CN101196421A - Axle weight evaluation method for vehicle dynamic weighing system - Google Patents
Axle weight evaluation method for vehicle dynamic weighing system Download PDFInfo
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- CN101196421A CN101196421A CNA2007101918909A CN200710191890A CN101196421A CN 101196421 A CN101196421 A CN 101196421A CN A2007101918909 A CNA2007101918909 A CN A2007101918909A CN 200710191890 A CN200710191890 A CN 200710191890A CN 101196421 A CN101196421 A CN 101196421A
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Abstract
The invention relates to an axle weight estimation method of vehicle dynamic weighing system. Firstly, when the vehicle passes by the dynamic axle weight balance, a weighting sensor collects the original axle weight signal of each vehicle axle. The original axle weight signal comprises static axle weight signal, low-frequency inference signal and high-frequency inference signal and counting sampling point; secondly, the high-frequency inference signal with the frequency not less than 50Hz is filtered; thirdly, counting the speed of vehicle axle; fourthly, intercepting the effective sampling point when the vehicle pass spy the dynamic axle weight balance according to the speed of vehicle speed to achieve effective axle weight signal; fifthly, on the basis of effective axle weight signal, drawing out 10 to 150 data points to compose the axle weight signal for simplifying counting; sixthly, using axle weight signal to achieve the index needed by the non-linear fitting, and using the non-linear fitting counting method to get the similar low-frequency inference signal; seventhly, using the effective axle weight signal and axle weight signal to reduce the similar low-frequency inference signal to get the evaluated static axle weight signal. The invention can reduce counting and lower the requirements to hardware, which can improve the weighting precision of dynamic axle weight balance.
Description
Technical field
The present invention relates to a kind of axle weight evaluation method of vehicle dynamic weighing system, belong to the vehicle weighing field.
Background technology
Carry in overload anticipation, bridge overload warning, the highway toll and axle carries widespread use vehicle dynamic weighing system in the dynamic weighting at the road vehicle axle, and along with the quickening demand of capital construction constantly increases.But in vehicle dynamic weighing system, undesired signal mainly comes from high-frequency interferencing signal and low-frequency interference signal, high-frequency interferencing signal can be eliminated by high frequency filter, but low frequency signal can't be used the method filtering of low-pass filtering, and low-frequency interference signal accounts for the major part of undesired signal, therefore when the travel speed of vehicle is higher than 10km/h, bigger deviation can appear in measuring accuracy, repeatability also greatly reduces, therefore average error does not wait 5%～30%, because dynamic shaft revaluation low precision, therefore limited its range of application, an axle revaluation precision that improves the heavy system of dynamic shaft is necessary, and an axle revaluation precision key that improves dynamic axle weight scale is its computing method.But Traditional calculating methods, as ADV method, DV method, V method, the method for average etc., can not satisfy practical application request, and the modern computing method, as filter method, intelligent method etc., because of it calculates too complicated and former thereby can not be applied in the actual dynamic weighing system to requirements for hardware height etc.
Summary of the invention
The purpose of this invention is to provide a kind of energy minimizing calculating and reduction to requirements for hardware, and can improve the axle weight evaluation method of the vehicle dynamic weighing system of weighing precision.
The present invention is that the technical scheme that achieves the above object is: a kind of axle weight evaluation method of vehicle dynamic weighing system is characterized in that:
(1), vehicle during by dynamic axle weight scale LOAD CELLS gather the heavy signal of original axis of every axletree, the heavy signal of original axis comprises static shaft heavy signal, low-frequency interference signal and high-frequency interferencing signal, and statistic sampling is counted;
(2), the high-frequency interferencing signal of rejection frequency 〉=50Hz;
(3), calculate axletree speed according to the sample frequency of sampling number, setting and the vehicle stroke by dynamic axle weight scale;
(4), the efficiently sampling according to axletree speed intercepting vehicle during by dynamic axle weight scale counts, and obtains the effectively heavy signal of axle;
(5), on the basis of the effective heavy signal of axle, extract 10～150 data points, form the heavy signal of axle that is used to simplify calculating;
(6), the heavy signal of usefulness axle obtains the required parameter of nonlinear fitting, and draws approximate low-frequency interference signal with the nonlinear fitting algorithm;
(7), with effectively heavy signal of axle or the heavy signal of axle deduct approximate low-frequency interference signal, the heavy signal of the static shaft that obtains estimating.
The present invention adopts its advantage of top technical scheme to be:
1, the present invention is according to the sampling number of acquired signal, the speed of dynamic estimation vehicle every axle during by dynamic axle weight scale, and use based on the method for speed intercepting efficiently sampling and count and obtain the effectively heavy signal of axle, can guarantee that effective heavy signal intercepting is accurately and reliably.
2, the present invention is directed to the nonlinear fitting algorithm, as the Levenberg-Marquardt fitting algorithm hardware platform is required situation high, that can not satisfy practical application, the method of processing is extracted in employing to effective heavy signal, promptly guaranteed computational accuracy, simplified computational complexity again, can be applicable in the existing dynamic weighing system.
3, the present invention is directed to the indefinite situation of low-frequency interference signal frequency, adopt the nonlinear fitting algorithm of changeable frequency that undesired signal is carried out process of fitting treatment, can obtain approximate low-frequency interference signal, remove the accuracy of low-frequency interference signal owing to improve, and improve the weighing precision of dynamic axle weight scale.
Description of drawings
Below in conjunction with accompanying drawing embodiments of the invention are described in further detail.
Fig. 1 is the synoptic diagram of the heavy weighing system of vehicle dynamic axle of the present invention.
Fig. 2 is the process flow diagram of the axle weight evaluation method of vehicle dynamic weighing system of the present invention.
Fig. 3 is the oscillogram of the present invention according to the resultant effective heavy signal in axle speed intercepting back.
Fig. 4 is the oscillogram that the heavy signal of the effective axle of the present invention and the static shaft of estimation after treatment weigh signal.
Embodiment
When vehicle during along the weighing platform 1 of direction shown in Figure 1 by dynamic axle weight scale, whether discern by the switching of 1 liang of sidelight curtain 2 of weighing platform has vehicle to pass through, discern through 3 pairs of wheel shafts of tire recognizer again, ground induction coil 4 is standby as light curtain 2, gather an axle tuple certificate by being installed in weighing platform 1 following four LOAD CELLS or more LOAD CELLS, and adopt signal processing platform based on RAM, adopt 51 chips as the signal controlling platform, between the two by and port communications.
The axle weight evaluation method of vehicle dynamic weighing system of the present invention is seen shown in Figure 2, gather the heavy signal of original axis of every axletree by being installed in weighing platform 1 following LOAD CELLS, the heavy signal of original axis comprises static shaft heavy signal, low-frequency interference signal and high-frequency interferencing signal, and statistic sampling counts, and the discrete mathematics model of the heavy signal of original axis can be:
In the above-mentioned formula, y (n) is the discrete mathematics model of the heavy signal of original axis, and w is that the static shaft of vehicle is heavy, and A is the amplitude of low-frequency interference signal, and f is the frequency of low-frequency interference signal, and FS is the sample frequency that weighing system is set, and is the phase place of low-frequency interference signal, A
iBe the amplitude of high-frequency interferencing signal, f
iBe the frequency of high-frequency interferencing signal,
iPhase place for high-frequency interferencing signal.
With FIR, IIR type low-pass filter the heavy signal of original axis is carried out pre-service, contained high-frequency interferencing signal in the heavy signal of filtering original axis, the high-frequency interferencing signal of wave filter rejection frequency 〉=50Hz, the heavy signal of axle this moment just only contains the heavy and low-frequency interference signal of static shaft basically.
Calculate axletree speed according to the sample frequency of sampling number, setting and the vehicle stroke by dynamic axle weight scale; The mathematic(al) representation of this axletree speed is: v=FS* (L+ Δ)/length, wherein, v is the speed of vehicle every axle during by dynamic axle weight scale, FS is a sample frequency, and the L+ Δ is the vehicle stroke of dynamic axle weight scale up and down, and L is the weighing platform width of dynamic axle weight scale, and Δ is a penalty coefficient, Δ is between 0.2～0.8, and length is a sampling number.
According to the axletree speed that calculates, efficiently sampling when intercepting vehicle by dynamic axle weight scale is counted, obtain the effectively heavy signal of axle, see shown in Figure 3, oscillogram for the heavy signal of effective axle, owing to gather and comprised dynamic axle weight scale section on the vehicle in the heavy signal of axle, vehicle is dynamic axle weight scale Duan Gongsan section under effectively section of weighing on the dynamic axle weight scale and vehicle, therefore can by remove on the vehicle dynamically the axle weight scale section and down the sampling number of dynamic axle weight scale section obtain efficiently sampling and count, this efficiently sampling is counted and can be obtained by calculating, its mathematic(al) representation is L2=length-2L1, wherein L2 is that efficiently sampling is counted, L1 is dynamically an axle weight scale section and the sampling number of dynamic axle weight scale section down on the vehicle, since on the vehicle dynamically under axle weight scale and the vehicle sampling number L1 and the axletree speed v of dynamic axle weight scale section be inversely proportional to, therefore can be by calculating, its mathematic(al) representation is L1=FS*S/v, wherein S is dynamically an axle weight scale section and the displacement of dynamic axle weight scale section process down on the vehicle, owing to removed the vehicle dynamic fluctuation part during axle weight scale up and down, so can improve the accuracy that car weight is estimated.
On the basis of the heavy signal of effective axle, extract 10～150 data points, in sampling process, can adopt modes such as running mean, simple extraction, effective heavy signal after sampling formed the heavy signal of axle that is used to simplify calculating, and the mathematic(al) representation of this heavy signal is:
X (n)=w+A ' * sin (2*pi*f*n*/FS '+ '), wherein x (n) is the discrete mathematics model, A ' is the amplitude of the low-frequency interference signal after extracting, f is the frequency of low-frequency interference signal, FS ' is the sample frequency after extracting, ' is the phase place after extracting, because effective that can obtain counting still less weighs signal, so can satisfy at RAM, can use nonlinear algorithm in the embedded systems such as DSP and handle this an effectively heavy signal required parameter of the heavy signal acquisition nonlinear fitting of axle, from the mathematic(al) representation of the heavy signal of axle, can learn, with this model of nonlinear fitting algorithm match, need four parameters, i.e. the heavy initial value w of axle
0, low-frequency interference signal amplitude initial value A
0, low-frequency interference signal frequency initial value f
0Phase place initial value with low-frequency interference signal
0Wherein, the heavy initial value w of axle
0Can obtain by the mean value of the heavy signal of reference axis, and N is the number of data points after sampling, or adopts the maximal value of the heavy signal of axle or minimum value to obtain,
Its mathematic(al) representation is:
And the amplitude initial value A of low-frequency interference signal
0Obtain weight with half of maximal value Max in the heavy signal of axle and minimum M in, its numeral expression formula is:
And the frequency initial value f of low-frequency interference signal
0Can select 1Hz～5Hz for use, or calculate acquisition with maximal value coordinate Maxp, the minimum value coordinate Minp of the heavy signal of respective shaft and the sample frequency FS ' after the extraction, its mathematic(al) representation is f
0=FS '/(Maxp-Minp|) is owing to can guarantee maximum value and minimal value in certain scope, so can guarantee to satisfy the scope of the frequency of low-frequency interference signal.The phase place initial value of low-frequency interference signal
0Can select 0.5～2 for use, or weigh first extreme point coordinate pp, the low-frequency interference signal frequency initial value f of signal with respective shaft
0And the calculating of the sample frequency FS ' after extracting, being transformed into again in 0～2 π scope, its mathematic(al) representation is:
Obtain the phase place initial value of low-frequency interference signal
0, drawing approximate low-frequency interference signal with the nonlinear fitting algorithm, the mathematic(al) representation of approximate low-frequency interference signal is:
Wherein,
The amplitude of the low-frequency disturbance that obtains for match,
The frequency of the low-frequency disturbance that obtains for match
The phase place that obtains for match.
Deduct approximate low-frequency interference signal with a heavy signal of effective axle or the heavy signal of axle at last, the static shaft that can obtain estimating weighs signal, and from Fig. 4 oscillogram as can be seen, the waveform of the heavy signal of the static shaft of estimation is compared smoother with the heavy signal waveform of axle.
With the axle weight evaluation method of the vehicle dynamic weighing system of the present invention heavy signal Processing of axle to gathering, car speed is at≤20km/h, axletree gross weight evaluated error is controlled at ± 2.5% in, improved the weighing precision of dynamic axle weight scale.
Claims (10)
1. the axle weight evaluation method of a vehicle dynamic weighing system is characterized in that:
(1), vehicle during by dynamic axle weight scale LOAD CELLS gather the heavy signal of original axis of every axletree, the heavy signal of original axis comprises static shaft heavy signal, low-frequency interference signal and high-frequency interferencing signal, and statistic sampling is counted;
(2), the high-frequency interferencing signal of rejection frequency 〉=50Hz;
(3), calculate axletree speed according to the sample frequency of sampling number, setting and the vehicle stroke by dynamic axle weight scale;
(4), the efficiently sampling according to axletree speed intercepting vehicle during by dynamic axle weight scale counts, and obtains the effectively heavy signal of axle;
(5), on the basis of the effective heavy signal of axle, extract 10～150 data points, form the heavy signal of axle that is used to simplify calculating;
(6), the heavy signal of usefulness axle obtains the required parameter of nonlinear fitting, and draws approximate low-frequency interference signal with the nonlinear fitting algorithm;
(7), with effectively heavy signal of axle or the heavy signal of axle deduct approximate low-frequency interference signal, the heavy signal of the static shaft that obtains estimating.
2. the axle weight evaluation method of vehicle dynamic weighing system according to claim 1, it is characterized in that: the mathematic(al) representation of described axletree speed is: v=FS* (L+ Δ)/length, wherein, v is the speed of vehicle every axle during by dynamic axle weight scale; FS is a sample frequency; L is the weighing platform width of dynamic axle weight scale; Δ is a penalty coefficient, and Δ is between 0.2～0.8, and length is a sampling number.
3. the axle weight evaluation method of vehicle dynamic weighing system according to claim 1, it is characterized in that: the mathematic(al) representation that described efficiently sampling is counted is: L2=length-2L1, wherein L2 is that efficiently sampling is counted, L1 is dynamically an axle weight scale section and the sampling number of dynamic axle weight scale section down on the vehicle, the mathematic(al) representation of this sampling number is: L1=FS*S/v, wherein S is dynamically an axle weight scale section and the displacement of dynamic axle weight scale section process down on the vehicle.
4. the axle weight evaluation method of vehicle dynamic weighing system according to claim 1, it is characterized in that: the mathematic(al) representation of the heavy signal of described axle: x (n)=w+A ' * sin (2*pi*f*n*/FS '+ '), wherein x (n) is the discrete mathematics model, A ' is the amplitude of the low-frequency interference signal after extracting, f is the frequency of low-frequency interference signal, FS ' is the sample frequency after extracting, and ' is the phase place after extracting.
5. the axle weight evaluation method of vehicle dynamic weighing system according to claim 1, it is characterized in that: the parameter of described nonlinear fitting comprises a heavy initial value w
0, low-frequency interference signal amplitude initial value A
0, low-frequency interference signal frequency initial value f
0Phase place initial value with low-frequency interference signal
0
6. the axle weight evaluation method of vehicle dynamic weighing system according to claim 5 is characterized in that: the heavy initial value w of described axle
0Mean value, maximal value or minimum value by the heavy signal of reference axis obtain, and its mathematic(al) representation is:
7. the axle weight evaluation method of vehicle dynamic weighing system according to claim 5 is characterized in that: the amplitude initial value A of described low-frequency interference signal
0For maximal value Max and minimum M in the heavy signal of axle half, its numeral expression formula is
8. the axle weight evaluation method of vehicle dynamic weighing system according to claim 5 is characterized in that: the frequency initial value f of described low-frequency interference signal
0Span is at 1Hz～5Hz, or with maximal value coordinate Maxp, the minimum value coordinate Minp of the heavy signal of respective shaft and the sample frequency FS ' after extracting calculate and obtain, its mathematic(al) representation is f
0=FS '/(| Maxp-Minp|).
9. the axle weight evaluation method of vehicle dynamic weighing system according to claim 5 is characterized in that: the phase place initial value of described low-frequency interference signal
0Span is 0.5～2, or with first extreme point coordinate pp, the low-frequency interference signal frequency initial value f of the heavy signal of respective shaft
0And the calculating of the sample frequency FS ' after extracting, being transformed into again in 0～2 π scope, its mathematic(al) representation is:
10. the axle weight evaluation method of vehicle dynamic weighing system according to claim 1, it is characterized in that: the mathematic(al) representation of described approximate low-frequency interference signal is
Wherein
The amplitude of the low-frequency disturbance that obtains for match,
The frequency of the low-frequency disturbance that obtains for match,
The phase place that obtains for match.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103776523A (en) * | 2014-02-26 | 2014-05-07 | 济钢集团有限公司 | Whole vehicle metering method of full-table dynamic rail weighbridge |
| CN105352576A (en) * | 2015-11-27 | 2016-02-24 | 太原磅管家科技有限公司 | Vehicle separation method based on dynamic weighing system |
| CN106066202A (en) * | 2016-06-01 | 2016-11-02 | 河北中岗通讯工程有限公司 | Digitalized axle simulation identification device and method |
| CN107560699A (en) * | 2016-06-30 | 2018-01-09 | 长城汽车股份有限公司 | Stationary vehicle weighing system and method and computing unit |
| CN109916488A (en) * | 2017-12-13 | 2019-06-21 | 北京万集科技股份有限公司 | Dynamic vehicle weighing method and device |
| CN112161689A (en) * | 2020-07-27 | 2021-01-01 | 江苏量动信息科技有限公司 | Weighing error estimation method and device of dynamic automobile scale based on measurement error theory |
Family Cites Families (2)
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| CN2646678Y (en) * | 2003-09-03 | 2004-10-06 | 北京万集科技有限责任公司 | Dynamic axle load metering equipment |
| CN100533073C (en) * | 2006-05-08 | 2009-08-26 | 金华市东方巨龙衡器有限公司 | Dynamic weighting system and method for vehicle |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103776523A (en) * | 2014-02-26 | 2014-05-07 | 济钢集团有限公司 | Whole vehicle metering method of full-table dynamic rail weighbridge |
| CN103776523B (en) * | 2014-02-26 | 2015-12-02 | 济钢集团有限公司 | The car load metering method of a kind of full court face dynamic railway truck scale |
| CN105352576A (en) * | 2015-11-27 | 2016-02-24 | 太原磅管家科技有限公司 | Vehicle separation method based on dynamic weighing system |
| CN106066202A (en) * | 2016-06-01 | 2016-11-02 | 河北中岗通讯工程有限公司 | Digitalized axle simulation identification device and method |
| CN107560699A (en) * | 2016-06-30 | 2018-01-09 | 长城汽车股份有限公司 | Stationary vehicle weighing system and method and computing unit |
| CN109916488A (en) * | 2017-12-13 | 2019-06-21 | 北京万集科技股份有限公司 | Dynamic vehicle weighing method and device |
| CN109916488B (en) * | 2017-12-13 | 2021-02-09 | 北京万集科技股份有限公司 | Dynamic vehicle weighing method and device |
| CN112161689A (en) * | 2020-07-27 | 2021-01-01 | 江苏量动信息科技有限公司 | Weighing error estimation method and device of dynamic automobile scale based on measurement error theory |
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Address after: 213125 No. 111 Taihu West Road, Xinbei District, Changzhou City, Jiangsu Province Co-patentee after: Mettler-Toledo (Changzhou) Precision Instrument Co., Ltd. Patentee after: Mettler-Toledo (Changzhou) Measurement Technology Co., Ltd. Co-patentee after: Mettler-Toledo (Changzhou) Weighing Equipment System Co., Ltd. Address before: 213022 No. 12 Kunlun Road, Xinbei District, Changzhou City, Jiangsu Province Co-patentee before: Mettler-Toledo (Changzhou) Precision Instruments Co., Ltd. Patentee before: Mettler-Toledo (Changzhou) Weighing Equipment System Co., Ltd. Co-patentee before: Mettler-Toledo (Changzhou) Measurement Technology Co., Ltd. |