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.
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,
Be 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, and A ' is the amplitude of the low-frequency interference signal after extracting, and f is the frequency of low-frequency interference signal, and FS ' is the sample frequency after extracting,
' be the phase place after extracting, because effective that can obtain counting still less weighs signal, so can satisfy in embedded systems such as RAM, DSP, can use nonlinear algorithm handle this effectively the heavy signal of axle obtain the required parameter of nonlinear fitting with the heavy signal 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.