CN108731953A - A kind of polygon failure on-line detecting method of Railway wheelset - Google Patents
A kind of polygon failure on-line detecting method of Railway wheelset Download PDFInfo
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- CN108731953A CN108731953A CN201810260552.4A CN201810260552A CN108731953A CN 108731953 A CN108731953 A CN 108731953A CN 201810260552 A CN201810260552 A CN 201810260552A CN 108731953 A CN108731953 A CN 108731953A
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- G—PHYSICS
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention discloses a kind of polygon failure on-line detecting methods of Railway wheelset, including step：A judges that wheel whether there is polygon failure, if so, entering in next step；B judges the order of polygon failure existing for wheel；C judges the non-round degree of the rank polygon.In step A：Temporal signatures TZ_dB is calculated according to feature vector TZ_vector, if TZ_dB >=Threshold_1, there are polygon failures for wheel；In step B：Peak value number η, η in statistical nature vector T Z_vector envelopes more than Threshold_2 are the order of polygon failure；In step C：The most value TZ_n that circle internal vibration acceleration envelope is calculated using described eigenvector TZ_vector, with the non-round degree of the most value TZ_n quantitatively characterizings η rank polygons.The time series of vibration acceleration is converted to spatial sequence by the present invention, it is easier to be realized that wheel identifies the non-round order of polygon, dramatically be reduced detection difficulty；To characterize train wheel polygon failure, more traditional characterizing method validity higher dependent on the dimensionless dB values of historical data.
Description
Technical field
The invention belongs to railway transportation equipments and technical field of vehicle, and in particular to a kind of polygon failure of Railway wheelset
Online test method.
Background technology
Wheel polygonization feature is studied, can accurately and in real time detect wheel to polygonization situation exception feelings
Condition eliminates safe hidden trouble to adopt an effective measure in time, is necessary links in rail traffic vehicles safety monitoring.Currently, state
Inside and outside train wheel condition monitoring is broadly divided into two major classes：Artificial detection and equipment real time on-line monitoring.Present China is big
The detection of part of the train maintenance track division wheel condition is also mainly based on artificial.Artificial detection needs vehicle to be examined in track division routine
By worker's naked eyes, the modes such as key strikes, ear is listened or the inspections such as all kinds of artificial caliper tools are used after being disassembled when looking into repair.
Equipment method of real-time includes mainly eddy-current method, ultrasonic telemetry, vibration acceleration method, laser sensor method and image method
Deng.Equipment method of real-time is broadly divided into vehicle-mounted monitoring system and monitors system with trackside.
Vibration acceleration monitoring method is not only able to reflection defect, additionally it is possible to reflect the extent of injury of defect, and monitor peace
The low cost of dress.The advantages of this method is easy for installation, and performance is stablized, and monitoring rate is more accurate, and it is (big can to monitor high speed train
In 100km/h).But this method is in monitoring, since the exciting source of Wheel Rail Contact is varied, so data processing technique is extremely
It is crucial.This patent is exactly directed to this difficulties, gives algorithm solution.
Meanwhile there is also following disadvantages for the prior art：Manual method inspection keeps the train turnaround time long, and artificial detection
It being influenced by factors such as artificial origin, operating conditions, inaccuracy slow so as to cause detection speed and labor intensity are big, and this
Method is only effective to the flat sore failure of wheel, then even more helpless to wheel polygonization failure；Equipment all at present is real-time
Monitoring method also has certain limitation to wheel whether there is or not polygon fault identification is preferable, but to the identification of polygon wheel order
Property.
Invention content
In order to solve the above problem of the existing technology, present invention aims at provide a kind of polygon of Railway wheelset
The time series of vibration acceleration is converted to spatial sequence, more by failure on-line detecting method based on the method integrated in circle
Wheel easy to implement identifies the non-round order of polygon, dramatically reduces detection difficulty；It contacts historical data and characterizes train
Wheel polygon failure, the characterizing method are to rely on the dimensionless dB values characterization of historical data, and more traditional characterizing method is effective
Property higher.
The technical solution adopted in the present invention is：A kind of polygon failure on-line detecting method of Railway wheelset, including with
Lower process：
A, judge that wheel whether there is polygon failure, if so, entering in next step；
B, judge the order of polygon failure existing for wheel；
C, judge the non-round degree of the rank polygon.
Further, the step A includes following procedure：
A1, the feature vector TZ_vector that internal vibration acceleration is enclosed according to circle inner product calculation of group dividing one；
A2, temporal signatures TZ_dB is calculated,
If A3, TZ_dB >=Threshold_1, there are polygon failures for wheel；
Wherein, 0.01g is the virtual value of failure-free data, and 0.01 is empirical value, and g is acceleration of gravity；Threshold_1
To determine whether the time domain dB eigenvalue thresholds of polygon.
Further, the step B specifically includes following steps：
B1, feature vector TZ_vector envelopes are calculated；
Peak value number η, η in B2, statistics envelope more than Threshold_2 are the order of polygon failure, wherein
Threshold_2 is the threshold value for judging how many rank polygon.
Further, the step C is specifically included：
C1, judge whether η ranks polygon is more apparent：If 4≤η≤40, enter in next step；
The non-round degree of C2, quantitatively characterizing η rank polygons.
Further, the step C2 specifically includes following procedure：It is calculated in circle using described eigenvector TZ_vector
The most value TZ_n of vibration acceleration envelope, with the non-round degree of the most value TZ_n quantitatively characterizings η rank polygons, wherein：
TZ_n=max (envelope (TZ_vector)).
Further, the feature vector TZ_ of internal vibration acceleration is enclosed in the step A1 according to circle inner product calculation of group dividing one
Vector specifically includes following steps：
A11, speed data N1 axle box vibration acceleration data corresponding with the velocity sampling interval time are read
Acc；
A12, calculating speed data speed obtain in the unit interval discrete integration of time variable T_speed
Journey Li；
A13, vibration acceleration data Acc steppings forward；
A14, judge whether mileage number Li is more than wheel circumference c, if it is not, A11 is entered step, otherwise, into next step；
A15, mileage number are reset, and wheel adds 1 to rolling the number of turns, obtains taking turns the spatial sequence Acc_ on circumferencial direction
Circle encloses interior data intermediate quantity circle and resets；
A16, wheel are to rolling whether the number of turns reaches N2, if it is not, A11 is entered step, otherwise, into next step；
A17, by ask by N2 circle internal vibration data form matrix A cc_Circle row or column (depend on sample data
Column vector or row vector) the feature vector TZ_vector for being worth to a circle internal vibration acceleration；
Wherein speed is train speed；N1 is the ratio of vibration acceleration sample frequency and speed sampling frequency；N2 is circle
Interior differentiation step-length takes how many circles as the differentiation period.
Beneficial effects of the present invention are：
(1) time series of vibration acceleration can be transformed into spatial domain by integral in circle, it is easier to realize wheel to polygon
The non-round order identification of shape, dramatically reduces detection difficulty；
(2) present invention proposes a kind of train wheel polygon fault signature method of contact historical data, the characterization side
Method is to rely on the dimensionless dB values characterization of historical data, more traditional characterizing method validity higher；
(3) characterizing method is based on study, therefore can realize the polygon failure to different types of rail traffic vehicles
Characterization, it is applied widely；
(4) the polygon failure on-line detecting method of Railway wheelset provided by the invention, meter more simple compared with traditional algorithm
Calculation amount is small, and calculation amount meets the real-time calculating under level of hardware at this stage；
(5) the polygon failure on-line detecting method of Railway wheelset provided by the invention is added based on the vibration sampled whens waiting
Velocity sensor gathered data, such sensor is easy for installation, and performance is stablized, and the vehicle of high-speed cruising can be monitored.
Description of the drawings
Fig. 1 is the flow chart of the polygon failure on-line detecting method of Railway wheelset.
Fig. 2 is the flow chart for the feature vector that internal vibration acceleration is enclosed using circle inner product calculation of group dividing one.
Specific implementation mode
Below in conjunction with the accompanying drawings and specific embodiment does further explaination to the present invention.
Embodiment
As shown in Figure 1, a kind of polygon failure on-line detecting method of Railway wheelset, including following procedure：
A, judge that wheel whether there is polygon failure, if so, entering in next step；
B, judge the order of polygon failure existing for wheel；
C, judge the non-round degree of the rank polygon.
In another embodiment, the step A includes following procedure：
A1, the feature vector TZ_vector that internal vibration acceleration is enclosed according to circle inner product calculation of group dividing one；
A2, temporal signatures TZ_dB is calculated,
If A3, TZ_dB >=Threshold_1, there are polygon failures for wheel；
Wherein, 0.01g is the virtual value of failure-free data, and 0.01 is empirical value, and g is acceleration of gravity；Threshold_1
To determine whether the time domain dB eigenvalue thresholds of polygon.
In another embodiment, the step B specifically includes following steps：
B1, feature vector TZ_vector envelopes are calculated；
Peak value number η, η in B2, statistics envelope more than Threshold_2 are the order of polygon failure, wherein
Threshold_2 is the threshold value for judging how many rank polygon.
In another embodiment, the step C is specifically included：
C1, judge whether η ranks polygon is more apparent：If 4≤η≤40, enter in next step；
The non-round degree of C2, quantitatively characterizing η rank polygons.
In another embodiment, the step C2 specifically includes following procedure：Utilize described eigenvector TZ_vector
The most value TZ_n for calculating circle internal vibration acceleration envelope, with the non-round degree of the most value TZ_n quantitatively characterizings η rank polygons,
In：
TZ_n=max (envelope (TZ_vector)).
As shown in Fig. 2, the feature vector TZ_ of internal vibration acceleration is enclosed in the step A1 according to circle inner product calculation of group dividing one
Vector specifically includes following steps：
A11, speed data N1 axle box vibration acceleration data corresponding with the velocity sampling interval time are read
Acc；
A12, calculating speed data speed obtain in the unit interval discrete integration of time variable T_speed
Number of passes Li：Li=speed (i) × T_speed+Li；
A13, vibration acceleration data Acc steppings forward：Circle=(circle, Acc)；
A14, judge whether mileage number Li is more than wheel perimeter c, if it is not, A11 is entered step, otherwise, into next step；
A15, mileage number Li are reset, and wheel adds 1 to rolling number of turns N_circle, obtains taking turns the spatial sequence on circumferencial direction
Acc_Circle encloses interior data intermediate quantity circle and resets：
Li=0；N_Circle=N_Circle+1；
Acc_Circle(:, N_Circle) and=Circle；
Circle=NULL；
A16, wheel are to rolling whether number of turns N_circle reaches N2, if it is not, A11 is entered step, otherwise, into next step；
A17, by ask by N2 circle internal vibration data form matrix A cc_Circle row or column (depend on sample data
Column vector or row vector) the feature vector TZ_vector for being worth to a circle internal vibration acceleration：
TZ_vector=mean (Acc_circle ')；
Wherein speed is train speed；N1 is the ratio of vibration acceleration sample frequency and speed sampling frequency；N2 is circle
Interior differentiation step-length takes how many circles as the differentiation period；Acc_Circle ' is the transposition of matrix A cc_Circle.
The present invention is not limited to above-mentioned optional embodiment, anyone can show that other are each under the inspiration of the present invention
The product of kind form.Above-mentioned specific implementation mode should not be understood the limitation of pairs of protection scope of the present invention, protection of the invention
Range should be subject to be defined in claims, and specification can be used for interpreting the claims.
Claims (6)
1. a kind of polygon failure on-line detecting method of Railway wheelset, which is characterized in that including following procedure：
A, judge that wheel whether there is polygon failure, if so, entering in next step；
B, judge the order of polygon failure existing for wheel；
C, judge the non-round degree of the rank polygon.
2. the polygon failure on-line detecting method of Railway wheelset according to claim 1, which is characterized in that the step
A includes following procedure：
A1, the feature vector TZ_vector that internal vibration acceleration is enclosed according to circle inner product calculation of group dividing one；
A2, temporal signatures TZ_dB is calculated,
If A3, TZ_dB >=Threshold_1, there are polygon failures for wheel；
Wherein, 0.01g is the virtual value of failure-free data, and 0.01 is empirical value, and g is acceleration of gravity；Threshold_1 is to sentence
It is disconnected that whether there is or not the time domain dB eigenvalue thresholds of polygon.
3. the polygon failure on-line detecting method of Railway wheelset according to claim 2, which is characterized in that the step
B specifically includes following steps：
B1, feature vector TZ_vector envelopes are calculated；
Peak value number η, η in B2, statistics envelope more than Threshold_2 are the order of polygon failure, wherein
Threshold_2 is the threshold value for judging how many rank polygon.
4. the polygon failure on-line detecting method of Railway wheelset according to claim 3, which is characterized in that the step
C is specifically included：
C1, judge whether η ranks polygon is more apparent：If 4≤η≤40, enter in next step；
The non-round degree of C2, quantitatively characterizing η rank polygons.
5. the polygon failure on-line detecting method of Railway wheelset according to claim 4, which is characterized in that the step
C2 specifically includes following procedure：The most value TZ_n of circle internal vibration acceleration envelope is calculated using described eigenvector TZ_vector,
With the non-round degree of the most value TZ_n quantitatively characterizings η rank polygons, wherein：
TZ_n=max (envelope (TZ_vector)).
6. according to the polygon failure on-line detecting method of claim 2-5 any one of them Railway wheelsets, which is characterized in that
In the step A1 according to circle inner product calculation of group dividing one enclose internal vibration acceleration feature vector TZ_vector specifically include it is following
Step：
A11, speed data N1 axle box vibration acceleration data Acc corresponding with the velocity sampling interval time is read；
A12, calculating speed data speed obtain the mileage Li of unit interval to a discrete integration of time variable T_speed；
A13, vibration acceleration data Acc steppings forward；
A14, judge whether mileage number Li is more than wheel circumference c, if it is not, A11 is entered step, otherwise, into next step；
A15, mileage number are reset, and wheel adds 1 to rolling the number of turns, obtains taking turns the spatial sequence Acc_Circle on circumferencial direction, encloses
Interior data intermediate quantity circle is reset；
A16, wheel are to rolling whether the number of turns reaches N2, if it is not, A11 is entered step, otherwise, into next step；
A17, it is shaken by N2 being worth in a circle for matrix A cc_Circle row or column that forms of circle internal vibration data by asking
The feature vector TZ_vector of dynamic acceleration；
Wherein speed is train speed；N1 is the ratio of vibration acceleration sample frequency and speed sampling frequency；N2 is sentenced in circle
Other step-length takes how many circles as the differentiation period.
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| CN201810260552.4A CN108731953B (en) | 2018-03-27 | 2018-03-27 | Online detection method for polygonal fault of train wheel set |
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| 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 |
| CN110979390A (en) * | 2019-12-05 | 2020-04-10 | 中车株洲电力机车有限公司 | Method and system for repairing polygonal wheel of rail transit vehicle |
| CN112766043A (en) * | 2020-12-25 | 2021-05-07 | 北京安铁软件技术有限公司 | Train wheel polygon detection signal processing method and system |
| CN113776760A (en) * | 2020-06-09 | 2021-12-10 | 成都运达科技股份有限公司 | Train wheel set out-of-round fault monitoring method and system based on whole-axle vibration analysis |
| WO2023138581A1 (en) * | 2022-01-20 | 2023-07-27 | 清华大学 | Method and apparatus for detecting polygonal fault of wheel set of rail transit locomotive |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
| CN110979390A (en) * | 2019-12-05 | 2020-04-10 | 中车株洲电力机车有限公司 | Method and system for repairing polygonal wheel of rail transit vehicle |
| CN110979390B (en) * | 2019-12-05 | 2021-10-26 | 中车株洲电力机车有限公司 | Method and system for repairing polygonal wheel of rail transit vehicle |
| CN113776760A (en) * | 2020-06-09 | 2021-12-10 | 成都运达科技股份有限公司 | Train wheel set out-of-round fault monitoring method and system based on whole-axle vibration analysis |
| CN113776760B (en) * | 2020-06-09 | 2023-06-27 | 成都运达科技股份有限公司 | Train wheel set out-of-round fault monitoring method and system based on whole-axis vibration analysis |
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| CN112766043B (en) * | 2020-12-25 | 2023-10-17 | 北京安铁软件技术有限公司 | Train wheel polygon detection signal processing method and system |
| WO2023138581A1 (en) * | 2022-01-20 | 2023-07-27 | 清华大学 | Method and apparatus for detecting polygonal fault of wheel set of rail transit locomotive |
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