A method of mine car load testing recognition

Abstract

The application provides a mine car load test identification method, and belongs to the technical field of vehicle safety. The method comprises the following steps: S1, a plurality of one-way strain gauges are adopted, and a quarter bridge or half bridge or full bridge circuit is adopted, without adopting a strain rosette, without considering various compensations, temperature drift and zero drift, and dynamic load is collected in real time; S2, strain signal processing; S3, based on the strain signal processing, only the dynamic alternating component of the strain signal is reserved, and the dynamic component of the mine car load is identified; and S4, a static or quasi-static balanced load is superimposed, and a complete load spectrum of the mine car is obtained. Through the extremely simple test method and a series of scientific and reasonable processing and analysis on the test results, noise interference and various drifts can be effectively removed, error deviation can be corrected, and useful information hidden in original data can be fully mined. Through the test and data processing process, the finally identified load spectrum of the mine car can truly reflect the actual load state of the mine car.

Description

Technical Field

[0001] This invention provides a method for testing and identifying the load on a mining truck, belonging to the field of vehicle safety technology. Background Technology

[0002] As a key piece of equipment in mine transportation, the real-time load status of mining cars directly affects their operational safety, service life, and transportation efficiency. Accurate identification of mining car loads is of paramount importance for vehicle performance evaluation, safety monitoring and early warning, transportation efficiency optimization, and mine production management. However, traditional methods for identifying mining car loads have many limitations in practical applications.

[0003] Strain measurement, or stress measurement, is fundamental to mine car load identification. Strain gauges, as high-precision sensors, have been widely used to measure strain signals from mine cars. However, strain signals often contain interference components such as noise, temperature drift, zero drift, and burrs. Furthermore, traditional methods have shortcomings in signal filtering, zero drift processing, and the comprehensiveness of load identification. For example, bandpass filtering can remove low-frequency static components, leading to data distortion; complete zero drift removal or simplistic zeroing of the centerline can incorrectly remove static components, affecting the accuracy of load identification.

[0004] In recent years, although researchers have made many improvements in optimizing signal filtering strategies, improving zero-drift processing methods, integrating multiple software tools, and introducing simulation technology, the limitations of traditional methods have not been completely resolved.

[0005] The dynamic load on the rear axle of the mining truck must be tested in real time, and cannot be calculated using the ADAMS vehicle model. This is because mining trucks use large tires with a diameter of about 3 meters, such as 50 / 80R57. There is no tire dynamics test bench or test results available for traditional passenger cars, and there is no model of the mining truck tires. Therefore, it is impossible to perform ADAMS simulation of the mining truck's road driving. Thus, only real-time load testing is possible.

[0006] Real-time load testing typically uses strain testing. Strain testing requires compensation, such as bending stress compensation, axial stress compensation, and especially temperature compensation. These compensations increase the complexity and workload of strain testing, and may even be impossible to implement due to structural or space limitations. For example, bending stress compensation in axial stress testing requires two strain gauges to be arranged on two symmetrical sides of the tension member, while axial stress compensation in bending stress testing requires four strain gauges to be arranged on two symmetrical sides of the bending member. These compensations are often impossible to implement due to structural or space limitations.

[0007] Strain testing requires compensation, but adding compensation makes strain testing more cumbersome or even impossible. Conducting long-term tests directly without compensation results in severe temperature drift and zero drift in the strain curves. This temperature drift and zero drift occur because the slowly varying components of the strain signal approach the static components, causing an overall shift in the test results and affecting the accuracy and effectiveness of subsequent load identification. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a method for identifying mine car loads. By employing a simplified testing approach supplemented by a series of scientifically sound processing and analysis of the test results, it effectively removes noise interference and various drifts, corrects error deviations, and fully extracts useful information hidden within the original data. Through this processing, the identified mine car load is not only more reasonable but also accurately reflects the actual load state of the mine car. This method has the following characteristics:

[0009] 1. Simple testing: The test uses a quarter-bridge, half-bridge, or full-bridge circuit with multiple uniaxial strain gauges, eliminating the need for strain rosettes and cumbersome compensation techniques such as temperature compensation, bending stress compensation for axial stress testing, and axial stress compensation for bending stress testing.

[0010] 2. Simple data processing: Low-pass filtering is used to retain the static components and the low-frequency components of interest. Then, the neutral line is set to zero to completely remove temperature drift, zero drift and DC components, leaving only the low-frequency AC components of interest.

[0011] 3. Load AC component identification: Identify load AC components based on retaining only the low-frequency AC components of interest;

[0012] 4. Static Load Compensation: The static load component is extracted using ADAMS whole-vehicle model simulation technology. Combined with typical mining truck driving conditions such as downhill with no load or full load, uphill with full load or no load, and flat road driving with no load and full load, the static DC component of the load is superimposed on the AC component of the load based on the uphill and downhill sections displayed by GPS signals, thereby obtaining the dynamic load spectrum of the entire working condition. This overcomes the shortcomings of traditional methods and achieves more accurate, comprehensive and realistic full-condition load identification of mining trucks in a simplified way, providing a brand-new solution for the performance evaluation, safety monitoring and optimization design of mining trucks.

[0013] 5. In the field of mine car load identification, traditional methods based on strain gauge measurements have an inherent and insurmountable limitation: even under ideal conditions (e.g., using high-precision strain gauges, effectively suppressing system zero drift, and reducing environmental noise interference to an extremely low level), the measurement results essentially only reflect the static load caused by the ore, the static or quasi-static load caused by axle load transfer due to uphill and downhill slopes, and the static and dynamic strain caused by the dynamic load of the entire vehicle's movement. In other words, traditional methods cannot directly and continuously acquire the static strain signal caused by the total weight of the entire vehicle and the ore, including the mine car's own weight.

[0014] The fundamental reason is that strain gauges measure the localized micro-strain that occurs in the mine car structure under load. When the mine car is unloaded and stationary, the car body structure already bears the stress generated by its own weight and the weight of auxiliary equipment. At this time, the reference output value of the strain gauge is zero. During subsequent loading and transportation of ore, the strain gauge cannot reflect the static load component caused by the mine car's own weight. Thus, traditional strain measurement systems can usually only effectively capture the dynamically changing parts, failing to effectively correlate, separate, and integrate the initial static strain component representing the total vehicle weight with the captured dynamic strain component. Therefore, its output signal loses the static strain information caused by the total vehicle weight, making load identification, subsequent vehicle performance evaluation, safety monitoring and early warning, transportation efficiency optimization, and mine production management based on this information absolutely unacceptable.

[0015] The core innovation of this invention lies in successfully solving the critical problem of "missing absolute total weight signal". By introducing the multibody dynamics simulation software ADAMS, the static stress field of the car body structure under combined loads is calculated based on a high-precision physical model, which can accurately identify and separate the static components in the measurement signal (mainly including the relatively constant load effects such as the weight of the mining car and the ore).

[0016] The signal output by the method of this invention is no longer a simple change in ore weight, but a complete, accurate, and continuous load change signal of the entire vehicle and the total weight of the ore. This signal not only contains real-time dynamic information on the ore loading, but also firmly anchors the absolute benchmark of the vehicle's own weight, thus providing an unprecedented, complete, and reliable load data foundation for key applications such as accurate weighing, safety monitoring, and efficiency statistics of mining trucks.

[0017] The specific technical solution is as follows:

[0018] A method for identifying the load of a mining truck during testing includes the following steps:

[0019] S1, Strain Test

[0020] It employs multiple uniaxial strain gauges and uses a quarter-bridge, half-bridge, or full-bridge circuit, eliminating the need for strain rosettes and various compensation, temperature drift, and zero drift considerations, and enabling real-time acquisition of dynamic loads.

[0021] S2, Strain Signal Processing

[0022] Low-pass filtering: The original strain signal is processed using a 50Hz low-pass filter;

[0023] Complete removal of temperature drift and zero drift: The low-pass filtered signal is completely de-drifted, so that all signals fluctuate up and down based on the Y=0 straight line, and only the dynamic alternating component of the strain signal is retained.

[0024] Deburring: Removes outliers in the signal, reduces the adverse effects of abnormal spikes and other glitches on load recognition, and improves the smoothness and reliability of the signal.

[0025] S3, Dynamic Load Identification

[0026] Based on strain signals that retain only dynamic alternating components after strain signal processing, the dynamic components of the mine car load are identified.

[0027] S4. Superimpose static or quasi-static equilibrium loads to obtain the complete load spectrum of the mine car.

[0028] Acquisition of static or quasi-static equilibrium loads: A complete mining truck model is built using ADAMS software; static equilibrium simulation analysis based on the ADAMS model yields static equilibrium loads for typical mining conditions such as unloaded uphill and downhill driving, unloaded and fully loaded horizontal driving, and fully loaded uphill and downhill driving; then, based on the uphill and downhill sections displayed by GPS signals, the static equilibrium loads of the corresponding conditions are superimposed onto the dynamic loads of the identified corresponding sections to reconstruct the complete load signal, ultimately obtaining a mining truck load spectrum that includes both dynamic and static or quasi-static equilibrium load components.

[0029] The technical effects of the present invention are as follows:

[0030] (1) Strain testing is simple

[0031] The test is simple and is not afraid of drift: it uses multiple uniaxial strain gauges and a quarter-bridge, half-bridge or full-bridge circuit, without the need for strain rosettes, various compensations, temperature drift and zero drift, and real-time acquisition of dynamic loads.

[0032] Compensated strain testing, such as bending stress compensation, axial stress compensation, and especially temperature compensation, increases the complexity and workload of strain testing. In some cases, compensation cannot be achieved due to structural or spatial limitations. For example, bending stress compensation in axial stress testing requires two strain gauges to be arranged on two symmetrical sides of the tension member, while axial stress compensation in bending stress testing requires four strain gauges to be arranged on two symmetrical sides of the bending member. These compensations are often impossible to implement due to structural or spatial limitations.

[0033] (2) Ensure data authenticity

[0034] Low-pass filtering fully preserves the low-frequency dynamic AC components of interest in the strain signal, ensuring that the identified load signal accurately reflects the actual dynamic alternating load of the mine car, providing a reliable basis for subsequent vehicle analysis.

[0035] Traditional bandpass filtering methods, due to different low-frequency cutoff frequencies such as 0.3, 0.5, 0.7, and 1.0 Hz, gradually retain too much or too little quasi-static component, causing uncertainty in the retention or removal of static or quasi-static components such as sensor zero drift, temperature drift, and shaft load transfer under uphill and downhill conditions, resulting in load distortion.

[0036] (3) Comprehensive improvement

[0037] By integrating dynamic and static components, a complete load spectrum of the mining truck is obtained, providing more comprehensive data support for in-depth research on the mechanical performance of the mining truck under different working conditions.

[0038] It overcomes the one-sidedness and limitations of the past, which only focused on dynamic changes or only on static components.

[0039] (4) Innovative integration

[0040] This innovative approach combines the strengths of various professional software such as Ncode, MATLAB, and ADAMS, allowing each software to perform its specific function while working closely together. This results in a simple, efficient, reliable, and accurate method and process for identifying and testing loads on mining trucks. It differs from single-software analysis or single-strain testing methods, providing an integrated and innovative approach and methodology for solving similar engineering problems. Attached Figure Description

[0041] Figure 1 Comparison of X-axis strain of the rear longitudinal arm before and after 50Hz low-pass filtering in an embodiment (front: solid red line; rear: dashed black line);

[0042] Figure 2 A comparison of the X-axis strain before and after zero drift removal of the rear longitudinal arm in the embodiment (front: solid red line; back: dashed black line);

[0043] Figure 3Comparison of X-axis strain deburring of the rear longitudinal arm before and after the embodiment (front: solid red line; rear: dashed black line);

[0044] Figure 4 A comparison of the rear longitudinal arm Z-axis strain before and after 50Hz low-pass filtering in an embodiment (front: solid red line; rear: dashed black line);

[0045] Figure 5 A comparison of the Z-axis strain before and after zero drift removal of the rear longitudinal arm in the example (front: solid red line; back: dashed black line);

[0046] Figure 6 Comparison of Z-direction strain deburring of the rear longitudinal arm before and after the example (front: solid red line; rear: dashed black line);

[0047] Figure 7 f, as an example 4,x -X-direction strain;

[0048] Figure 8 f, as an example 5,z -X-direction strain;

[0049] Figure 9 f, as an example 4,x -Z-direction strain;

[0050] Figure 10 f, as an example 5,z -Z-direction strain;

[0051] Figure 11 f, as an example 4,x Load;

[0052] Figure 12 f, as an example 5,z Load;

[0053] Figure 13 Force (dynamic component) of the longitudinal arm in the Z direction after a fully loaded downhill slope;

[0054] Figure 14 The force in the Z-direction of the longitudinal arm after a fully loaded downhill section. Detailed Implementation

[0055] The technical solution of the present invention will be described with reference to specific embodiments.

[0056] This embodiment takes the Z-axis load of the rear longitudinal arm as an example when the mine car is fully loaded and going downhill.

[0057] (1) Strain signal processing:

[0058] Rear trailing arm X-axis strain data:

[0059] ①50Hz low-pass filter, such as Figure 1 .

[0060] ② Remove zero drift, such as Figure 2 .

[0061] ③ Remove burrs, such as Figure 3 .

[0062] Z-axis strain data of the rear trailing arm:

[0063] ①50Hz low-pass filter, such as Figure 4 .

[0064] ② Remove zero drift, such as Figure 5 .

[0065] ③ Remove burrs, such as Figure 6 .

[0066] (2) Dynamic load identification:

[0067] like Figures 7 to 12 Fix the left and right shaft ends, and apply f4 = f to the hinge hole in sequence. 4,x f5 = f 5,z Load: Extract strain in a specific direction at the location of the two experimental patches to obtain the unit load strain matrix required for hinge load identification.

[0068] A e-f,b (1:2,1:2)=[19.992,36.589;

[0069] -3.2382,5.7679]*1.0e-11.

[0070] Rear trailing arm load identification and calculation process in the X and Z directions:

[0071] During testing, the patch strains e4 and e5 were obtained, and the load expression was identified:

[0072] [F4,F5] T =A e-f,b -1 ·[e4,e5] T

[0073] Import this formula into MATLAB and perform calculations with the previously processed strain signal to obtain the dynamic component of the load, such as... Figure 13 As shown.

[0074] (3) Obtain the static load:

[0075] Simulation results from the ADAMS model show that when the mine car is fully loaded and going downhill (2247s-2400s), the static component of the rear longitudinal arm in the Z direction is 257.1 tons.

[0076] (4) Dynamic loads and static loads are superimposed to obtain a complete load signal, such as... Figure 14 .

Claims

1. A method for testing and identifying the load spectrum of a mining car, characterized in that, Includes the following steps: S1, Strain Test It employs multiple uniaxial strain gauges and uses a quarter-bridge, half-bridge, or full-bridge circuit, eliminating the need for strain rosettes, various compensations, temperature drift, and zero drift, and enabling real-time acquisition of dynamic loads. S2, Strain Signal Processing This includes using low-pass filtering, complete zero-drift removal, and glitch removal. The specific methods for strain signal processing are as follows: Low-pass filtering: The original strain signal is processed using a 50Hz low-pass filter; Complete zero drift removal: The low-pass filtered signal is subjected to complete zero drift removal, so that all signals fluctuate up and down based on the straight line Y=0, and only the dynamic alternating component of the strain signal is retained. Deburring: Removes outliers in the signal, reduces the adverse effects of abnormal spikes and glitches on load identification, and improves the smoothness and reliability of the signal; S3, Dynamic Load Identification Based on strain signals that retain only dynamic alternating components after strain signal processing, the dynamic components of the mine car load are identified. S4. Superimpose static or quasi-static equilibrium loads to obtain the complete load spectrum of the mine car; Alternatively, the method for obtaining quasi-static equilibrium loads is as follows: A complete mining truck model was built using ADAMS software. Based on the static balance simulation analysis of the ADAMS complete vehicle model, the static balance loads of typical mining area working conditions such as unloaded uphill and downhill driving, unloaded and fully loaded horizontal road driving, and fully loaded uphill and downhill driving were obtained. Then, based on the uphill and downhill sections displayed by GPS signals, the static balance load of the corresponding working conditions is superimposed on the dynamic load of the identified corresponding section to reconstruct the complete load signal, and finally the mine car load spectrum containing both dynamic components and static or quasi-static balance load components is obtained.

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