CN101196421B - Axle weight evaluation method for vehicle dynamic weighing system - Google Patents
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Abstract
The invention relates to an axle weight estimation method of a vehicle dynamic weighing system, comprising the following steps: (1) when the vehicle passes by a dynamic axle weight balance, collecting an original axle weight signal of each vehicle axle by a weighting sensor, the original axle weight signal comprising a static axle weight signal, a low-frequency inference signal and a high-frequency inference signal, counting sampling point; (2) filtering the high-frequency inference signal with the frequency not less than 50Hz; (3) calculating a speed of the vehicle axle; (4) intercepting the effective sampling point number when the vehicle passes by the dynamic axle weight balance according to the speed of vehicle axle to obtain an effective axle weight signal; (5) on the basis of the effective axle weight signal, extracting 10 to 150 data points to compose the axle weight signal for simplifying calculation; (6) obtaining a parameter required by the non-linear fitting by using the axle weight signal, and obtaining an approximate low-frequency inference signal by using the non-linear fitting algorithm; (7) obtaining an evaluated static axle weight signal by subtracting the approximate low-frequency inference signal from the effective axle weight signal or the axle weight signal. The invention can reduce calculation and lower the requirements to a hardware system, and improve the weighting precision of the dynamic axle weight balance.
Description
Technical Field
The invention relates to an axle weight estimation method of a vehicle dynamic weighing system, and belongs to the field of vehicle weighing.
Background
The dynamic weighing system is widely applied to the axle load overload prejudgment of road vehicles, the bridge overload alarm, the road toll and the axle load dynamic weighing measurement, and the demand is increased along with the acceleration of the infrastructure. However, in the dynamic weighing system of the vehicle, the interference signals mainly come from high-frequency interference signals and low-frequency interference signals, the high-frequency interference signals can be eliminated through a high-frequency filter, but the low-frequency signals cannot be filtered by a low-pass filtering method, and the low-frequency interference signals account for most of the interference signals, so that when the driving speed of the vehicle is higher than 10km/h, the measurement accuracy has larger deviation, the repeatability is greatly reduced, the average error is not equal to 5% -30%, the application range of the dynamic axle weight system is limited due to the poor dynamic axle weight estimation accuracy, the axle weight estimation accuracy of the dynamic axle weight system is very necessary to be improved, and the key point for improving the axle weight estimation accuracy of the dynamic axle weight scale is the calculation method. However, the traditional calculation methods, such as the ADV method, the DV method, the V method, and the averaging method, cannot satisfy the actual application requirements, while the modern calculation methods, such as the filtering method and the intelligent method, cannot be applied to the actual dynamic weighing system due to the complicated calculation and the high requirement on the hardware system.
Disclosure of Invention
The invention aims to provide an axle weight estimation method of a vehicle dynamic weighing system, which can reduce calculation and requirements on a hardware system and improve weighing precision.
The technical scheme for achieving the aim of the invention is as follows: an axle weight estimation method of a vehicle dynamic weighing system is characterized in that:
(1) when the vehicle passes through the dynamic axle weight scale, the weighing sensor collects original axle weight signals of each axle, the original axle weight signals comprise static axle weight signals, low-frequency interference signals and high-frequency interference signals, and the number of sampling points is counted;
(2) filtering out high-frequency interference signals with the frequency being more than or equal to 50 Hz;
(3) calculating the speed of the axle according to the number of sampling points, the set sampling frequency and the stroke of the vehicle passing through the dynamic axle weight scale;
(4) intercepting the effective sampling points when the vehicle passes through the dynamic axle weight scale according to the axle speed to obtain an effective axle weight signal;
(5) extracting 10-150 data points on the basis of the effective axle weight signal to form an axle weight signal for simplifying calculation;
(6) obtaining parameters required by nonlinear fitting by using the axle weight signal, and obtaining an approximate low-frequency interference signal by using a nonlinear fitting algorithm;
(7) and subtracting the approximate low-frequency interference signal from the effective axle weight signal or the axle weight signal to obtain an estimated static axle weight signal.
The invention adopts the technical scheme and has the advantages that:
1. according to the invention, the speed of each axle when the vehicle passes through the dynamic axle weight scale is dynamically estimated according to the sampling points of the collected signals, and the effective sampling points are intercepted by a speed-based method to obtain effective axle weight signals, so that the intercepted effective axle weight signals can be ensured to be accurate and reliable.
2. Aiming at the conditions that the nonlinear fitting algorithm, such as the Levenberg-Marquardt fitting algorithm, has high requirements on a hardware platform and cannot meet the practical application, the method for extracting and processing the effective axle weight signal is adopted, so that the calculation precision is ensured, the calculation complexity is simplified, and the method can be applied to the conventional dynamic weighing system.
3. Aiming at the condition that the frequency of the low-frequency interference signal is uncertain, the method adopts a frequency-variable nonlinear fitting algorithm to fit the interference signal, can obtain an approximate low-frequency interference signal, and improves the weighing precision of the dynamic axle load scale due to the improvement of the accuracy of removing the low-frequency interference signal.
Drawings
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a vehicle dynamic axle weight weighing system of the present invention.
FIG. 2 is a flow chart of an axle weight estimation method of the dynamic vehicle weighing system of the present invention.
FIG. 3 is a waveform of an effective axle weight signal obtained after the invention is intercepted according to the axle speed.
FIG. 4 is a waveform diagram of an effective axle weight signal and a processed estimated static axle weight signal according to the present invention.
Detailed Description
When a vehicle passes through a weighing platform 1 of the dynamic axle weight scale along the direction shown in figure 1, whether the vehicle passes through is identified by the connection and disconnection of light curtains 2 on the two sides of the weighing platform 1, then the wheel axle is identified through a tire identifier 3, a ground induction coil 4 is used as a standby of the light curtain 2, four weighing sensors or more weighing sensors arranged below the weighing platform 1 collect axle weight data, a signal processing platform based on an RAM is adopted, a 51 chip is used as a signal control platform, and the two are communicated through a parallel port.
The axle weight estimation method of the vehicle dynamic weighing system of the invention is shown in figure 2, a weighing sensor arranged below a weighing platform 1 collects an original axle weight signal of each axle, the original axle weight signal comprises a static axle weight signal, a low-frequency interference signal and a high-frequency interference signal, the number of sampling points is counted, and a discrete mathematical model of the original axle weight signal can be as follows:
in the above formula, y (n) is a discrete mathematical model of the original axle weight signal, w is the static axle weight of the vehicle, A is the amplitude of the low-frequency interference signal, f is the frequency of the low-frequency interference signal, and FS is set for the weighing systemThe sampling frequency of (a) is determined,
is the phase of the low-frequency interference signal, AiIs the amplitude of the high-frequency interference signal, fiIs the frequency of the high-frequency interference signal,
iis the phase of the high frequency interference signal.
The initial axle weight signal is preprocessed by an FIR (finite Impulse response) or IIR (infinite impulse response) type low-pass filter, high-frequency interference signals contained in the initial axle weight signal are filtered, the high-frequency interference signals with the frequency being more than or equal to 50Hz are filtered by the filter, and at the moment, the axle weight signal basically only contains static axle weight and low-frequency interference signals.
Calculating the speed of the axle according to the number of sampling points, the set sampling frequency and the stroke of the vehicle passing through the dynamic axle weight scale; the mathematical expression for the axle speed is: v is FS (L + delta)/length, wherein v is the speed of each axle when the vehicle passes through the dynamic axle weight scale, FS is the sampling frequency, L + delta is the stroke of the dynamic axle weight scale up and down, L is the platform width of the dynamic axle weight scale, delta is the compensation coefficient, delta is between 0.2 and 0.8, and length is the number of sampling points.
Intercepting effective sampling points when a vehicle passes through the dynamic axle weight scale according to the calculated axle speed to obtain an effective axle weight signal, as shown in figure 3, which is a waveform diagram of the effective axle weight signal, wherein the collected axle weight signal comprises three sections including an upper dynamic axle weight scale section of the vehicle, an effective weighing section of the vehicle on the dynamic axle weight scale and a lower dynamic axle weight scale section of the vehicle, so that the effective sampling points can be obtained by removing the sampling points of the upper dynamic axle weight scale section and the lower dynamic axle weight scale section of the vehicle, the effective sampling points can be obtained by calculation, the mathematical expression of the effective sampling points is L2 length-2L1, wherein L2 is the effective sampling points, L1 is the sampling points of the upper dynamic axle weight scale section and the lower dynamic axle weight scale section of the vehicle, and the sampling points L1 of the upper dynamic axle weight scale section and the lower dynamic axle weight scale section of the vehicle are inversely proportional to the axle speed v, so that the effective axle weight signal can be obtained by calculation, the mathematical expression is L1 ═ FS × S/v, wherein S is the passing displacement of the upper dynamic axle weight scale section and the lower dynamic axle weight scale section of the vehicle, and the fluctuation part of the upper dynamic axle weight scale and the lower dynamic axle weight scale of the vehicle is removed, so that the accuracy of vehicle weight estimation can be improved.
Extracting 10-150 data points on the basis of the effective axle weight signal, in the sampling process, adopting the modes of moving average, simple extraction and the like, wherein the sampled effective axle weight signal forms an axle weight signal for simplifying calculation, and the mathematical expression of the axle weight signal is as follows:
x(n)＝w+A′*sin(2*pi*f*n*/FS′+
'), where x (n) is a discrete mathematical model, A ' is the amplitude of the decimated jammer signal, f is the frequency of the jammer signal, FS ' is the decimated sampling frequency,
the extracted phase can obtain effective axle weight signals with fewer points, so that the requirement that parameters required by nonlinear fitting are obtained by processing the effective axle weight signals by using the axle weight signals by using a nonlinear algorithm in embedded systems such as RAM (random access memory), DSP (digital signal processor) and the like can be met, the condition that four parameters, namely an axle weight initial value w, are required by fitting the model by using the nonlinear fitting algorithm can be known from a mathematical expression of the axle weight signals0Amplitude initial value A of low-frequency interference signal0Frequency initial value f of low-frequency interference signal0And phase initialization value of low frequency interference signal
0. Wherein the initial value w of the axle weight0Can be obtained by calculating the average value of the axle weight signal, and N is the number of data points after sampling, or the maximum value or the minimum value of the axle weight signal is adopted,
the mathematical expression is as follows: <math><mrow><msub><mi>w</mi><mn>0</mn></msub><mo>=</mo><mfenced open='{' close='' separators=' '><mtable><mtr><mtd><mfrac><mn>1</mn><mi>N</mi></mfrac><munderover><mi>Σ</mi><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>N</mi></munderover><mi>x</mi><mrow><mo>(</mo><mi>i</mi><mo>)</mo></mrow></mtd></mtr><mtr><mtd><mi>max</mi><mrow><mo>(</mo><mi>x</mi><mrow><mo>(</mo><mi>i</mi><mo>)</mo></mrow><mo>)</mo></mrow></mtd></mtr><mtr><mtd><mi>min</mi><mrow><mo>(</mo><mi>x</mi><mrow><mo>(</mo><mi>i</mi><mo>)</mo></mrow><mo>)</mo></mrow></mtd></mtr></mtable><mo>.</mo></mfenced></mrow></math>
and the amplitude initial value A of the low-frequency interference signal0Obtaining the weight by using half of the maximum value Max and the minimum value Min in the axle weight signals, wherein the numerical expression is as follows: and the frequency initial value f of the low-frequency interference signal0The frequency of the sampling frequency FS 'can be calculated by selecting 1 Hz-5 Hz or by using the maximum value coordinate Maxp, the minimum value coordinate Minp and the extracted sampling frequency FS' of the corresponding axle weight signal, and the mathematical expression is f0FS'/(Maxp-Minp |), the maximum value and the minimum value can be ensured within a certain range, and thus a range satisfying the frequency of the low-frequency interference signal can be ensured. Phase initialization value of low frequency interference signal 00.5-2 can be selected, or the first extreme point coordinate pp corresponding to the axle weight signal and the initial value f of the low-frequency interference signal frequency can be used0Calculating the extracted sampling frequency FS ', and converting the sampling frequency FS' into a range of 0-2 pi, wherein the mathematical expression is as follows:obtaining the initial phase value of the low-frequency interference signal
0Obtaining approximate low-frequency interference signal by using nonlinear fitting algorithmThe mathematical expression for approximating a low frequency interference signal is:wherein,
to fit the resulting amplitude of the glitch,for fitting the obtained frequency of the low-frequency interference
The phase obtained by fitting.
Finally, the approximate low-frequency interference signal is subtracted from the effective axle weight signal or the axle weight signal to obtain an estimated static axle weight signal, and as can be seen from the waveform diagram in fig. 4, the waveform of the estimated static axle weight signal is smoother than that of the axle weight signal.
The axle weight signal acquired by the axle weight estimation method of the vehicle dynamic weighing system is processed, the vehicle speed is less than or equal to 20km/h, the axle total weight estimation error is controlled within +/-2.5 percent, and the weighing precision of the dynamic axle weight scale is improved.
Claims (5)
1. An axle weight estimation method of a vehicle dynamic weighing system is characterized in that:
(1) when the vehicle passes through the dynamic axle weight scale, the weighing sensor collects original axle weight signals of each axle, the original axle weight signals comprise static axle weight signals, low-frequency interference signals and high-frequency interference signals, and the number of sampling points is counted;
(2) filtering out high-frequency interference signals with the frequency being more than or equal to 50 Hz;
(3) calculating the speed of the axle according to the number of sampling points, the set sampling frequency and the stroke of the vehicle passing through the dynamic axle weight scale; the mathematical expression for the axle speed is: v is FS (L + Δ)/length, where v is the speed of each axle as the vehicle passes through the dynamic axle weight scale; FS is sampling frequency; l is the weighing platform width of the dynamic axle load scale; delta is a compensation coefficient, delta is between 0.2 and 0.8, and length is the number of sampling points;
(4) effective sampling points when the vehicle passes through the dynamic axle load scale are intercepted according to the axle speed, effective axle load signals are obtained, and the mathematical expression of the effective sampling points is as follows: l2 ═ length-2L1, where L2 is the number of valid sampling points, length is the number of sampling points, L1 is the number of sampling points of the dynamic axle weight scale section on the vehicle and the dynamic axle weight scale section below, and the mathematical expression of the number of sampling points is: l1 ═ FS × S/v, where S is the amount of displacement that the upper dynamic axle weight scale section and the lower dynamic axle weight scale section of the vehicle pass through, and v is the speed of each axle when the vehicle passes through the dynamic axle weight scales; FS is sampling frequency;
(5) extracting 10-150 data points on the basis of the effective axle weight signal to form an axle weight signal for simplifying calculation;
(6) obtaining parameters required by nonlinear fitting by using the axle weight signal, and obtaining an approximate low-frequency interference signal by using a nonlinear fitting algorithm;
(7) and subtracting the approximate low-frequency interference signal from the effective axle weight signal or the axle weight signal to obtain an estimated static axle weight signal.
2. The axle weight estimation method of a vehicle dynamic weighing system according to claim 1, characterized in that: the parameters of the nonlinear fitting comprise an initial value w of the axle weight0Amplitude initial value A of low-frequency interference signal0Frequency initial value f of low-frequency interference signal0And phase initialization value of low frequency interference signal
3. The axle weight estimation method of a vehicle dynamic weighing system according to claim 2, characterized in that: the initial value w of the axle weight0By calculating the average value and the maximum value of the axle weight signalOr minimum value, the mathematical expression of which is:
4. The axle weight estimation method of a vehicle dynamic weighing system according to claim 2, characterized in that: the initial value A of the amplitude of the low-frequency interference signal0Is half of the difference between the maximum value max (x (i)) and the minimum value min (x (i)) in the axle weight signal, and the numerical expression is
5. The axle weight estimation method of a vehicle dynamic weighing system according to claim 2, characterized in that: frequency initial value f of the low-frequency interference signal0The value range is 1 Hz-5 Hz, or the value range is obtained by calculating the maximum value coordinate Maxp, the minimum value coordinate Minp and the extracted sampling frequency FS' of the corresponding axle weight signal, and the mathematical expression is f0＝FS′/(|Maxp-Minp|)。
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| 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 |
| CN105352576B (en) * | 2015-11-27 | 2018-09-25 | 太原磅管家科技有限公司 | A kind of vehicle separating 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 |
| 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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| 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 |
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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. |