A method for online detection of rail corrugation faults

Abstract

The present invention belongs to the technical field of rail transit track detection and discloses a method for online detection of rail corrugation faults. The rail corrugation fault detection method of the present invention introduces a method of weighted calculation of rail corrugation indicators at four measuring points on one side of the train, thereby improving the reliability of rail corrugation evaluation indicators and effectively avoiding misjudgments caused by abnormal conditions of individual wheels; and introduces whether the maximum deviation of the rail corrugation wavelength indicators at four measuring points on one side of the train is less than the deviation coefficient as an alarm condition, thereby effectively avoiding misjudgments caused by abnormalities in three to four wheels. The rail corrugation fault detection method of the present invention has accurate and stable monitoring results, has low requirements on system performance, and meets the requirements of a low-cost embedded device real-time online monitoring system.

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. Background Art

[0002] As of the end of 2021, the national railway operating mileage was 150,000 kilometers, and the high-speed railway operating mileage reached 40,000 kilometers, ranking first in the world.

[0003] During train operation, many technical indicators related to economy, comfort, and safety require special attention, and rail corrugation is one of them. Rail corrugation refers to the wear and tear of the rail's top surface, which is characterized by regular, uneven longitudinal fluctuations. Rail corrugation can affect the rail's service life, affect the vertical irregularity of the train, increase vibration between the train wheels and rails, generate noise pollution, and affect ride comfort. Furthermore, vibration can affect the service life of train and rail components, and in severe cases, can even cause safety issues and lead to derailment.

[0004] Currently, rail corrugation detection is mainly divided into three categories: manual inspection, inspection by dedicated inspection vehicles, and real-time online monitoring. Manual inspection involves personnel using tools to directly measure rail surface irregularities online. The advantages are intuitiveness and high accuracy, but it requires manual operation during the "window" period of line maintenance, resulting in high labor costs and low detection coverage and efficiency. Dedicated inspection vehicles use inertial reference methods, axle box acceleration integration methods, chord measurement methods, or machine vision methods to detect rail corrugation characteristic parameters. Compared with manual inspection, detection efficiency is improved, but dedicated inspection vehicles are required, and inspections need to be carried out online during the "window" period of maintenance, which limits detection coverage and efficiency. Real-time online monitoring generally refers to the use of vibration acceleration sensors installed on the axle boxes of operating vehicles for detection. Rail corrugation evaluation indicators are obtained through detection algorithms and real-time detection is performed.

[0005] The existing real-time online monitoring methods can cover the entire operating line of the train and have high detection efficiency. However, because a single signal source is used to calculate rail corrugation evaluation indicators and diagnose faults, the status of the corresponding wheel has a great influence on the detection results, resulting in detection accuracy lower than manual and dedicated inspection vehicles. Summary of the Invention

[0006] This invention addresses this difficult issue by providing a specific solution. By analyzing and processing vibration acceleration signals collected by sensors at four measuring points on the same side of the train, the invention not only accurately reflects rail corrugation defects but also indicates their severity, while also requiring minimal installation cost.

[0007] To achieve the above purpose, the technical solution adopted by the present invention to solve the problem is:

[0008] Get real-time train speed information;

[0009] Measuring points are arranged on at least two wheels on the same side of the train to obtain vertical acceleration signals at each measuring point; the vibration acceleration signal is low-pass filtered, the middle section is intercepted, and frequency domain transformation is performed simultaneously;

[0010] According to the vibration acceleration signal obtained after intercepting the middle section, first calculate the time domain effective value, and then calculate the vibration effective value index;

[0011] According to the frequency domain data obtained by frequency domain transformation, the maximum amplitude and corresponding frequency in the frequency domain data are first extracted, and the rail corrugation amplitude index and rail corrugation wavelength index are calculated;

[0012] Calculate the rail corrugation index based on the effective value of vibration index and rail corrugation amplitude index;

[0013] Calculate and output the rail corrugation evaluation index and rail corrugation wavelength evaluation index based on the rail corrugation index of the measuring point on the same side of the train;

[0014] 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.

[0015] A method for online detection of rail corrugation faults, the specific implementation steps are as follows:

[0016] Step 1: Get the real-time speed information TSpd of the train;

[0017] Step 2: Obtain the vertical vibration acceleration signals of N wheels on the same side of the train;

[0018] Step 3: After low-pass filtering the vibration acceleration signal obtained in step 2, the middle segment is intercepted and FFTW frequency domain transformation is performed at the same time;

[0019] Step 4: Based on the vibration acceleration signal obtained after intercepting the middle section in step 3, first calculate the time domain RMS value, and then calculate the vibration RMS index RmsdB=20log(RMS);

[0020] Step 5: Based on the frequency domain data obtained by FFTW frequency domain transformation in step 3, first extract the maximum amplitude A and the corresponding frequency f in the frequency domain data, and calculate the rail corrugation amplitude index AdB = 20log(A) and the rail corrugation wavelength index Ln = TSpd*1000 / (3.6*f);

[0021] Step 6: Based on the vibration effective value index RmsdB obtained in step 4, the rail corrugation amplitude index AdB obtained in step 5 and the weighting coefficient a, calculate the rail corrugation index GndB=a*AdB+(1-a)*RmsdB;

[0022] Step 7: Sort the rail corrugation index GndB of the N measuring points on the same side of the train from large to small, in the order of GndB1, GndB2, GndB3, GndB4, ..., GndB N Weighted calculation, calculate and output rail corrugation evaluation index GdB=a1*GndB1+a2*GndB2+a3*GndB3+a4*GndB4+……+a N *GndB N ;

[0023] Step 8: Average the rail corrugation wavelength index Ln of the N measuring points on the same side of the train, and calculate and output the rail corrugation wavelength evaluation index L=Average( , N);

[0024] Step 9: When the train speed is greater than the preset value, the rail corrugation evaluation index GdB is compared with the alarm threshold, and the rail corrugation wavelength Ln of N measuring points on the same side of the train are compared to determine whether the alarm condition is met and whether to alarm;

[0025] Step 10: Output the alarm status ALTtr.

[0026] As a preferred embodiment, in step 3, intercepting the middle section is intercepting the vibration acceleration signal between 10% and 90% of the vibration acceleration signal.

[0027] As a preferred approach, in step 5, the method for extracting the maximum amplitude A and the corresponding frequency f in the frequency domain data is: using a least squares fitting algorithm to extract the maximum amplitude A and the corresponding frequency f in the frequency domain data.

[0028] As a better approach, in step 6, the weighting coefficient a ranges from 0.2 to 0.8, and is generally set to a=0.5.

[0029] As a preferred method, in step 7, due to the differences in wheel states, the wheel-rail excitation force also varies. In order to avoid the problem of poor accuracy of the evaluation index calculation results using a single data source and to avoid misjudgment caused by abnormal individual wheel states, the present invention introduces a method of weighted calculation of the rail corrugation index 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 N The value range is 0.05-0.5, and a1+a2+a3+a4+……+a N=1.

[0030] As a preferred method, in step 9, the method for determining whether to alarm is as follows: when the vehicle speed is greater than a preset value, the rail corrugation evaluation index GdB is compared with the alarm threshold. When GdB is less than the lowest alarm threshold, the operation is normal and the status value ALTst = 0; when the lowest threshold value ≤ GdB < the middle alarm threshold value, the status value ALTst = 1; when the middle alarm threshold value ≤ GdB < the highest alarm threshold value, the status value ALTst = 2; when GdB ≥ the highest alarm threshold value, the status value ALTst = 3; if the alarm continues for n seconds and the maximum deviation of the Ln values ​​of N measuring points is less than the deviation coefficient, the alarm status ALTtr = ALTst and an alarm prompt is issued; otherwise, the alarm status ALTtr = 0 and no alarm prompt is issued; when the vehicle speed is less than the preset value, the judgment ends. If only the rail corrugation evaluation index is used for judgment, when multiple wheels have abnormalities, a large number of false alarms will be caused. However, when multiple wheels have abnormalities, the possibility of showing the same rail corrugation wavelength at the same time is extremely small. Therefore, the present invention introduces the consistency of the rail corrugation wavelength indicators at N measuring points on the same side of the train as an alarm condition. That is, when the rail corrugation evaluation index exceeds the alarm threshold for n consecutive seconds and the maximum deviation of the rail corrugation wavelength indicators at the N measuring points on the same side of the train is ≤ the deviation coefficient, an alarm prompt is issued; further, ALTst=1 is a pre-judgment, ALTst=2 is a warning, and ALTst=3 is an alarm; further, the value range of n is 1-10 seconds; further, the value range of the deviation coefficient is 1-20 mm;

[0031] The beneficial effects of the present invention are as follows: The rail corrugation fault detection method of the present invention introduces a method for weighted calculation of rail corrugation indicators at N measuring points on the same side of the train, improving the reliability of rail corrugation evaluation indicators and effectively avoiding misjudgments caused by abnormal conditions of individual wheels. The method also uses the fact that 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 abnormal conditions at multiple wheels. The rail corrugation fault detection method of the present invention produces accurate and stable monitoring results, has low system performance requirements, and meets the requirements of a low-cost embedded device real-time online monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the hardware structure of the system of the embodiment of the method of the present invention.

[0033] Figure 2 It is a flow chart of the monitoring method of the present invention.

[0034] Figure 3 This is the time domain waveform of measuring point 1#.

[0035] Figure 4 This is the frequency domain waveform of measurement point 1#.

[0036] Figure 5 This is the time domain waveform of measuring point 2#.

[0037] Figure 6 This is the frequency domain waveform of measurement point 2#.

[0038] Figure 7 This is the time domain waveform of measuring point 3#.

[0039] Figure 8 This is the frequency domain waveform of measurement point 3#.

[0040] Figure 9 This is the time domain waveform of measuring point 4#.

[0041] Figure 10 This is the frequency domain waveform of measuring point 4#.

[0042] Figure 11 This is a trend chart of rail corrugation evaluation indicators using this method. DETAILED DESCRIPTION

[0043] The specific implementation of the present invention is described in detail below in conjunction with the technical solutions and drawings.

[0044] Example 1

[0045] like Figure 1 As shown in the schematic diagram of the system hardware structure, vibration sensors are placed above the train's axlebox bearings, and each of the eight wheels in each carriage is equipped with a vibration acceleration sensor. The data acquisition module collects the vertical vibration acceleration signals of the wheels from the sensors and sends them to the diagnostic analysis algorithm processing module. The communication module obtains the real-time train speed from the onboard central control unit via Ethernet and sends it to the diagnostic analysis algorithm processing module for calculating the rail corrugation wavelength. The rail corrugation evaluation index, rail corrugation wavelength evaluation index, and alarm information calculated by the diagnostic analysis algorithm processing module are transmitted in real time to the onboard central control unit via Ethernet. This method is implemented in the diagnostic analysis algorithm processing module.

[0046] like Figure 2 The monitoring method flow chart shows the specific steps as follows:

[0047] Step 1: Get the real-time speed information TSpd of the train;

[0048] Step 2: Obtain the collected vertical vibration acceleration signal of the train wheel;

[0049] Step 3: Perform a 2500Hz low-pass filter on the vibration acceleration signal. Select a 6th-order Butterworth filter as the wheel polygon evaluation data. The filter transfer function is as follows:

[0050]

[0051] Perform Fourier transform on the filtered signal to obtain frequency domain data. The Fourier transform formula is calculated using the FFTW formula, which is as follows:

[0052] in,

[0053] 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.

[0054] Step 4: Calculate the RMS value of the vibration in the time domain of the filtered signal. Since edge jitter in the filtered signal will cause calculation errors, a portion of the signal is intercepted to calculate the time domain RMS value. The intercepted signal is generally between 10% and 90% of the data segment. Then calculate the vibration RMS index RmsdB = 20log(RMS);

[0055] Step 5: Extract the maximum amplitude A and the corresponding frequency f from the frequency domain data using the least squares fitting algorithm. Then calculate the rail corrugation amplitude index AdB = 20log(A) and the rail corrugation wavelength index Ln = TSpd*1000 / (3.6*f).

[0056] Step 6: Calculate the polygon index GndB = a*AdB+(1-a)*RmsdB+Kfac. The weighting coefficient a ranges from 0.2 to 0.8, with a preferred value of a=0.5. Kfac is a correction coefficient with a range of 0-50 to adapt to the alarm value standardization of various types of vehicles. Introducing a weighting algorithm into the rail corrugation index and taking the effective vibration value as a component of the index parameter can ensure more accurate monitoring results.

[0057] Step 7: Sort the rail corrugation index GndB of the four measuring points on the same side of the train from large to small, perform weighted calculation, and calculate and output the rail corrugation evaluation index GdB=a1*GndB1+a2*GndB2+a3*GndB3+a4

[0058] *GndB4 introduces a weighted calculation method for rail corrugation indicators at four measuring points on the same side of the train. This effectively avoids the inaccuracy of evaluation indicators calculated using a single data source, reduces the impact of wheel condition differences on rail corrugation index calculation, and improves the reliability of rail corrugation evaluation indicators. The weighting coefficients a1, a2, a3, and a4 range from 0.05 to 0.5, with a1 + a2 + a3 + a4 = 1. Optimally, a1 = 0.15, a2 = 0.35, a3 = 0.35, and a4 = 0.15 are used.

[0059] Step 8: Average the rail corrugation wavelength index Ln of the four measuring points on the same side of the train, and calculate and output the rail corrugation wavelength evaluation index L=Average( , 4);

[0060] Step 9: When the vehicle speed TSpd ≥ 10 km / h, compare the rail corrugation evaluation index GdB with the alarm threshold. When GdB < 10 (dB), normal operation is achieved, and the status value ALTst = 0. When 10 (dB) ≤ GdB < 18 (dB) for n consecutive seconds, and the maximum deviation of the Ln values ​​of the four measuring points ≤ the deviation coefficient, the status value ALTst = 1, and the alarm status ALTtr = 1. When 18 (dB) ≤ GdB < 23 (dB) for n consecutive seconds, and the maximum deviation of the Ln values ​​of the four measuring points ≤ the deviation coefficient, the status value ALTst = 2, and the alarm status ALTtr = 2. When GdB ≥ 23 (dB) for n consecutive seconds, and the maximum deviation of the Ln values ​​of the four measuring points ≤ the deviation coefficient, the status value ALTst = 3, the alarm status ALTtr = 3, and an alarm prompt is issued. Otherwise, the alarm status ALTtr = 0, and no alarm prompt is issued. When the vehicle speed is < 10 km / h, the judgment ends. The consistency of the rail corrugation wavelength indicators at the four measuring points on the same side of the train is introduced as an alarm condition. That is, when the rail corrugation evaluation indicator exceeds the alarm threshold for n consecutive seconds, and the maximum deviation of the rail corrugation wavelength indicators at the four measuring points on the same side of the train is ≤ the deviation coefficient, an alarm prompt is issued, avoiding false alarms caused by abnormalities in 3 to 4 wheels. Preferably, an alarm prompt is issued when the rail corrugation evaluation indicator exceeds the alarm threshold for n = 1 second continuously, and the maximum deviation of the Ln value of the four measuring points is ≤ the deviation coefficient of 5 mm;

[0061] Step 10: Output the alarm status ALTtr.

[0062] Example 2

[0063] The method of the present invention was applied to a domestic intelligent high-speed railway dynamic safety monitoring system to perform rail corrugation fault diagnosis, as follows:

[0064] Step 1: Get the real-time train speed information TSpd = 153.94 km / h;

[0065] Step 2: Obtain the collected vertical vibration acceleration signal of the train wheel;

[0066] Step 3: If Figures 3 to 10 , perform 2500Hz low-pass filtering on the vibration acceleration signal, and select the 6th-order Butterworth filter;

[0067] Perform Fourier transform on the filtered signal to obtain frequency domain data, and use FFTW formula to calculate the Fourier transform formula;

[0068] Step 4: Calculate the time domain RMS value of the filtered signal. Since edge jitter in the filtered signal will cause calculation errors, a portion of the signal is intercepted to calculate the vibration time domain RMS value. The intercepted signal is generally taken between 10% and 90% of the data segment. Then calculate the vibration RMS index RmsdB = 20log(Rms);

[0069] 1# measurement point RmsdB=20log(6.7835g)=16.629dB

[0070] 2# measurement point RmsdB=20log(6.0282g)=15.603dB

[0071] 3# measurement point RmsdB=20log(7.3901g)=17.373dB

[0072] 4# measurement point RmsdB=20log(5.440g)=14.712dB

[0073] Step 5: Extract the maximum amplitude A and the corresponding frequency f from the frequency domain data, and then calculate the rail corrugation amplitude index AdB = 20log(A) and the rail corrugation wavelength index Ln = TSpd*1000 / (3.6*f);

[0074] 1# measurement point AdB=20log(5.7676g)=15.220dB

[0075] 1# measuring point Ln=TSpd*1000 / (3.6*f)=153.94*1000 / (3.6*569)=111.9mm

[0076] 2# measurement point AdB=20log(5.9233g)=15.451dB

[0077] 2# measuring point Ln=TSpd*1000 / (3.6*f)=153.94*1000 / (3.6*569)=111.9mm

[0078] 3# measurement point AdB=20log(6.9136g)=16.794dB

[0079] 3# measuring point Ln=TSpd*1000 / (3.6*f)=153.94*1000 / (3.6*568)=111.6mm

[0080] 4# measurement point AdB=20log(5.8560g)=15.352dB

[0081] 4# measuring point Ln=TSpd*1000 / (3.6*f)=153.94*1000 / (3.6*568)=111.6mm

[0082] Step 6: Calculate the polygon index GndB = a*AdB+(1-a)*RmsdB;

[0083] 1# measuring point GndB=0.5*15.220+0.5*16.629=15.925dB

[0084] 2# measurement point GndB=0.5*15.451+0.5*15.603=15.527dB

[0085] 3# measurement point GndB=0.5*16.794+0.5*17.373=17.084dB

[0086] 4# measurement point GndB=0.5*15.352+0.5*14.712=15.032dB

[0087] High-speed rail dynamic safety monitoring systems typically set a unified alarm threshold for standardized management. The evaluation values ​​obtained based on different algorithms are different, but the preset alarm thresholds are not adjusted accordingly. Therefore, the algorithm provider can set a corresponding correction factor based on the system's preset alarm thresholds to match the system's usage requirements. In this embodiment, the correction factor is -4dB, and the corrected polygon index is:

[0088] 1# measurement point GndB=15.925dB-4dB=11.925dB

[0089] 2# measurement point GndB=15.527dB-4dB=11.527dB

[0090] 3# measurement point GndB=17.084dB-4dB=13.084dB

[0091] 4# measurement point GndB=15.032dB-4dB=11.032dB

[0092] Step 7: Sort the rail corrugation index GndB of the four measuring points on the same side of the train from large to small, perform weighted calculation, and calculate and output the rail corrugation evaluation index GdB=a1*GndB1+a2*GndB2+a3*GndB3+a4

[0093] *GndB4=0.15*13.084+0.35*11.925+0.35*11.527+0.15*11.032=11.8256dB;

[0094] Step 8: Average the rail corrugation wavelength index Ln of the four measuring points on the same side of the train, and calculate and output the rail corrugation wavelength evaluation index L=Average( , 4) = 111.75 mm;

[0095] Step 9: If the train speed TSpd153.94 km / h ≥ 10 km / h, compare the rail corrugation evaluation index GdB with the alarm threshold. Since 10 (dB) ≤ GdB = 11.8256 dB < 18 (dB) for 1 second, the state value ALTst = 1, and the maximum deviation of the Ln values ​​at the four measuring points (111.9 mm - 111.6 mm = 0.3 mm) ≤ 5 mm, the alarm state ALTtr = ALTst = 1.

[0096] Step 10: Output the alarm status ALTtr=1, which is the rail corrugation prediction alarm.

[0097] The intelligent high-speed rail dynamic safety monitoring system has been operating continuously for five months using this algorithm, generating 261 alarms at six locations. Manual rail measurements at the alarm locations confirmed the presence of rail corrugation defects consistent with the monitoring results, with 100% accuracy. The method of this invention provides accurate monitoring results and high stability.

Claims

1. A method for online detection of rail corrugation faults, characterized in that: Get real-time train speed information; Arrange measurement points on at least two wheels on the same side of the train to obtain vibration acceleration signals from each measurement point; perform low-pass filtering on the vibration acceleration signals, intercept the middle section, and perform frequency domain transformation simultaneously; According to the vibration acceleration signal obtained after intercepting the middle section, first calculate the time domain effective value, and then calculate the vibration effective value index; According to the frequency domain data obtained by frequency domain transformation, the maximum amplitude and corresponding frequency in the frequency domain data are first extracted, and the rail corrugation amplitude index and rail corrugation wavelength index are calculated; Calculate the rail corrugation index based on the effective value of vibration index and rail corrugation amplitude index; Calculate and output the rail corrugation evaluation index and rail corrugation wavelength evaluation index based on the rail corrugation index of the measuring point on the same side of the train; 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. The method for online detection of rail corrugation faults according to claim 1, characterized in that: The steps include: Step 1: Real-time train speed information TSpd; Step 2: Obtain the vertical vibration acceleration signals of N wheels on the same side of the train; Step 3: After low-pass filtering the vertical vibration acceleration signal of the train wheel obtained in step 2, the middle section is intercepted and FFTW frequency domain transformation is performed at the same time; Step 4: Based on the vertical vibration acceleration signal of the train wheel obtained after intercepting the middle section in step 3, first calculate the time domain RMS value, and then calculate the vibration RMS index RmsdB = 20log(RMS); Step 5: Based on the frequency domain data obtained by FFTW frequency domain transformation in step 3, first extract the maximum amplitude A and the corresponding frequency f in the frequency domain data, and calculate the rail corrugation amplitude index AdB = 20log(A) and the rail corrugation wavelength index Ln = TSpd*1000 / (3.6*f); Step 6: Calculate the rail corrugation index GndB = a*AdB+(1-a)*RmsdB based on the vibration effective value index RmsdB obtained in step 4, the rail corrugation amplitude index AdB obtained in step 5, and the weighting coefficient a. Step 7: Sort the rail corrugation index GndB of the N measuring points on the same side of the train from large to small, in the order of GndB1, GndB2, GndB3, GndB4, ..., GndB N Weighted calculation, calculate and output rail corrugation evaluation index GdB=a1*GndB1+a2*GndB2+a3*GndB3+a4*GndB4+……+a N *GndB N ; Step 8: 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 9: When the train speed is greater than the preset value, the rail corrugation evaluation index GdB is compared with the alarm threshold, and the rail corrugation wavelength Ln of N measuring points on the same side of the train are compared to determine whether the alarm condition is met and whether to alarm; Step 10: Output the alarm status ALTtr.

3. The method for online detection of rail corrugation faults according to claim 2, further characterized in that: The intercepted middle section intercepts the vibration acceleration signal between 10% and 90% of the vibration acceleration signal.

4. The method for online detection of rail corrugation faults according to claim 2, further characterized in that: The method for extracting the maximum amplitude A and the corresponding frequency f in the frequency domain data includes: extracting the maximum amplitude A and the corresponding frequency f in the frequency domain data using a least squares fitting algorithm.

5. The method for online detection of rail corrugation faults according to claim 2, further characterized in that: The weighting coefficient a ranges from 0.2 to 0.

8.

6. The method for online detection of rail corrugation faults according to claim 2, further characterized in that: In step 7, the number of measurement points N ranges from 2 to 16; the weighting coefficients a1, a2, a3, a4, ..., a N The value range is 0.05-0.5, and a1+a2+a3+a4+……+a N =1.

7. The method for online detection of rail corrugation faults according to claim 2, further characterized in that: The method for determining whether to alarm is as follows: when the vehicle speed is greater than a preset value, the rail corrugation evaluation index GdB is compared with the alarm threshold; when GdB is less than the lowest value of the alarm threshold, the operation is normal and the status value ALTst=0; when the lowest threshold value ≤ GdB < the middle value of the alarm threshold, the status value ALTst=1; when the middle value of the alarm threshold ≤ GdB < the highest value of the alarm threshold, the status value ALTst=2; when GdB ≥ the highest alarm threshold, the status value ALTst=3; when the alarm occurs for n seconds continuously and the maximum deviation between the Ln values ​​of all measuring points on the same side of the train is less than the deviation coefficient, the alarm status ALTtr=ALTst, and an alarm prompt is given; otherwise, the alarm status ALTtr=0, no alarm prompt is given; further, ALTst=1 is a prejudgment, ALTst=2 is a warning, and ALTst=3 is an alarm.

8. The method for online detection of rail corrugation faults according to claim 7, characterized in that: In the continuous n seconds, the value range of n is 1-10 seconds.

9. The method for online detection of rail corrugation faults according to claim 7, characterized in that: The deviation coefficient has a value range of 1-20 mm.

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