CN106052606B - A kind of raceway surface recess detection method for the Wavelet Energy Spectrum that is averaged based on scale - Google Patents
A kind of raceway surface recess detection method for the Wavelet Energy Spectrum that is averaged based on scale Download PDFInfo
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- CN106052606B CN106052606B CN201610361595.2A CN201610361595A CN106052606B CN 106052606 B CN106052606 B CN 106052606B CN 201610361595 A CN201610361595 A CN 201610361595A CN 106052606 B CN106052606 B CN 106052606B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B17/00—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations
- G01B17/08—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations for measuring roughness or irregularity of surfaces
Abstract
The invention discloses it is a kind of based on scale be averaged Wavelet Energy Spectrum raceway surface be recessed detection method, first extract vehicle pass through failed track when axle box vibration signal；Secondly simultaneously denoising is filtered to original vibration signal, retains the information related with raceway surface recess；Then by carrying out Wavelet-Energy Spectrum to filtered signal, the frequency range residing for fault-signal is determined；Thirdly be averaged to the signal of corresponding failure frequency range calculating scale Wavelet Energy Spectrum；Raceway surface recess failure is judged whether finally by the maximum value of result of calculation and preset threshold value are carried out comparison.The present invention have it is at low cost, detection result is good, it is real-time the advantages that.
Description
Technical field
It is particularly a kind of to be averaged small wave energy based on scale the invention belongs to track irregularity On-line Fault monitoring technical field
The raceway surface recess detection method of amount spectrum.
Background technology
During train operation, track is to cause the first cause of train abnormal vibrations there are irregularity situation, and track is not
Smooth-going is related with its wavelength to the vibration effect situation of train, and long wave irregularity will cause passenger uncomfortable, reduce operator
Operation level, and Short wave irregularity will cause wheel-rail force to increase sharply so that train vibration aggravates, and reduces the Train Parts longevity
It orders, will make path wear under serious conditions, and jeopardize traffic safety.Raceway surface recess is exactly one kind of Short wave irregularity, so
Real time on-line monitoring is carried out to it, ensures that track quality state meets Operational requirements and is just particularly important.
Molina L F etc. are in document《Condition monitoring of railway turnouts and other
track components using machine vision》Middle proposition installs machine vision instrument additional to rail on vehicle in use
Road situation is detected, this kind of detection method cost is higher, accuracy of detection is low, it is difficult to meet the needs of real time on-line monitoring；Liu
Family's grade is in document《The vehicle-mounted automatic track detection device of city rail vehicle》It is middle to develop a kind of automatic rail detection system, in chassis and
Bogie installation illumination, video camera, laser lamp suspension equipment realize that track detecting, train such as position at the functions, and by testing result
Ground control centre is transferred to, but device installation is complicated, equipment cost is high, and accuracy of detection is low.Liu Feng etc. is in document《Based on fortune
Seek the Rail irregularity detection of vehicle and disease recognition research》In based on bogie acceleration track health status has been carried out just
Step is inquired into, and Fast Fourier Transform (FFT) has been carried out to bogie vibration signal to obtain its amplitude spectrum information, and to the disease of each node
Evil information carries out continuous wavelet transform analysis, determines the spatial position of track disease, but this method can not determine track irregularity
Severity.
Now with greatly developing for urban track traffic, being continuously increased for the operation pressure leads to the maintenance of track maintenance department
Pressure is increasing, this also proposes the safe condition of track higher standard and the request, and traditional track repair method is
Through present maintenance requirement can not be met.
Invention content
The purpose of the present invention is to provide it is a kind of based on scale be averaged Wavelet Energy Spectrum raceway surface be recessed detection method,
Using the on-line checking mode based on vehicle in use, detection efficiency is fast, measurement range is big, real-time is good and does not influence subway fortune
Battalion arranges.
Realize the object of the invention technical solution be：It is a kind of based on scale be averaged Wavelet Energy Spectrum raceway surface it is recessed
Detection method is fallen into, is included the following steps：
Step 1, vibration acceleration sensor is installed on city rail vehicle axle box, acquires axle box vibration signal s (t) in real time；
Step 2：The vibration signal of extraction is filtered, the low-and high-frequency interference of removal noise, retains and track is recessed
Related vibration information s ' (t)；
Step 3：Continuous wavelet transform, the frequency of positioning track surface indentation failure are carried out to the signal s ' (t) that step 2 obtains
Rate range；
Step 4：According to step 3 acquired results, the scale calculated in the frequency range related with raceway surface recess is averaged
Wavelet Energy Spectrum；
Step 5：Scale is averaged Wavelet Energy Spectrum as a period of time sequence, wherein maximum value is found out, if it exceeds setting in advance
Fixed threshold value, then it is believed that there are raceway surface recess at this.
Compared with prior art, the present invention its remarkable advantage：(1) detection mode is the on-line checking based on vehicle in use,
Real-time is good, easy to detect, and engineering construction is good and does not influence operation arrangement in real time online；(2) detection device is simple, installation side
Just it is, at low cost；(3) have the advantages that detection speed is fast, measurement range is big.
The present invention is described in further detail below in conjunction with the accompanying drawings.
Description of the drawings
Fig. 1 be the present invention is based on scale be averaged Wavelet Energy Spectrum raceway surface recess detection method flow chart.
Fig. 2 is train lumped parameter simplified model.
Fig. 3 is one section of actual measurement track irregularity waveform signal.
Fig. 4 (a) is the vertical axle box vibration acceleration figure added in after emulation raceway surface recess.
Fig. 4 (b) is the lateral axle box vibration acceleration figure added in after emulation raceway surface recess.
Fig. 5 (a) is axle box vertical vibration signal Fourier transformation frequency domain figure.
Fig. 5 (b) is axle box vertical vibration signal Fourier transformation frequency domain figure.
Fig. 6 (a) is the Fourier transformation frequency domain figure for filtering rear axle casing vertical vibration signal.
Fig. 6 (b) is the Fourier transformation frequency domain figure for filtering rear axle casing vertical vibration signal.
Fig. 7 (a) is the signal scalogram that axle box vertical vibration signal obtains after wavelet transformation.
Fig. 7 (b) is the signal scalogram that axle box vertical vibration signal obtains after wavelet transformation.
Fig. 8 (a) is averaged wavelet energy spectrogram for the vertical scale of axle box.
Fig. 8 (b) is averaged wavelet energy spectrogram for axle box breadth wise dimension.
Specific embodiment
With reference to Fig. 1, Fig. 2, the present invention is based on the be averaged raceway surfaces of Wavelet Energy Spectrum (SAWP) of scale to be recessed detection method,
Include the following steps：
Step 1, vibration acceleration sensor is installed on city rail vehicle axle box, acquires axle box vibration signal s (t) in real time.
Acquisition axle box vibration signal need to acquire that axle box is vertical and oscillation crosswise signal simultaneously, and acquisition axle box vibration acceleration signal includes axis
Case is vertical and lateral vibration acceleration signal.The foundation for acquiring axle box vibration signal is as follows：
With reference to Fig. 2, train lumped parameter simplified model is established, wherein, K1Represent the Elastic contact stiffness between wheel track, m1
For unsprung mass, m2For the reduced mass of track, K2For track vertical stiffness, c2For the vertical damping of track, z1And z2It represents respectively
Unsprung mass, relative to the displacement of equipoise, is taken in downward direction as just with track.When track is there are after irregularity η, just like
Under vertical kinetics equation：
The solution of wheel rail relation can be converted into the solution to Differential Equation with Constant Coefficients：
In formula, For axle box acceleration.
It is assumed that track recess signal is harmonic signal, is solved equation, can obtained by carrying out Induction Solved by Laplace Transformation to above formula：
It understands that the harmonic frequency of track recess is consistent with the acceleration of axle box vibration signal by formula (3), therefore acquires axle box and hang down
To vibration acceleration signal and its lateral vibration acceleration signal as signal source.
Step 2, the vibration signal of extraction is filtered, the low-and high-frequency interference of removal noise, retains and track is recessed
Related vibration information s ' (t).Vibration signal is filtered and is as follows：
The first step carries out vibration signal s (t) Fourier transformation, the rough frequency domain model for determining to include fault vibration signal
Enclose [H1, H2]:
Second step, in frequency range [H1, H2] interior to vibration signal s (t) progress bandpass filterings, it obtains removal low-and high-frequency and does
It disturbs and the s ' of noise signal (t).
Step 3, continuous wavelet transform is carried out to the signal s ' (t) that step 2 obtains, draws the signal scale after wavelet transformation
Figure, the frequency range [H of positioning track surface indentation failure1', H '2].Continuous wavelet change is carried out to filtered signal s ' (t)
It changes, specific transformation for mula is as follows：：
WhereinIt is the conjugate function of wavelet basis function, WTf(a, b) is wavelet conversion coefficient.
Wavelet basis function selected by wavelet transformation is Morlet small echos：
ω in formula0Represent angular frequency, j represents imaginary number.
Step 4：According to step 3 acquired results, the scale calculated in the frequency range related with raceway surface recess is averaged
Wavelet Energy Spectrum.The scale calculated in the frequency range related with raceway surface recess be averaged Wavelet Energy Spectrum method such as
Under：
In formula, j1Represent smallest dimension, j2Represent out to out, δjRepresent scale step-length, δaRepresent the time of shift factor
Step-length, a represent scale factor, bjRepresent shift factor, CδIt is a constant related with wavelet basis function；
Selection of Wavelet Basis Morlet small echos, for Morlet small echos, Cδ0.776 is taken, scale a and cycle T have following relationship：
Step 5：Scale is averaged Wavelet Energy Spectrum as a period of time sequence, wherein maximum value is found out, if it exceeds setting in advance
Fixed threshold value, then it is believed that there are raceway surface recess at this.
With reference to specific embodiment, the present invention is described in further detail.
Embodiment
With reference to Fig. 3, Fig. 4, in order to study the relationship between axle box vibration acceleration and raceway surface recess, such as Fig. 3 is chosen
One section of shown actual measurement track irregularity data choose 20~100m sections actual measurement track irregularity data in Fig. 3 and carry out track
Surface indentation emulates, the slight raceway surface recess of one section of simulation at 32.5m, track recess length 20mm, depth 0.1mm, emulation
Train running speed is 10m/s in the process, obtains axle box vibration signal response as shown in Figure 4, wherein Fig. 4 (a) hangs down for axle box
To vibration signal, Fig. 4 (b) is axle box laterally to vibration signal.As seen from Figure 4, axle box vibration signal has significantly at 32.5m
Mutation, so axle box vibration signal is recessed to obtain fault message comprising raceway surface.
With reference to Fig. 5, Fig. 6, Fourier transformation is carried out to axle box vibration signal according to step 2, it can be seen that in 200~600HZ
There are peak value in frequency range, as shown in Figure 5.So setting frequency filtering threshold value H1=200HZ, H2=600HZ believes vibration
Number low-pass filtering is carried out, filtered signal is as shown in Figure 6.
With reference to Fig. 7, wavelet transformation is carried out to filtered vibration signal according to step 3, obtains signal ruler as shown in Figure 7
Degree figure, wherein Selection of Wavelet Basis Morlet small echos, the it can be seen from the figure that frequency related with raceway surface recess are concentrated mainly on
Between 300Hz to 600Hz.When train is recessed by slight raceway surface, axle box vertical vibration signal becomes there is no apparent
Change, in Fig. 7 (a), in addition to the signal impact at 32.5m, also there is higher wavelet energy near 30.5m and 34.5m,
So that vertical vibration signal cannot identify that raceway surface is recessed after wavelet transformation.Axle box oscillation crosswise signal is to slight track
Surface indentation is more sensitive, at track surface indentation, has apparent amplitude to change, can clearly be distinguished in Fig. 7 (b).
With reference to Fig. 8, the average dimension Wavelet Energy Spectrum of vibration signal, result of calculation such as Fig. 8 institutes are calculated according to step 4~5
Show, be readily seen in fig. 8 no matter axle box is vertical or oscillation crosswise signal has obvious peak value at 32.5m.Scale is put down
Equal Wavelet Energy Spectrum is a period of time sequence, by calculating the SAWP in the frequency range related with raceway surface recess, is found out
Maximum value therein, if beyond threshold value, it may be considered that there are raceway surface recess at this.Threshold value, which is crossed senior general and caused, fails to judge,
Threshold value is too small, can reduce accuracy of detection, and the present invention selects moderate 0.5m2/s4As automatic detection threshold value, the later stage can be according to big
Amount measured result adjusts the threshold value.
Claims (6)
1. a kind of raceway surface recess detection method for the Wavelet Energy Spectrum that is averaged based on scale, it is characterised in that including following step
Suddenly：
Step 1, vibration acceleration sensor is installed on city rail vehicle axle box, acquires axle box vibration signal s (t) in real time；
Step 2, the vibration signal of extraction is filtered, the low-and high-frequency interference of removal noise retains related with track recess
Vibration information s ' (t)；
Step 3, continuous wavelet transform is carried out to the signal s ' (t) that step 2 obtains, is drawn after carrying out wavelet transformation to vibration signal
Signal scalogram obtains the frequency range [H of positioning track surface indentation failure1', H '2]；
Step 4, it according to step 3 acquired results, calculates the scale in the frequency range related with raceway surface recess and is averaged small echo
Energy spectrum；
Step 5, scale is averaged Wavelet Energy Spectrum as a period of time sequence, wherein maximum value is found out, if it exceeds preset
Threshold value, then it is believed that there are raceway surface recess at this.
2. according to the method described in claim 1, it is characterized in that in step 1, acquisition axle box vibration signal need to acquire axis simultaneously
Case is vertical and oscillation crosswise signal, and acquisition axle box vibration acceleration signal includes that axle box is vertical and lateral vibration acceleration signal.
3. the according to the method described in claim 1, it is characterized in that specific step being filtered in step 2 to vibration signal
It is rapid as follows：
The first step carries out vibration signal s (t) Fourier transformation, the rough frequency domain [H for determining to include fault vibration signal1,
H2]:
Second step, in frequency range [H1, H2] in vibration signal s (t) carry out bandpass filtering, obtain removal low-and high-frequency interference and
The s ' (t) of noise signal.
4. according to the method described in claim 1, it is characterized in that in step 3, filtered signal s ' (t) is carried out continuous small
Wave conversion：
WhereinIt is the conjugate function of wavelet basis function, WTf(a, b) is wavelet conversion coefficient, and s ' (t) refers to vibration signal.
5. the method according to claim 1 or 4, it is characterised in that the wavelet basis function in step 3 selected by wavelet transformation
For Morlet small echos：
ω in formula0Represent angular frequency, j represents imaginary number.
6. according to the method described in claim 1, it is characterized in that the frequency related with raceway surface recess is calculated described in step 4
In the range of scale be averaged Wavelet Energy Spectrum method it is as follows：
In formula, j1Represent smallest dimension, j2Represent out to out, δjRepresent scale step-length, δaRepresent the time step of shift factor,
A represents scale factor, bjRepresent shift factor, CδIt is one related with wavelet basis function to be always on；
For Morlet small echos, Cδ0.776 is taken, scale a and cycle T have following relationship：
ω0Represent angular frequency.
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| CN108920420A (en) * | 2018-03-23 | 2018-11-30 | 同济大学 | A kind of Wavelet noise-eliminating method suitable for driving evaluation test data processing |
| CN112298273B (en) * | 2020-11-03 | 2021-09-14 | 石家庄铁道大学 | Wheel scratch length measuring method and device and terminal equipment |
| CN115112061B (en) * | 2022-06-28 | 2023-07-25 | 苏州大学 | Rail wave grinding detection method and system |
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