Self-adaptive filtering method of dynamic axle weighing signal of vehicle

Abstract

The invention relates to a self-adapting method for filtering vehicle dynamic shaft load signal, aiming to effectively eliminate weighing error due to vehicle dynamic load and remarkably improve the vehicle weighing accuracy. The invention processes the original shaft load signal by zero phase offset and low-pass filtering, so as to eliminate white noise interference and inhibit phase distortion of the shaft load signal during low-pass filtering process. The invention also adopts optimized cut-off to the original weighing signal by searching average value of extreme points of shaft load signal, so as to better inhibit gravitation impulse interference in the weighing signal. Further, the invention decomposes the weighing signal by empirical mode decomposition method, meanwhile, adopts end point continuation method in empirical mode decomposition, so as to eliminate end effect, effectively inhibit the generation of false mode and make the decomposed result relatively true. The invention eliminates low-frequency interference through empirical mode decomposition, so as to obtain true shaft load signal.

Description

Technical field [0001] The present invention relates to a method of processing a vehicle dynamic weighing signal, in particular a self-adaptive filtering method of a vehicle dynamic axis weight signal. Background technique [0002] The highway vehicle dynamically weighs, which is weighing in the car, that is, the vehicle is by weighing platform at a certain speed, and weigh the weight of the weight of the vehicle, and then the weight is added, and finally the vehicle weight is obtained. . Dynamic weighing is compared with the parking static weighing, has the advantages of fast, efficient, and does not affect traffic, but the maximum disadvantage is the accuracy of weighing. This is because the power of the load cell is inevitably subject to ground unevenness, vehicle itself vibration, scale platform vibration, tire driving force, etc., these factors form additional dynamic loads on the sensor, causing Heavy error. According to the test analysis, the vibration frequency of the dyn...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, the domestic methods for processing the dynamic weighing signal of the vehicle are: multi-point mean filtering, sliding multi-point mean filtering and low-pass filtering, etc., but these methods can only remove some high-frequency interference and cannot Remove the low-frequency vibration interference caused by the dynamic load of the vehicle, which causes large weighing errors

The main reason why it cannot be removed is that the amplitude, phase and frequency of the low-frequency interference change randomly, and the above-mentioned digital filtering or low-pass filtering cannot be changed according to this change. From this perspective, it is not a Adaptive signal processing method, therefore, it is difficult to effectively filter out the low frequency interference

In addition, if the speed of the vehicle passing the weighing platform exceeds the rated value specified by the system (for example: greater than 20km / h), it means that the weighing signal collected by the system is compressed on the time axis. At this time, the intercepted weighing signal The dynamic load low-frequency interference signal in the interval is less than one period. This interference signal is called short-course low-frequency interference, which cannot meet the above-mentioned requirements of the mean filter for the full period of the signal.

But this method can only be a kind of train of thought, still very imperfect, the defect that still exists at present is difficult to overcome: the one, it intercepts the weighing signal with the maximum value point interception method, it is easy to divide the upper weighing platform and the lower weighing platform The gravitational impact signal is cut into the effective weighing signal, resulting in errors; second, the real cause of the false mode has not been found, so it uses the measure of adding the last few eigenmode functions and residuals to eliminate False modes, on the contrary, will add the low-frequency vibration interference mode that should be eliminated to the real weighing signal, and cause unnecessary weighing errors, which makes it difficult to reflect the advantages of this method

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