Research on Dynamic Load Characteristics of Advanced Variable Speed Drive System for Agricultural Machinery during Engagement

Cheng, Zhun; Lu, Zhixiong

doi:10.3390/agriculture12020161

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…The distribution functions of the exceeding amount and exceeding threshold are shown in Equations ( 1) and ( 2), respectively: Due to the harsh working conditions in the field, there are some abnormal peaks in the lines drawn after dispersion due to external interference or human error during the tractor field test. In order to obtain true and reliable load data, outlier of the original signal should be detected and smoothed before spectrum compilation, and the abnormal peaks should be eliminated [25,26], and the results are shown in Figure 5.…”

Section: Determine the Distribution Functionmentioning

confidence: 99%

Extrapolation of Tractor Traction Resistance Load Spectrum and Compilation of Loading Spectrum Based on Optimal Threshold Selection Using a Genetic Algorithm

et al. 2023

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To obtain the load spectrum of the traction resistance of the three-point suspension device under tractor-plowing conditions, a load spectrum extrapolation method based on a genetic algorithm optimal threshold selection is proposed. This article first uses a pin force sensor to measure the plowing resistance of the tractor’s three-point suspension device under plowing conditions and preprocesses the collected load signal. Next, a genetic algorithm is introduced to select the threshold based on the Peak Over Threshold (POT) extremum extrapolation model. The Generalized Pareto Distribution (GPD) fits the extreme load distribution that exceeds the threshold range, generating new extreme points that follow the GPD distribution to replace the extreme points in the original data, achieving the extrapolation of the load spectrum. Finally, the loading spectrum that can be achieved on the test bench is obtained based on the miner fatigue theory and accelerated life theory. The results show that the upper threshold of the time-domain load data obtained by the genetic algorithm is 10.975 kN, and the grey correlation degree is 0.7249. The optimal lower threshold is 8.5455 kN, the grey correlation degree is 0.7722, and the fitting effect of the GPD distribution is good. The plowing operation was divided into five stages: plowing tool insertion, acceleration operation, constant speed operation, deceleration operation, and plowing tool extraction. A traction resistance loading spectrum that can be achieved on the test bench was developed. The load spectrum extrapolation method based on the genetic algorithm optimal threshold selection can improve the accuracy of threshold selection and achieve the extrapolation and reconstruction of the load spectrum. After processing the extrapolated load spectrum, it can be transformed into a load spectrum that can be recognized by the test bench.

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Section: Determine the Distribution Functionmentioning

confidence: 99%

Extrapolation of Tractor Traction Resistance Load Spectrum and Compilation of Loading Spectrum Based on Optimal Threshold Selection Using a Genetic Algorithm

et al. 2023

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“…Additionally, the adoption of advanced agricultural machinery and equipment has improved labor productivity, reducing the reliance on manual cultivation [48,49]. Furthermore, with the widespread dissemination of agricultural technology, farmers can increasingly rely on diversified agricultural production to meet various demands.…”

Section: Theoretical Mechanismsmentioning

confidence: 99%

Why Have China’s Poverty Eradication Policy Resulted in the Decline of Arable Land in Poverty-Stricken Areas?

Ran,

Hua,

et al. 2023

Land

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Arable land resources are the basic livelihood security for people in poverty-stricken areas, and poor people are prone to uncontrolled expansion of arable land and exogenous ecological damage to secure their livelihoods. To avoid this vicious cycle, China’s poverty eradication policy requires greater management and restoration of arable land in poverty-stricken areas, but it is unknown what impacts it may bring. Therefore, this study examines the impact of policy implementation on arable land by the Difference-in-Differences (DID) model and uses the mediating and moderating models to test the policy’s mechanism on arable land. The results reveal that the policy significantly reduces the arable land, and the results remain robust, controlling for potential endogeneity variables and robustness tests. What’s more, the results of the mediating and moderating effect models indicate that the reduction of arable land is mainly due to the increase of unit yield, guiding the local people to abandon degraded land and to carry out ecological restoration, thus reducing the arable land area and achieving sustainable development. Finally, recommendations are proposed from the perspective of human-land coordination.

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“…The calibration of sensors is a necessary preparation step before experiments [ 31 , 32 ]. Before the experiments were conducted, four microphones inserted in the impedance tube were calibrated using a pistonphone and calibration box.…”

Section: Experimental Studymentioning

confidence: 99%

Influence of Magnetic Field on Sound Transmission Loss of the Unit Filled with Magnetorheological Fluid

Wang

2022

Materials

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To explore the feasibility of applying magnetorheological fluids (MRFs) in the field of noise control, the influence of the magnetic field intensity and direction on the sound transmission loss (STL) of a unit filled with MRF (MRF unit) were investigated in this study. First, two types of test sample containing the MRF unit were designed and fabricated. The magnetic field applied to the MRF was provided by the permanent magnets used in pairs. The direction of the magnetic field was perpendicular or parallel to the direction of the sound wave propagation. The distribution of the magnetic field intensity of the two types of test samples was simulated using magnetostatic finite element analysis and validated with the magnetic field intensity measured using a Teslameter. For comparison, test samples containing air and water units were also prepared. Then, the STL of the two types of test samples were measured under different magnetic field intensities using the impedance tube method. Finally, the STL curves of the two types of test samples were presented, and the influence of magnetic field intensity and direction on the STL were discussed. The results demonstrate that the magnetic field direction has a significant influence on the STL of the MRF unit. In addition, when the magnetic field direction is parallel to the sound propagation direction, the STL of the test sample containing MRF unit significantly increases with the increase of the magnetic field intensity at low and middle frequencies.

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Research on Dynamic Load Characteristics of Advanced Variable Speed Drive System for Agricultural Machinery during Engagement

Cited by 10 publications

References 18 publications

Extrapolation of Tractor Traction Resistance Load Spectrum and Compilation of Loading Spectrum Based on Optimal Threshold Selection Using a Genetic Algorithm

Extrapolation of Tractor Traction Resistance Load Spectrum and Compilation of Loading Spectrum Based on Optimal Threshold Selection Using a Genetic Algorithm

Why Have China’s Poverty Eradication Policy Resulted in the Decline of Arable Land in Poverty-Stricken Areas?

Influence of Magnetic Field on Sound Transmission Loss of the Unit Filled with Magnetorheological Fluid

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