CN107650945A - A kind of recognition methods of wheel polygon and its device based on vertical wheel rail force - Google Patents
A kind of recognition methods of wheel polygon and its device based on vertical wheel rail force Download PDFInfo
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- CN107650945A CN107650945A CN201710845323.4A CN201710845323A CN107650945A CN 107650945 A CN107650945 A CN 107650945A CN 201710845323 A CN201710845323 A CN 201710845323A CN 107650945 A CN107650945 A CN 107650945A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61K—AUXILIARY EQUIPMENT SPECIALLY ADAPTED FOR RAILWAYS, NOT OTHERWISE PROVIDED FOR
- B61K9/00—Railway vehicle profile gauges; Detecting or indicating overheating of components; Apparatus on locomotives or cars to indicate bad track sections; General design of track recording vehicles
- B61K9/12—Measuring or surveying wheel-rims
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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
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/20—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring contours or curvatures, e.g. determining profile
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Abstract
The invention discloses a kind of wheel polygon recognition methods based on vertical wheel rail force and its device, the recognition methods to comprise the following steps：Vertical wheel rail force signal is gathered with shear stress method；Enter row set empirical modal EEMD decomposition to the vertical wheel rail force signal gathered, so as to obtain the intrinsic mode functions IMF components of lump；Calculate the marginal spectrum of the intrinsic mode functions IMF comprising wheel polygon Disease Characters information；According to the intrinsic mode functions IMF of acquisition marginal spectrum, hurt characteristic frequency f is obtained, and according to default decision criteria, to identify whether wheel polygon and polygon-type occurs.Its advantage is:Monitoring in real time can be carried out to the vehicle by the monitoring section and hurt is judged, there is fast and accurate feature；EEMD methods can be good at handling the vertical wheel rail force signal of time-varying, and by the marginal spectrum of signal after calculating decomposition, so as to obtain the vertical wheel rail force hurt characteristic frequency after polygon disease occurs for wheel.
Description
Technical field
The invention belongs to railway security monitoring technical field, and in particular to a kind of wheel polygon based on vertical wheel rail force
Recognition methods and its device.
Background technology
High-speed railway and urban track traffic have been obtained soon due to advantages such as its large conveying quantity, energy-conservation and environmental protection in recent ten years
The development of speed, according to railway " 13 development plan ", the peak period of railway construction development is still during 13, will be built
Into by trunk of " Eight Verticals and Eight Horizontals " passage, inter-city passenger rail be supplement High-speed Railway Network.Urban track traffic simultaneously is being built and transported
It is double to seek city number, mileage increase half or so.But with the increase of China railways service time, occur various
Influence passenger comfort and jeopardize the disease of traffic safety, this seriously governs the further development of China railways cause and walked out
Remove implementation.
The content of the invention
According to the deficiencies of the prior art described above, It is an object of the present invention to provide a kind of wheel based on vertical wheel rail force
Polygon recognition methods and its device, the recognition methods and its device on rail by setting resistance strain gage to be obtained
Rail strain signal is changed, decomposition computation so as to obtain hurt characteristic frequency f, so as to according to default decision criteria,
Whether the identification wheel for carrying out fast accurate there is the type of polygon and polygon.
The object of the invention is realized and completed by following technical scheme：
A kind of wheel polygon recognition methods based on vertical wheel rail force, it is characterised in that the recognition methods includes following step
Suddenly：
（1）Vertical wheel rail force signal is gathered with shear stress method；
（2）Enter row set empirical modal EEMD decomposition to the vertical wheel rail force signal gathered, so as to obtain the sheet of lump
Levy modular function IMF components；
（3）Calculate the step（2）In comprising wheel polygon Disease Characters information intrinsic mode functions IMF marginal spectrum；
（4）According to the step（3）The intrinsic mode functions IMF of middle acquisition marginal spectrum, acquisition hurt characteristic frequency f, and according to
Default decision criteria, to identify whether wheel polygon and polygon-type occurs.
The step（1）In, the span centre opening position between the adjacent sleeper no less than 5 groups, glued in steel rail web position
Resistance strain gage is pasted, each resistance strain gage gathers the strain signal of the rail and is converted to the vertical wheel rail force letter
Number.
The computational methods that the strain signal of the rail is converted to the vertical wheel rail force signal are：
P=|Qr|+Ql
Qr= Ql=(Jb/S)τ
τ=Gε
In formula：
P is the vertical wheel rail force；
QrAnd QlFor shearing；
J is the moment of inertia of the rail profile to natural axis；
B is the rail profile thickness at the natural axis；
Static moments of the S for section beyond Calculation Shear point to natural axis；
τ is the shear stress suffered by the rail；
G is the modulus of shearing of the rail；
The rail shearing strain that ε is collected by the resistance strain gage.
The step（2）In, the set empirical modal EEMD, which is decomposed, to be referred to believe the vertical wheel rail force gathered
The white Gaussian noise of number addition normal distribution, then is decomposed the amended vertical wheel rail force signal using EMD methods.
The step（3）In, the marginal spectrum computational methods of the intrinsic mode functions IMF are：To the intrinsic of the lump of acquisition
Modular function IMF components carry out Hilbert conversion, so as to obtain the Hilbert spectrograms H of Energy distribution on time-frequency plane（ω, t）,
Calculation formula is as follows：
To the Hilbert spectrograms H obtained（ω, t）The integration in time domain is carried out, so as to obtain marginal spectrum H（ω）, calculation formula
It is as follows：
In formula：
Re represents to take the real part of imaginary number；
a（T) it is the intrinsic mode functions IMF components of lump；
J is unit imaginary number,
ω is the instantaneous frequency in Hilbert conversion；
T is instantaneous moment.
The step（4）In, the default decision criteria is specially：
If periodicity wheel rail force wavelength X is 2 π R, hurt characteristic frequency f is v/2 π R, it is inclined that single order polygon, wheel occurs in wheel
The situation of the heart；
If periodicity wheel rail force wavelength X is π R, hurt characteristic frequency f is v/ π R, it is oval that second order polygon, wheel occurs in wheel
The situation of change；
If periodicity wheel rail force wavelength X is 2 π R/3, hurt characteristic frequency f is 3v/2 π R, there is three rank polygons, car in wheel
Take turns the situation of triangularization；
If periodicity wheel rail force wavelength X is π R/2, hurt characteristic frequency f is 2v/ π R, there is quadravalence polygon, wheel in wheel
The situation of quadrangle；
If periodicity wheel rail force wavelength X is 2 π R/N, hurt characteristic frequency f is vN/2 π R, there is N ranks polygon, wheel in wheel
The situation of N sides shape；
Wherein, R is the vehicle wheel roll radius；V is the speed of service of train.
Described device includes strain acquirement module, train speed acquisition module, data long-distance transmission module and long-range
Monitoring module, the strain acquirement module and the train speed acquisition module are through the data long-distance transmission module and institute
State remote monitoring module and form communication connection.
Also it is connected with vertical wheel rail force computing module on the remote monitoring module in turn, set empirical modal EEMD is decomposed
Module, signal margin spectrum computing module and wheel polygon judge module.
It is an advantage of the invention that：
（1）The not high situation of the longer, degree of accuracy is taken for existing wheel polygon detecting mode, is surveyed based on shear stress method
On the basis of vertical wheel rail force, the vertical wheel rail force signal obtained in real time and signal processing technology are taken full advantage of, it is proposed that one
Wheel polygon recognition methods and device of the kind based on vertical wheel rail force, the vehicle by the monitoring section can be carried out real-time
Monitoring and hurt are judged, and so as to repair processing to there is the timely Xuan of the wheel of wheel polygon progress, avoid the generation of security incident；
（2）Because EEMD methods have reasonability directly perceived, high efficiency, adaptivity and processing nonlinear and nonstationary local signal
Superiority, therefore can be good at handling the vertical wheel rail force signal of time-varying；The marginal spectrum of signal decomposed by calculating simultaneously after, from
And obtain wheel and the vertical wheel rail force hurt characteristic frequency after polygon disease occurs.
Brief description of the drawings
Fig. 1 is the wheel polygon recognition methods schematic flow sheet based on vertical wheel rail force in the present invention；
Fig. 2 is the structural representation of wheel polygon identification device in the present invention；
Fig. 3 is that stickup of the resistance strain gage at steel rail web position sets schematic diagram in the present invention；
Fig. 4 is set empirical modal EEMD decomposing schematic representations in the present invention；
Fig. 5 is the vertical wheel rail force time-histories figure in the case of Wheel ovalization in the present invention；
Fig. 6 is the intrinsic mode functions figure of vertical wheel rail force signal in the present invention；
Fig. 7 is the marginal spectrogram of vertical wheel rail force signal in the present invention.
Embodiment
The feature of the present invention and other correlated characteristics are described in further detail by embodiment below in conjunction with accompanying drawing, with
It is easy to the understanding of technical staff of the same trade：
Such as Fig. 1-7, mark 1-3 is respectively in figure：Rail 1, sleeper 2, resistance strain gage 3.
Embodiment 1：As shown in figs. 1-7, the present embodiment is known more particularly to a kind of wheel polygon based on vertical wheel rail force
Other method and its device, specifically include following steps：
（Step 1）
1.1）As shown in Figure 1, 2, 3, the installation for carrying out wheel polygon identification device is laid, wheel polygon identification device tool
Body includes strain acquirement module, train speed acquisition module, data long-distance transmission module and remote monitoring module；Wherein,
Strain acquirement module includes several resistance strain gages 3 for being arranged at the web of the rail position of rail 1, sets 5 groups of electricity in the present embodiment altogether
Hinder foil gauge 3, the span centre opening position of the set location of each resistance strain gage 3 between adjacent sleeper 2；Train speed gathers mould
Block is specifically the speed measuring instrument on train, and the real-time speed of train is obtained for monitoring；Data long-distance transmission module is used
Connected in forming remote monitoring module with the communication between foregoing strain acquirement module, train speed acquisition module, so that
Remote monitoring module can receive the strain signal come from transmitted by strain acquirement module and come from train speed collection
Train running speed data transmitted by module are, it is necessary to which explanation, is also connected with turn multiple soft on remote monitoring module
Part computing module, including vertical wheel rail force computing module, set empirical modal EEMD decomposing modules, signal margin spectrum computing module
And wheel polygon judges module；
1.2）After completion is laid in foregoing wheel polygon identification device installation, adopted by the strain being arranged on rail 1
Collection module measures the strain signal of the rail 1 when train passes through, and sample frequency highest can reach 20kHz/ passages, fully meet
Requirement to sample frequency；The speed of service of train is measured by train speed acquisition module simultaneously；And will be foregoing gathered
Rail strain signal and train running speed are uploaded in remote monitoring module in real time by data long-distance transmission module, remotely
Monitoring module is stored the rail strain signal and train running speed of acquisition；
1.3）Afterwards, remote monitoring module sends rail strain signal to vertical wheel rail force computing module, should by rail
Varying signal is converted to vertical wheel rail force signal, as shown in figure 5, calculation formula is：
P=|Qr|+Ql
Qr= Ql=(Jb/S)τ
τ=Gε
In formula：
P is vertical wheel rail force；
QrAnd QlFor shearing；
J is the moment of inertia of the section of rail 1 to natural axis；
B is the section thickness of rail 1 at natural axis；
Static moments of the S for section beyond Calculation Shear point to natural axis；
τ is the shear stress suffered by rail 1；
G is the modulus of shearing of rail 1；
The shearing strain of rail 1 that ε is collected by resistance strain gage 3.
（Step 2）
As shown in figure 4, vertical wheel rail force signal is sent to set empirical modal EEMD decomposing modules, to vertical wheel rail force signal
Enter row set empirical modal EEMD decomposition, i.e. vertical wheel rail force signal is added to the white Gaussian noise of normal distribution, then used
EMD methods are decomposed amended vertical wheel rail force signal, and the IMF obtained every time is integrated into average as final signal point
Result is solved, obtains the intrinsic mode functions IMF components and survival function of lump（r5）, as shown in Figure 6.
（Step 3）
The lump intrinsic mode functions IMF components of acquisition are sent to signal margin spectrum computing module, is composed and calculated by signal margin
Module carries out Hilbert conversion to the lump intrinsic mode functions IMF components obtained, so as to obtain Energy distribution on time-frequency plane
Hilbert spectrograms H（ω, t）, as shown in fig. 7, calculation formula is as follows：
The Hilbert spectrums H obtained（ω, t）The integration in time domain is carried out, obtains marginal spectrum H（ω）, calculation formula is as follows：
In formula：
Re represents to take the real part of imaginary number；
a（T) it is the IMF components of lump；
J is unit imaginary number,
ω is the instantaneous frequency in Hilbert conversion；
T is instantaneous moment.
（Step 4）
Wheel polygon judges module and enters work, according to step（3）Obtained in marginal spectrum H（ω）, it is polygon to obtain wheel
Shape hurt characteristic frequency f, and according to default decision criteria, to identify whether wheel polygon and polygon-type occurs,
Foregoing default decision criteria referring specifically to table 1 below, wherein, R is train wheel rolling radius；V is the speed of service of train.
The wheel polygon hurt characteristic frequency f of table 1
The beneficial effect of the present embodiment is：The wheel polygon recognition methods based on vertical wheel rail force that the present embodiment is provided
And its device, the train wheel polygon by detecting section can be detected in real time, while can very easily enter
Row remote monitoring, it largely compensate for the weak point of existing artificial detection method, it is ensured that the safe operation of train.
Embodiment 2：The present embodiment is specifically related to a kind of wheel polygon recognition methods based on vertical wheel rail force, is implementing
Illustrate, comprise the following steps with reference to concrete case on the basis of example 1：
（Step 1）As shown in figure 3, ovalization has occurred for certain wheel according to a preliminary estimate, pass through the strain acquirement mould being arranged on rail 1
Block measures vertical wheel rail force signal, as shown in Figure 5；The vertical wheel rail force signal is subjected to EEMD decomposition again, obtains the sheet of lump
Modular function IMF components are levied, as shown in Figure 6；Similarly, its excess-three kind wheel polygon carries out signal decomposition using same method；
（Step 2）Time domain upper integral is carried out to the intrinsic mode functions IMF components of lump, obtains signal margin spectrum, as shown in fig. 7,
It can be seen that the hurt characteristic frequency f after wheel generation ovalization is 24.1Hz；
（Step 3）According to measured train running speed v=125km/h, vehicle wheel roll radius R=0.46m, sentenced by table 1 is default
Knowable to hurt characteristic frequency calculation formula corresponding to ovalization wheel in fixing then, the hurt characteristic obtained by this method
Frequency complies fully with theoretical calculation formula, therefore can be determined that the train wheel has occurred that Wheel ovalization.
Claims (8)
1. a kind of wheel polygon recognition methods based on vertical wheel rail force, it is characterised in that the recognition methods includes following step
Suddenly：
（1）Vertical wheel rail force signal is gathered with shear stress method；
（2）Enter row set empirical modal EEMD decomposition to the vertical wheel rail force signal gathered, so as to obtain the sheet of lump
Levy modular function IMF components；
（3）Calculate the step（2）In comprising wheel polygon Disease Characters information intrinsic mode functions IMF marginal spectrum；
（4）According to the step（3）The intrinsic mode functions IMF of middle acquisition marginal spectrum, acquisition hurt characteristic frequency f, and according to
Default decision criteria, to identify whether wheel polygon and polygon-type occurs.
A kind of 2. wheel polygon recognition methods based on vertical wheel rail force according to claim 1, it is characterised in that institute
State step（1）In, the span centre opening position between the adjacent sleeper no less than 5 groups, strained in steel rail web position adhering resistance
Piece, each resistance strain gage gather the strain signal of the rail and are converted to the vertical wheel rail force signal.
3. a kind of wheel polygon recognition methods based on vertical wheel rail force according to claim 2, it is characterised in that will
The computational methods that the strain signal of the rail is converted to the vertical wheel rail force signal are：
P=|Qr|+Ql
Qr= Ql=(Jb/S)τ
τ=Gε
In formula：
P is the vertical wheel rail force；
QrAnd QlFor shearing；
J is the moment of inertia of the rail profile to natural axis；
B is the rail profile thickness at the natural axis；
Static moments of the S for section beyond Calculation Shear point to natural axis；
τ is the shear stress suffered by the rail；
G is the modulus of shearing of the rail；
The rail shearing strain that ε is collected by the resistance strain gage.
A kind of 4. wheel polygon recognition methods based on vertical wheel rail force according to claim 1, it is characterised in that institute
State step（2）In, the set empirical modal EEMD, which is decomposed, to be referred to the vertical wheel rail force signal gathered adding normal state
The white Gaussian noise of distribution, then decomposed the amended vertical wheel rail force signal using EMD methods.
A kind of 5. wheel polygon recognition methods based on vertical wheel rail force according to claim 1, it is characterised in that institute
State step（3）In, the marginal spectrum computational methods of the intrinsic mode functions IMF are：To IMF points of the intrinsic mode functions of the lump of acquisition
Amount carries out Hilbert conversion, so as to obtain the Hilbert spectrograms H of Energy distribution on time-frequency plane（ω, t）, calculation formula is as follows
It is shown：
To the Hilbert spectrograms H obtained（ω, t）The integration in time domain is carried out, so as to obtain marginal spectrum H（ω）, calculation formula
It is as follows：
In formula：
Re represents to take the real part of imaginary number；
a（T) it is the intrinsic mode functions IMF components of lump；
J is unit imaginary number,
ω is the instantaneous frequency in Hilbert conversion；
T is instantaneous moment.
A kind of 6. wheel polygon recognition methods based on vertical wheel rail force according to claim 1, it is characterised in that institute
State step（4）In, the default decision criteria is specially：
If periodicity wheel rail force wavelength X is 2 π R, hurt characteristic frequency f is v/2 π R, it is inclined that single order polygon, wheel occurs in wheel
The situation of the heart；
If periodicity wheel rail force wavelength X is π R, hurt characteristic frequency f is v/ π R, it is oval that second order polygon, wheel occurs in wheel
The situation of change；
If periodicity wheel rail force wavelength X is 2 π R/3, hurt characteristic frequency f is 3v/2 π R, there is three rank polygons, car in wheel
Take turns the situation of triangularization；
If periodicity wheel rail force wavelength X is π R/2, hurt characteristic frequency f is 2v/ π R, there is quadravalence polygon, wheel in wheel
The situation of quadrangle；
If periodicity wheel rail force wavelength X is 2 π R/N, hurt characteristic frequency f is vN/2 π R, there is N ranks polygon, wheel in wheel
The situation of N sides shape；
Wherein, R is the vehicle wheel roll radius；V is the speed of service of train.
7. according to a kind of dress of any described wheel polygon recognition methods based on vertical wheel rail force in claim 1-6
Put, it is characterised in that described device include strain acquirement module, train speed acquisition module, data long-distance transmission module and
Remote monitoring module, the strain acquirement module and the train speed acquisition module are through the data long-distance transmission module
Communication connection is formed with the remote monitoring module.
8. a kind of device of wheel polygon recognition methods based on vertical wheel rail force according to claim 7, its feature
It is also to be connected with vertical wheel rail force computing module, set empirical modal EEMD decomposition moulds on the remote monitoring module in turn
Block, signal margin spectrum computing module and wheel polygon judge module.
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| CN108593315A (en) * | 2018-04-25 | 2018-09-28 | 石家庄铁道大学 | The wheel polygon detecting method and terminal device of frequency domain character are vibrated based on axle box |
| CN108731953A (en) * | 2018-03-27 | 2018-11-02 | 常州路航轨道交通科技有限公司 | A kind of polygon failure on-line detecting method of Railway wheelset |
| CN109278796A (en) * | 2018-11-16 | 2019-01-29 | 北京主导时代科技有限公司 | A kind of vehicular wheel out of round degree detection system |
| CN109334708A (en) * | 2018-10-11 | 2019-02-15 | 北京声科测声学技术有限公司 | A kind of wheel polygon method for testing and analyzing and system |
| CN110171442A (en) * | 2019-06-12 | 2019-08-27 | 中国神华能源股份有限公司 | Detection system, the detection method of wheel flat |
| CN110806324A (en) * | 2019-11-11 | 2020-02-18 | 成都西交智众科技有限公司 | Wheel polygon abrasion detection method based on rail displacement and data acquisition equipment |
| CN111141206A (en) * | 2020-01-20 | 2020-05-12 | 中国农业大学 | Strain gauge dynamic characteristic detection device and test method thereof |
| CN111824207A (en) * | 2020-06-09 | 2020-10-27 | 华东交通大学 | Wheel out-of-roundness recognition method based on rail bottom strain |
| CN112991577A (en) * | 2021-02-25 | 2021-06-18 | 成都运达科技股份有限公司 | Railway vehicle wheel polygon state diagnostic system |
| CN114997252A (en) * | 2022-08-05 | 2022-09-02 | 西南交通大学 | Vehicle-mounted detection method for wheel polygon based on inertia principle |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108731953A (en) * | 2018-03-27 | 2018-11-02 | 常州路航轨道交通科技有限公司 | A kind of polygon failure on-line detecting method of Railway wheelset |
| CN108593315A (en) * | 2018-04-25 | 2018-09-28 | 石家庄铁道大学 | The wheel polygon detecting method and terminal device of frequency domain character are vibrated based on axle box |
| CN109334708A (en) * | 2018-10-11 | 2019-02-15 | 北京声科测声学技术有限公司 | A kind of wheel polygon method for testing and analyzing and system |
| CN109278796A (en) * | 2018-11-16 | 2019-01-29 | 北京主导时代科技有限公司 | A kind of vehicular wheel out of round degree detection system |
| CN110171442A (en) * | 2019-06-12 | 2019-08-27 | 中国神华能源股份有限公司 | Detection system, the detection method of wheel flat |
| CN110806324A (en) * | 2019-11-11 | 2020-02-18 | 成都西交智众科技有限公司 | Wheel polygon abrasion detection method based on rail displacement and data acquisition equipment |
| CN111141206A (en) * | 2020-01-20 | 2020-05-12 | 中国农业大学 | Strain gauge dynamic characteristic detection device and test method thereof |
| CN111141206B (en) * | 2020-01-20 | 2024-05-17 | 中国农业大学 | Strain gauge dynamic characteristic detection device and testing method thereof |
| CN111824207A (en) * | 2020-06-09 | 2020-10-27 | 华东交通大学 | Wheel out-of-roundness recognition method based on rail bottom strain |
| CN112991577A (en) * | 2021-02-25 | 2021-06-18 | 成都运达科技股份有限公司 | Railway vehicle wheel polygon state diagnostic system |
| CN112991577B (en) * | 2021-02-25 | 2022-08-02 | 成都运达科技股份有限公司 | Railway vehicle wheel polygon state diagnostic system |
| CN114997252A (en) * | 2022-08-05 | 2022-09-02 | 西南交通大学 | Vehicle-mounted detection method for wheel polygon based on inertia principle |
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Application publication date: 20180202 |