collected by sensors at N measuring points on the same side of the train, which reduces missed judgments caused by value errors, and can make refined judgments and locate rail corrugation faults in combination with kilometer marks; introduces a wavelength range bandpass filtering method to reduce misjudgments caused by interference from non-rail corrugation frequency signals; introduces a method of weighted calculation of rail corrugation indicators at N measuring points on the same side of the train, improves the reliability of rail corrugation evaluation indicators, and effectively avoids misjudgments caused by abnormal states of individual wheels; introduces whether the maximum deviation of the rail corrugation wavelength indicators at N measuring points on the same side of the train is less than the deviation coefficient as an alarm condition, effectively avoiding misjudgments caused by abnormalities in multiple wheels; introduces a method of ratio with normal values, which can intuitively reflect the difference between faulty tracks and normal tracks under different vehicle speed conditions. The monitoring results of the rail corrugation fault detection method of the present invention are accurate and stable, with low requirements on system performance, meeting the requirements of low-cost embedded device real-time online monitoring systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the hardware structure of a system for implementing the method of the present invention.

[0040] Figure 2 It is a flow chart of the monitoring method of the present invention.

[0041] Figure 3 These are the original vibration time-domain waveforms of the 1000m track section at measuring points 1#-4#, where (a) is the original vibration time-domain waveform of the 1000m track section at measuring point 1#, (b) is the original vibration time-domain waveform of the 1000m track section at measuring point 1#, (c) is the original vibration time-domain waveform of the 1000m track section at measuring point 1#, and (d) is the original vibration time-domain waveform of the 1000m track section at measuring point 2#, both of which are cars.

[0042] Figure 4 These are the original time domain waveforms of the local time periods of the 1#-4# measuring points, where (a) is the original time domain waveform of the local time period of the 1#-1# measuring point, (b) is the original time domain waveform of the local time period of the 1#-2# measuring points, (c) is the original time domain waveform of the local time period of the 2#-1# measuring point, and (d) is the original time domain waveform of the local time period of the 2#-2# measuring points.

[0043] Figure 5 These are the time domain waveforms after filtering at the local time periods of the 1#-4# measuring points, where (a) is the time domain waveform after filtering at the 1# vehicle 1 position measuring point, (b) is the time domain waveform after filtering at the 1# vehicle 2 positions measuring point, (c) is the time domain waveform after filtering at the local time periods of the 2# vehicles 1 position measuring point, and (d) is the time domain waveform after filtering at the local time periods of the 2# vehicles 2 positions measuring points.

[0044] Figure 6 It is the vibration acceleration signal sequence diagram of the local period of the 1# measuring point.

[0045] Figure 7 It is the time window diagram of the vibration signal of the 1#-4# measuring points at a certain moment in this method, among which (a) is the time domain waveform diagram of the 1# vehicle 1 measuring point after filtering, (b) is the time domain waveform diagram of the 1# vehicle 2 measuring points after filtering, (c) is the time domain waveform diagram of the 2# vehicle 1 measuring point after filtering, and (d) is the time domain waveform diagram of the 2# vehicle 2 measuring points after filtering.

[0046] Figure 8 It is the time domain waveform diagram of the vibration signal time window of the 1#-4# measuring point at a certain moment, among which, (a) is the RMS of the 1st position of car 1: 11.4573, (b) is the RMS of the 2nd position of car 1: 12.6317, (c) is the RMS of the 1st position of car 2: 10.6037, and (d) is the RMS of the 2nd position of car 2: 13.2469.

[0047] Fig. 9It is the frequency domain waveform of the vibration signal time window of the 1#-4# measuring point at a certain moment, among which (a) is 1 car 1 position, (b) is 1 car 2 positions, (c) is 2 cars 1 position, and (d) is 2 cars 2 positions.

[0048] Fig.10 This is the window diagram of the corrugation signal value of the 1#-4# measuring points of the original method, among which (a) is the time domain waveform diagram of the 1#-1# measuring point after filtering, (b) is the time domain waveform diagram of the 1#-2# measuring points after filtering, (c) is the time domain waveform diagram of the 2#-1# measuring point after filtering, and (d) is the time domain waveform diagram of the 2#-2# measuring points after filtering.

[0049] Fig.11 This is the trend chart of rail corrugation evaluation indicators of this method. DETAILED DESCRIPTION

[0050] The specific implementation of the present invention is described in detail below in combination with the technical scheme and the accompanying drawings.

[0051] Example 1

[0052] like Figure 1 As shown in the schematic diagram of the system hardware structure, the vibration sensor is arranged above the train axle box bearing, and each of the eight wheels of each carriage is arranged with a vibration acceleration sensor. The data acquisition module collects the wheel vertical vibration acceleration signal obtained by the sensor and sends it to the diagnosis and analysis algorithm processing module; the communication module obtains the real-time speed of the train sent by the on-board central control unit through Ethernet and sends it to the diagnosis and analysis algorithm processing module for calculating the rail corrugation wavelength, filtering parameters, etc.; the rail corrugation evaluation index, rail corrugation wavelength evaluation index and alarm information calculated by the diagnosis and analysis algorithm processing module are sent from the communication module to the on-board central control unit in real time through Ethernet. This method is implemented in the diagnosis and analysis algorithm processing module.

[0053] like Figure 2 The monitoring method flow chart shows that the specific steps are as follows:

[0054] Step 1: Get the real-time speed information TSpd of the train;

[0055] Step 2: Obtain the collected vertical vibration acceleration signal of the train wheel;

[0056] Step 3: According to the wavelength range L low and L High , calculate the cutoff frequency f of the bandpass filter x =TSpd*1000 / 3.6 / L High , filter upper cutoff frequency f s =TSpd*1000 / 3.6 / L low ;

[0057] Step 4: Bandpass filter the vibration acceleration signal. The filter selects the 6th-order Butterworth filter. The filter transfer function is as follows:

[0058]

[0059] The wavelength range bandpass filtering method is introduced to effectively reduce the misjudgment caused by the interference of non-rail corrugation frequency signals.

[0060] Step 5: Calculate the measurement point according to the vehicle speed through the preset length track evaluation unit L u Time T u =Lu*3.6 / TSpd and by presetting the track resolution length L f Time T f =L f *3.6 / TSpd;

[0061] Step 6: Based on the T obtained in step 5 u The vibration signal of the first measuring point obtained in step 4 is windowed and intercepted, and the signal is calculated according to T f Slide the window backward to form a vibration acceleration signal sequence;

[0062] Step 7: According to the distance L between each measuring point and the first measuring point j and vehicle speed TSpd, calculate the delay parameter T d =L j *3.6 / TSpd;

[0063] Step 8: According to T d and T u , extract the vibration acceleration signal of each measuring point passing through the same track evaluation unit length interval, calculate the time domain effective value RMSi respectively, and perform FFTW frequency domain transformation respectively. The calculation formula is as follows:

[0064] in,

[0065] According to the properties of the correlation function and the use of recursive divide and conquer, all coefficients, that is, the frequency domain amplitudes, can be quickly calculated.

[0066] A method of performing time series window analysis and processing on the vibration acceleration signals collected by sensors at multiple measuring points on the same side of the train is introduced, which can effectively reduce missed judgments caused by value errors and misjudgments caused by abnormal signals at a single measuring point.

[0067] Step 9: Sort the time domain effective values ​​RMSi of the N measuring points on the same side of the train from large to small, in the order of RMS1, RMS2, RMS3, RMS4, ..., RMS NWeighted calculation, divided by the time domain effective value statistic RMSv of the historical measured track without rail corrugation fault corresponding to the vehicle speed, calculate and output the rail corrugation evaluation index CI = (a1*RMS1+a2*RMS2+a3*RMS3+a4*RMS4+……+a N *RMS N ) / RMSv; the weighting coefficients a1, a2, a3, and a4 range from 0.01 to 0.99, and a1+a2+a3+a4=1, preferably a1=0.15, a2=0.35, a3=0.35, and a4=0.15. The method of weighted calculation of the rail corrugation index at the four measuring points on the same side of the train is introduced, which can effectively reduce the influence of wheel state differences on the calculation of rail corrugation index and improve the reliability of rail corrugation evaluation index.

[0068] Step 10: According to the frequency domain data obtained by the FFTW frequency domain transformation in step 8, first extract the frequency f corresponding to the maximum amplitude in the frequency domain data, and calculate the rail corrugation wavelength index Ln=TSpd*1000 / (3.6*f);

[0069] Step 11: Average the rail corrugation wavelength index Ln of the four measuring points on the same side of the train, calculate and output the rail corrugation wavelength evaluation index

[0070] Step 12: When the vehicle speed TSpd≥100km / h, compare the rail corrugation evaluation index CI with the alarm threshold. When CI<6, the operation is normal, and the state value ALTst=0; when 6≤CI<9 for n seconds continuously, and the maximum deviation of the Ln value of the four measuring points is ≤deviation coefficient, the state value ALTst=1, and the alarm state ALTtr=1; when CI≥9 for n seconds continuously, and the maximum deviation of the Ln value of the four measuring points is ≤deviation coefficient, the state value ALTst=2, the alarm state ALTtr=2, and an alarm prompt is given; otherwise, the alarm state ALTtr=0, and no alarm prompt is given; when the vehicle speed is <100km / h, the judgment is terminated. Whether the rail corrugation wavelength index of the four measuring points on the same side of the train is consistent is introduced as an alarm condition, that is, when the rail corrugation evaluation index exceeds the alarm threshold for n seconds continuously, and the maximum deviation of the rail corrugation wavelength index of the four measuring points on the same side of the train is ≤deviation coefficient, an alarm prompt is given, avoiding false alarms caused when 3 to 4 wheels are abnormal. Preferably, when the rail corrugation evaluation index exceeds the alarm threshold for n=1 seconds continuously, and the maximum deviation of the Ln values ​​of the four measuring points is ≤ the deviation coefficient of 5 mm, an alarm prompt is issued;

[0071] Step 13: Output the alarm status ALTtr and set the alarm status to 0.

[0072] Example 2

[0073] Using the method of the present invention Figure 3 The 1000m measured vibration detection data is analyzed, and the data processing at a certain moment is as follows:

[0074] Step 1: Get the train speed information TSpd=292.09Km / h at a certain time;

[0075] Step 2: Obtain the collected vertical vibration acceleration signal of the train wheel;

[0076] Step 3: According to the wavelength range L low and L High , calculate the cutoff frequency f of the bandpass filter x =292.09*1000 / 3.6 / 300=270.454Hz, filter upper cutoff frequency

[0077] f s =292.09*1000 / 3.6 / 30=2704.54Hz

[0078] Step 4: Figures 4 to 5 , perform band-pass filtering on the vibration acceleration signal;

[0079] Step 5: Calculate the measurement point according to the vehicle speed through the preset length track evaluation unit L u Time T u =Lu*3.6 / TSpd=20*3.6 / 292.09=0.2465s and by presetting the track resolution length L f Time T f =L f *3.6 / TSpd=1*3.6 / 292.09=0.0123s;

[0080] Step 6: Figure 6 , according to T obtained in step 5 u The vibration signal of the first measuring point obtained in step 4 is windowed and intercepted, and the signal is calculated according to T f Slide the window backward to form a vibration acceleration signal sequence;

[0081] Step 7: According to the distance L between each measuring point and the first measuring point j and vehicle speed TSpd, calculate the delay parameter T d1 =L j / TSpd=25*3.6 / 292.09=0.308s、T d2 =L j / TSpd=50*3.6 / 292.09=0.616s、T d3 =L j / TSpd = 75*3.6 / 292.09 = 0.924s;

[0082] Step 8: Figures 7 to 9 , according to T d and T u , extract the vibration acceleration signals of each measuring point passing through the same track evaluation unit length interval, calculate the time domain effective value RMSi respectively, and perform FFTW frequency domain transformation respectively; Fig.10 In the original monitoring method, due to the time difference between different measuring points entering the faulty track and the influence of the normal track vibration signal, the obtained vibration signal cannot correctly reflect the actual condition of the track, that is, due to the value error, it is easy to cause the underreporting of rail corrugation faults.

[0083] Step 9: Fig.11 , sort the time domain effective values ​​RMSi of the four measuring points from large to small, perform weighted calculation, and divide them by the historical measured time domain effective value statistic RMSv of the track without rail corrugation fault at the corresponding speed, calculate and output the rail corrugation evaluation index CI, Figure 8 period CI = (a1*RMS1+a2*RMS2+a3*RMS3+

[0084] a4*RMS4) / RMSv=(0.15*13.2469+0.35*12.6317+0.35*11.4573+0.15*10.6037) / 1.52=7.90.

[0085] The historical measured time domain effective value statistics RMSv of the track without rail corrugation fault corresponding to the vehicle speed, the preferred values ​​are shown in Table 1. The present invention introduces a method of ratio with normal value, which can intuitively reflect the difference between the fault track and the normal track under different vehicle speed conditions.

[0086] Table 1 The statistical value table of the time domain effective value of the historical measured track without rail corrugation fault under different speed conditions

[0087]

[0088]

[0089] Step 10: extract the frequency f corresponding to the maximum amplitude in the frequency domain data, and then calculate the rail corrugation wavelength index Ln=TSpd*1000 / (3.6*f);

[0090] 1# measuring point Ln=TSpd*1000 / (3.6*f)=292.09*1000 / (3.6*575.919)=140.9mm 2# measuring point Ln=TSpd*1000 / (3.6*f)=292.09*1000 / (3.6*575.919)=140.9mm 3# measuring point Ln=TSpd*1000 / (3.6*f)=292.09*1000 / (3.6*575.919)=140.9mm

[0091] 4# measuring point Ln = TSpd*1000 / (3.6*f) = 292.09*1000 / (3.6*575.919) = 140.9 mm

[0092] Step 11: Average the rail corrugation wavelength index Ln of the four measuring points on the same side of the train, calculate and output the rail corrugation wavelength evaluation index

[0093] Step 12: When the vehicle speed TSpd292.09km / h≥100km / h, compare the rail corrugation evaluation index CI with the alarm threshold. Since 6≤CI=7.90<9 for 1 second continuously, the state value ALTst=1, and the maximum deviation of the Ln values ​​of the four measuring points (140.9mm-140.9mm=0mm)≤5mm, the alarm state ALTtr=ALTst=1;

[0094] Step 13: Output alarm status ALTtr=1, that is, rail corrugation prediction alarm.

[0095] Through manual calculation of the vibration data of the measured rail corrugation defect position, this method can accurately determine the track fault.

Claims

1. A method for online detection of rail corrugation faults based on a sliding timing window, characterized in that: The steps are as follows: obtain the real-time speed information of the train; Arrange measuring points on at least two wheels on the same side of the train to obtain vertical vibration acceleration signals of each measuring point; Band-pass filtering is performed on the vibration acceleration signal according to the wavelength range of rail corrugation; Calculate the time for the measuring point to pass through a preset length track evaluation unit and the time for passing through a preset track resolution length according to the vehicle speed; According to the time when the first measuring point passes through the preset length track evaluation unit, the vibration acceleration signal after bandpass filtering is windowed and intercepted, and the interception window is slid backward according to the time of the preset track resolution length to form a vibration acceleration signal sequence; Calculate the delay parameters based on the positional relationship, spacing and vehicle speed of each measuring point and the first measuring point; According to the delay parameters, the vibration acceleration signals of each measuring point passing through the same track evaluation unit length interval are extracted, the effective values ​​in the time domain are calculated respectively, and the frequency domain transformation is performed respectively; According to the effective time-domain values ​​of each measuring point on the same side and the statistical values ​​of effective time-domain values ​​of historically measured tracks without rail corrugation faults under different vehicle speed conditions, the rail corrugation evaluation index is calculated and output; According to the frequency domain data obtained by frequency domain transformation, the frequency corresponding to the maximum amplitude in the frequency domain data is first extracted, the rail corrugation wavelength index is calculated, and the rail corrugation wavelength evaluation index is calculated and output; When the train speed is greater than the preset value, the rail corrugation evaluation index is compared with the alarm threshold, and the rail corrugation wavelengths of the measuring points on the same side of the train are compared. When the alarm conditions are met, the alarm status is output.

2. A method for online detection of rail corrugation faults based on a sliding timing window, characterized in that: Here are the steps: Step 1: Get the real-time speed information TSpd of the train; Step 2: Obtain the vertical vibration acceleration signals of N wheels on the same side of the train; Step 3: According to the wavelength range L low and L High , calculate the cutoff frequency f of the bandpass filter x =TSpd*1000 / 3.6 / L High , filter upper cutoff frequency f s =TSpd*1000 / 3.6 / L low ; Step 4: Perform band-pass filtering on the vibration acceleration signal according to the band-pass filtering obtained in step 3; Step 5: Calculate the measurement point according to the vehicle speed through the preset length track evaluation unit L u Time T u =Lu*3.6 / TSpd and by presetting the track resolution length L f Time T f =L f *3.6 / TSpd; Step 6: Based on the T obtained in step 5 u The vibration acceleration signal of the first measuring point obtained in step 4 is windowed and intercepted, and the signal is calculated according to T f Slide the window backward to form a vibration acceleration signal sequence; Step 7: According to the position relationship between each measuring point and the first measuring point, the spacing L j and vehicle speed TSpd, calculate the delay parameter T d =L j *3.6 / TSpd; Step 8: According to T d and T u , extract the vibration acceleration signals of each measuring point passing through the same track evaluation unit length interval, calculate the effective value RMSi in the time domain, and perform FFTW frequency domain transformation respectively; Step 9: Sort the time domain effective values ​​RMSi of the N measuring points on the same side of the train from large to small, in the order of RMS1, RMS2, RMS3, RMS4, ..., RMS N Weighted calculation, read the time domain effective value statistics RMSv of the historical measured track without rail corrugation fault corresponding to the vehicle speed, calculate and output the rail corrugation evaluation index CI = (a1*RMS1+a2*RMS2+a3*RMS3+a4*RMS4+……+a N *RMS N ) / RMSv; Step 10: According to the frequency domain data obtained by the FFTW frequency domain transformation in step 8, first extract the frequency f corresponding to the maximum amplitude in the frequency domain data, and calculate the rail corrugation wavelength index Ln=TSpd*1000 / (3.6*f); Step 11: Average the rail corrugation wavelength index Ln of N measuring points on the same side of the train, calculate and output the rail corrugation wavelength evaluation index Step 12: When the train speed is greater than the preset value, the rail corrugation evaluation index CI is compared with the alarm threshold, and the rail corrugation wavelength Ln of N measuring points on the same side of the train is compared to determine whether the alarm condition is met and whether to alarm; Step 13: Output the alarm status ALTtr and set the alarm status to 0.

3. The method for online detection of rail corrugation faults based on a sliding timing window according to claim 2 is characterized in that: In step 3, the wavelength range L low =30mm, L High =300mm.

4. The method for online detection of rail corrugation faults based on a sliding timing window according to claim 2 is characterized in that: In step 5, the length of the track evaluation unit L is preset u Smaller than the track corrugation length, the value range is 10-50m.

5. The method for online detection of rail corrugation faults based on a sliding timing window according to claim 2 is characterized in that: In step 5, the track resolution length L is preset f , the value range is 0.1-5m.

6. The method for online detection of rail corrugation faults based on a sliding timing window according to claim 2 is characterized in that: In step 9, the number of measuring points N ranges from 2 to 16; the weighting coefficients a1, a2, a3, a4, ..., a N The value range is 0.01-0.99, and a1+a2+a3+a4+……+a N =1.

7. The method for online detection of rail corrugation faults based on a sliding timing window according to claim 2 is characterized in that: In step 12, the method for judging whether to alarm is as follows: when the vehicle speed is greater than a preset value, the rail corrugation evaluation index CI is compared with the alarm threshold; when CI is less than the level 1 alarm threshold, the operation is normal, and the state value ALTst=0; when the level 1 alarm threshold ≤CI<level 2 alarm threshold, the state value ALTst=1; when the M-1 alarm threshold ≤CI<level M alarm threshold, the state value ALTst=M-1; when CI≥level M alarm threshold, the state value ALTst=M; when the alarm is continuously for n seconds and the maximum deviation of the Ln values ​​of N measuring points is less than the deviation coefficient, the alarm state ALTtr=ALTst, and an alarm prompt is given; otherwise, the alarm state ALTtr=0, and no alarm prompt is given; when the vehicle speed is less than the preset value, the judgment is ended.

8. The method for online detection of rail corrugation faults based on a sliding timing window according to claim 7 is characterized in that: In step 12, whether the rail corrugation wavelength indexes of N measuring points on the same side of the train are consistent is introduced as an alarm condition, that is, when the rail corrugation evaluation index exceeds the alarm threshold for n seconds continuously and the maximum deviation of the rail corrugation wavelength indexes of N measuring points on the same side of the train is ≤ the deviation coefficient, an alarm prompt is issued.

9. The method for online detection of rail corrugation faults based on a sliding timing window according to claim 8 of the present invention is characterized in that: The alarm level M has a value range of 1-10; the value range of n is 1-5 seconds; the value range of the deviation coefficient is 0-10 mm.

10. The method for online detection of rail corrugation faults based on a sliding timing window according to claim 9 is characterized in that: The alarm level M=2, ALTst=1 is a warning, and ALTst=2 is an alarm.

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