CN107403139A - A kind of municipal rail train wheel flat fault detection method - Google Patents
A kind of municipal rail train wheel flat fault detection method Download PDFInfo
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- CN107403139A CN107403139A CN201710528473.2A CN201710528473A CN107403139A CN 107403139 A CN107403139 A CN 107403139A CN 201710528473 A CN201710528473 A CN 201710528473A CN 107403139 A CN107403139 A CN 107403139A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
- G01H1/003—Measuring characteristics of vibrations in solids by using direct conduction to the detector of rotating machines
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
- G01H1/12—Measuring characteristics of vibrations in solids by using direct conduction to the detector of longitudinal or not specified vibrations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
- G06F2218/06—Denoising by applying a scale-space analysis, e.g. using wavelet analysis
Abstract
The invention discloses a kind of municipal rail train wheel flat fault detection method.This method is based on improved Wigner Weir distribution Time-Frequency Analysis Method, comprises the following steps：Collection in worksite signal, adaptive noise reduction is used to vibration signal；Continuous wavelet transform is carried out to vibration signal, wavelet spectrum figure is linearly divided into time frequency unit, to meet WVD dimension；Wavelet coefficient is extracted, signal Analysis feature, estimates threshold value；IWVD processing is carried out to vibration signal, according to the time-frequency figure of signal and time-frequency amplitude figure, carries out wheel condition differentiation；If cross-interference terms exceed the component number N of vibration signal, return re-evaluates threshold value, continues to handle.The present invention is based on improved Wigner Weir time-frequency distributions, and testing result is obvious, strong applicability.
Description
Technical field
The invention belongs to wheel detection technical field, particularly a kind of municipal rail train wheel flat fault detection method.
Background technology
Railway wheelset is one of most important part in vehicular operational part, and it is subjected to vehicle body and passenger or goods
Total weight, and the interaction force being responsible between conducting wheel pair and rail, can especially be produced when wheel is by above rail
Vertical Acceleration on track.Wheel needs to bear more influence on group, including larger dead load and dynamic loading, group
Centrifugal force when filling thermal stress caused by stress and brake(-holder) block, brake lining braking and passing through curve etc..
However as the speed lifting and the sharp increase of passenger number of city rail vehicle, phase interaction of the wheel pair between track
Firmly strengthen, so as to cause wheel pair increasing with the non-rounding phenomenon of rail contact surface, wherein just including the flat scar of wheel tread this
The most common damage type of kind, the damage type can damage to vehicle and each part of track, and cause safety and take and relax
The problems such as appropriateness reduces.Existing wheel detection technology, it is impossible to detect accurately and in real time the abnormal conditions such as wheel flat, nothing
Method is adopted an effective measure eliminate safe hidden trouble in time, is the developing monitoring problem of city rail traffic.
The content of the invention
It is an object of the invention to provide the municipal rail train wheel flat fault detect side that a kind of accuracy is high, real-time is good
Method, eliminated safe hidden trouble so as to adopt an effective measure in time.
Realizing the technical solution of the object of the invention is：A kind of municipal rail train wheel flat fault detection method, is based on
Improved Wigner-Weir distribution Time-Frequency Analysis Method, comprises the following steps：
Step 1, collection in worksite signal, adaptive noise reduction is used to vibration signal；
Step 2, continuous wavelet transform is carried out to vibration signal, wavelet spectrum figure is linearly divided into time frequency unit, with
Meet WVD dimension；
Step 3, wavelet coefficient, signal Analysis feature, estimation threshold value λ are extracted；
Step 4, IWVD processing is carried out to vibration signal, according to the time-frequency figure of signal and time-frequency amplitude figure, carries out wheel shape
State differentiates；
Step 5, if cross-interference terms exceed the component number N of vibration signal, return to step 3 re-evaluates threshold value λ, entered
Row step 4 is handled.
As a kind of specific example, the collection in worksite signal described in step 1, adaptive noise reduction is used to vibration signal, had
Body is as follows：
The input of collection in worksite signal is signal source s and noise source n combination, and auxiliary input is noise source n1；Adaptive filter
Wave system system output e is actual to be estimated for sourceAnd signal s, noise source n and the noise with sef-adapting filter are estimatedGroup
Close, shown in formula specific as follows：
Noise signal n and n1Uncorrelated, sef-adapting filter passes through n based on built-in adaptive algorithm1Made an uproar in real time
Sound is estimatedAvaptive filtering system is usedTo replace n, to realize the function of adaptive interference cancelling.
As a kind of specific example, continuous wavelet transform is carried out to vibration signal described in step 2, by wavelet spectrum figure line
Time frequency unit is divided into property, to meet WVD dimension, is concretely comprised the following steps：
(2.1) function ψ (t) ∈ L are set2(R), Fourier transform corresponding to ψ isWhenMeet enabled condition：When, i.e. CψBounded, then ψ is mother wavelet function, by flexible and translation, is transformed to：
In formula, a, b ∈ R, and a ≠ 0, a are referred to as contraction-expansion factor, b is referred to as shift factor；
If signal f (t) the ∈ L after adaptive-filtering2(R), then f (t) wavelet transformation is defined as：
In formula,ForConjugate operation form；
(2.2) absolute value that W (t, f) is the wavelet coefficient in Wavelet Spectrum obtained by vibration signal continuous wavelet transform is set,
WVDx(t, f) is Wigner-Weir distribution expression formula；Due to WVDxThe dimension of (t, f) is identical with sampling number, therefore using letter
Number sampling number as wavelet scale length, it is ensured that WVDx(t, f) and W (t, f) dimension is identical.
As a kind of specific example, the extraction wavelet coefficient described in step 3, signal Analysis feature, threshold value λ is estimated, specifically
It is as follows：
Selected threshold λ, expression formula are：
WhereinIt is average value of the wavelet coefficient in whole time-frequency domain, σ is the standard deviation of wavelet coefficient.
As a kind of specific example, IWVD processing is carried out to vibration signal described in step 4, according to the time-frequency figure of signal and when
Frequency amplitude figure, wheel condition differentiation is carried out, it is specific as follows：
If certainty time-continuous signal x (t) is sampled signal, then x (t) is defined as follows in the WVD of time-domain：
WVDx(t, f)=∫ x (t+ τ/2) x*(t-τ/2)e-jftdτ (5)
WVDx(t, f) is signal x (t) instantaneous auto-correlation function RxThe Fourier transformation of (t, τ), i.e.,：
Rx(t, τ)=x (t+ τ/2) x*(t-τ/2)(6)
WVDx(t, f)=∫ Rx(t, τ) e-jftdτ (7)
Signal x (t) is defined as in the WVD of frequency domain：
In formula, S (θ) is S (t) Fourier transformation；
Then improved Wigner Weir distribution is as follows：
IWVDs(t, f)=WVDx(t,f)M(t,f) (9)
Wherein M (t, f) is by the Time frequency Filter of the time-frequency spectrum structure of the continuous wavelet transform of vibration signal, is embodied
Formula is as follows：
According to the time-frequency figure of signal and time-frequency amplitude figure, the differentiation of wheel flat failure is carried out.
Compared with prior art, its remarkable advantage is the present invention：(1) improved Wigner-Weir time-frequency distributions are based on, together
When overcome continuous wavelet transform rely on wavelet function selection, Wigner distribution analysis multicomponent data processing cross term interference be present
Essential defect, real-time is good, accuracy is high；(2) testing result is more obvious, and method applicability is strong.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of municipal rail train wheel flat fault detection method.
Fig. 2 is adaptive noise reduction principle schematic.
Fig. 3 is the flat scar fault simulation signal time-domain signal figure of 60 kilometers of speed per hour, wherein (a), which is fault-free wheel, emulates signal
Figure, the flat scar wheel emulation signal graphs of (b) 20mm, the flat scar wheel emulation signal graphs of (c) 40mm, the flat scar wheel emulation letters of (d) 80mm
Number figure.
Fig. 4 is 60 kilometers of emulation signal IWVD time-frequency figures of speed per hour, wherein (a) schemes for fault-free wheel IWVD, (b) is 20mm
Flat scar wheel IWVD figures, (c) are the flat scar wheel IWVD figures of 40mm, and (d) is the flat scar wheel IWVD figures of 80mm.
Fig. 5 is 60 kilometers of emulation signal IWVD time-frequency amplitude figures of speed per hour, wherein (a) is fault-free wheel IWVD time-frequency amplitudes
Figure, (b) is the flat scar wheel IWVD time-frequencies amplitude figures of 20mm, and (c) is the flat scar wheel IWVD time-frequencies amplitude figures of 40mm, and (d) is 80mm
Flat scar wheel IWVD time-frequency amplitude figures.
Embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
With reference to Fig. 1, the present invention is distributed Time-Frequency Analysis Method, first collection in worksite signal based on improved Wigner-Weir,
Processing is filtered to vibration signal, continuous wavelet transform then is carried out to signal, wavelet spectrum figure is linearly divided into small
Time frequency unit to meet WVD dimension；Wavelet coefficient is extracted, threshold value λ is estimated for the feature of institute's signal Analysis；Vibration is believed
Number carry out IWVD processing, the time-frequency figure and time-frequency amplitude figure of observation signal, carry out wheel condition differentiation, if while cross-interference terms
Excessively, more than the component number N of vibration signal, threshold value λ processing is re-evaluated.It is as follows including step：
Step 1, live actual acquisition signal, adaptive noise reduction is used to vibration signal, reduces noise jamming, specifically such as
Under：
As shown in Fig. 2 the input of collection in worksite signal is signal source s and noise source n combination, auxiliary input is noise source
n1；Avaptive filtering system output e is actual to be estimated for sourceAnd signal s, noise source n and the noise with sef-adapting filter
EstimationCombination, shown in formula specific as follows：
Noise signal n and n1Uncorrelated, sef-adapting filter passes through n based on built-in adaptive algorithm1Made an uproar in real time
Sound is estimatedAvaptive filtering system is usedTo replace n, to realize the function of adaptive interference cancelling.
Above formula square and consider noise signal n and n1It is uncorrelated, obtain equation below：
Reach the purpose of filtering optimization signal, the output e of Avaptive filtering system must be in terms of lowest mean square power
Minimize.IfIt is minimized in least square method, that signalLeast-squares estimation can wait until following formula：
In order that mean square error E [s2] gradient could be arranged to 0 relative to weight w, obtain following formula
It produces Wiener equation：
W=R-1P
Step 2, continuous wavelet transform is carried out to vibration signal, wavelet spectrum figure is linearly divided into small time-frequency list
Member, it is specific as follows to meet WVD dimension：
(2.1) function ψ (t) ∈ L are set2(R), Fourier transform corresponding to ψ isWhenMeet enabled condition：When, i.e. CψBounded, then ψ is referred to as mother wavelet function, by flexible and translation transformation, is changed into：
In formula, a, b ∈ R, and a ≠ 0, a are referred to as contraction-expansion factor, b is referred to as shift factor.
If signal f (t) the ∈ L after adaptive-filtering2(R), then f (t) wavelet transformation is defined as：
In formula,ForConjugate operation form.
(2.2) absolute value that W (t, f) is the wavelet coefficient in Wavelet Spectrum obtained by vibration signal continuous wavelet transform is set,
WVDx(t, f) is Wigner-Weir distribution expression formula；Due to WVDxThe dimension of (t, f) is identical with sampling number, therefore using letter
Number sampling number as wavelet scale length, it is ensured that WVDx(t, f) and W (t, f) dimension is identical.
Step 3, wavelet coefficient, signal Analysis feature, estimation threshold value λ are extracted；
Selected threshold λ, its expression formula are shown below.
WhereinIt is average value of the wavelet coefficient in whole time-frequency domain, σ is the standard deviation of wavelet coefficient.
Step 4, IWVD processing is carried out to vibration signal, the time-frequency figure and time-frequency amplitude figure of observation signal, carries out wheel shape
State differentiates, specific as follows：
If certainty time-continuous signal x (t) is sampled signal, then x (t) is defined as follows in the WVD of time-domain：
WVDx(t, f)=∫ x (t+ τ/2) x*(t-τ/2)e-jftdτ
Often by WVDx(t, f) is interpreted as signal x (t) instantaneous auto-correlation function RxThe Fourier transformation of (t, τ), i.e.,：
Rx(t, τ)=x (t+ τ/2) x*(t-τ/2)
WVDx(t, f)=∫ Rx(t, τ) e-jftdτ
Signal x (t) is defined as in the WVD of frequency domain：
In formula, S (θ) is S (t) Fourier transformation.
Improved Wigner Weir distribution can be represented with following formula：
IWVDs(t, f)=WVDx(t,f)M(t,f)
Wherein M (t, f) is by the Time frequency Filter of the time-frequency spectrum structure of the continuous wavelet transform of vibration signal.Embody
Formula is shown below
The time-frequency figure and time-frequency amplitude figure of observation signal, carry out the differentiation of wheel flat failure.
Step 5, by the time-frequency figure and time-frequency amplitude figure of signal, if cross-interference terms are excessive, return to step 3 is estimated again
λ is calculated, step 4 is carried out and handles.
Embodiment 1
Using described IWVD time-frequency distributions analysis methods, and utilize the computer simulation model institute established by Simpack
The emulation data obtained are tested：Believed from the emulation that speed per hour is 60 kilometers of rail cars bogie fault-free wheels hourly
Number and the rail cars No.1 bogie single-wheel hourly of 60 kilometers of speed per hour be respectively the flat scar of 20mm, 40mm, 80mm emulation letter
Number handled.Its time-domain diagram as shown in figure 3, wherein (a) is that fault-free wheel emulates signal graph, imitate by the flat scar wheels of (b) 20mm
True signal figure, the flat scar wheel emulation signal graphs of (c) 40mm, the flat scar wheel emulation signal graphs of (d) 80mm.IWVD time-frequencies figure such as Fig. 4
Shown, wherein (a) schemes for fault-free wheel IWVD, (b) is the flat scar wheel IWVD figures of 20mm, and (c) is the flat scar wheel IWVD of 40mm
Figure, (d) are the flat scar wheel IWVD figures of 80mm.IWVD time-frequency amplitude figures are as shown in figure 5, (a) is fault-free wheel IWVD time-frequency width
Value figure, (b) is the flat scar wheel IWVD time-frequencies amplitude figures of 20mm, and (c) is the flat scar wheel IWVD time-frequencies amplitude figures of 40mm, and (d) is
The flat scar wheel IWVD time-frequencies amplitude figures of 80mm.
As can see from Figure 4 when wheel has different flat scar failures, after IWVD conversion process, in its time-frequency figure
First bogie frequency distribution scope increase in the section compartment is shown, is mainly distributed between 200Hz-500Hz, and fault-free
The frequency of wheel is mainly distributed on 50Hz-200Hz.And the flat scar length wheels of 80mm as can see from Figure 5, its time-frequency amplitude
Time-frequency amplitude extreme point of the extreme point also above normal wheels.Each wheel condition amplitude extreme point concrete numerical value is as shown in table 1.
The wheel flat failure time-frequency amplitude extreme point of table 1
By analysis it is concluded that can not only determine wheel flat failure in the bogie by IWVD, can also pass through
Frequency domain amplitude extreme value determines wheel flat failure rank.
Claims (5)
1. a kind of municipal rail train wheel flat fault detection method, it is characterised in that during based on improved Wigner-Weir distribution
Frequency analysis method, comprises the following steps：
Step 1, collection in worksite signal, adaptive noise reduction is used to vibration signal；
Step 2, continuous wavelet transform is carried out to vibration signal, wavelet spectrum figure is linearly divided into time frequency unit, to meet
WVD dimension；
Step 3, wavelet coefficient, signal Analysis feature, estimation threshold value λ are extracted；
Step 4, IWVD processing is carried out to vibration signal, according to the time-frequency figure of signal and time-frequency amplitude figure, carries out wheel condition and sentence
Not；
Step 5, if cross-interference terms exceed the component number N of vibration signal, return to step 3 re-evaluates threshold value λ, walked
Rapid 4 processing.
2. municipal rail train wheel flat fault detection method according to claim 1, it is characterised in that described in step 1
Collection in worksite signal, adaptive noise reduction is used to vibration signal, it is specific as follows：
The input of collection in worksite signal is signal source s and noise source n combination, and auxiliary input is noise source n1；Avaptive filtering system
It is actual for source estimation to export eAnd signal s, noise source n and the noise with sef-adapting filter are estimatedCombination, specifically
It is shown below：
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MeterAvaptive filtering system is usedTo replace n, to realize the function of adaptive interference cancelling.
3. municipal rail train wheel flat fault detection method according to claim 1, it is characterised in that described in step 2
Continuous wavelet transform is carried out to vibration signal, wavelet spectrum figure is linearly divided into time frequency unit, to meet WVD dimension,
Concretely comprise the following steps：
(2.1) function ψ (t) ∈ L are set2(R), Fourier transform corresponding to ψ isWhenMeet enabled condition：When, i.e. CψBounded, then ψ is mother wavelet function, by flexible and translation, is transformed to：
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(2.2) absolute value that W (t, f) is the wavelet coefficient in Wavelet Spectrum obtained by vibration signal continuous wavelet transform, WVD are setx(t,
F) it is Wigner-Weir distribution expression formula；Due to WVDxThe dimension of (t, f) is identical with sampling number, therefore adopting using signal
Number of samples is as wavelet scale length, it is ensured that WVDx(t, f) and W (t, f) dimension is identical.
4. municipal rail train wheel flat fault detection method according to claim 1, it is characterised in that described in step 3
Extract wavelet coefficient, signal Analysis feature, estimation threshold value λ is specific as follows：
Selected threshold λ, expression formula are：
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WhereinIt is average value of the wavelet coefficient in whole time-frequency domain, σ is the standard deviation of wavelet coefficient.
5. municipal rail train wheel flat fault detection method according to claim 1, it is characterised in that right described in step 4
Vibration signal carries out IWVD processing, according to the time-frequency figure of signal and time-frequency amplitude figure, carries out wheel condition differentiation, specific as follows：
If certainty time-continuous signal x (t) is sampled signal, then x (t) is defined as follows in the WVD of time-domain：
WVDx(t, f)=∫ x (t+ τ/2) x*(t-τ/2)e-jftdτ (5)
WVDx(t, f) is signal x (t) instantaneous auto-correlation function RxThe Fourier transformation of (t, τ), i.e.,：
Rx(t, τ)=x (t+ τ/2) x*(t-τ/2) (6)
WVDx(t, f)=∫ Rx(t,τ)e-jftdτ (7)
Signal x (t) is defined as in the WVD of frequency domain：
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IWVDs(t, f)=WVDx(t,f)M(t,f) (9)
Wherein M (t, f) is that expression is such as by the Time frequency Filter of the time-frequency spectrum structure of the continuous wavelet transform of vibration signal
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According to the time-frequency figure of signal and time-frequency amplitude figure, the differentiation of wheel flat failure is carried out.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710528473.2A CN107403139B (en) | 2017-07-01 | 2017-07-01 | Urban rail train wheel flat scar fault detection method |
Applications Claiming Priority (1)
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