Research Progress on the Wear Resistance of Key Components in Agricultural Machinery

Wang, Ying; Li, Dong; Nie, Cheng; Gong, Pan; Yang, Junsheng; Hu, Zhigang; Li, Bin; Ma, Ming

doi:10.3390/ma16247646

Cited by 3 publications

(2 citation statements)

References 74 publications

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“…This can also help standardizing pesticide use and improve its efficiency of use. ASS can effectively solve the problems of lack of family labor and insufficient supply of technology, promote the efficient allocation of agricultural resources, and improve the productivity of agricultural technology 46 . Meanwhile, agricultural machinery and equipment can be leased to farmers or contracted for pest control, effectively reducing the amount of pesticides through precise application of medicines, and united distribution and control, thus saving labor costs.…”

Section: Discussionmentioning

confidence: 99%

The empirical effect of agricultural social services on pesticide inputs

Na,

Yan,

Xing

et al. 2024

Sci Rep

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Agricultural social services (ASS) play an important role in improving the efficiency of agricultural operations, reducing agricultural production costs, and promoting sustainable agricultural development. Using data from the 2020 China Rural Revitalization Survey, this study analyzes the impact of ASS on reducing pesticide inputs. The results show: (1) ASS play a significantly positive role in reducing pesticide inputs. (2) Heterogeneity analyses show that ASS’ role in reducing pesticide inputs is stronger for farming households with small farms, which participate in cooperatives, and do not have members involved in non-farm employment than that for farming households with large farms, which do not participate in cooperatives, and have members involved in non-farm employment. (3) Mechanism analysis shows that ASS’ green perception and demonstration-led effects contribute to reducing pesticide inputs by 148.6% and 36.8%, respectively, at the 1% level. Finally, this study proposes relevant policy recommendations for promoting ASS, promoting the continuous operation of farmland, and encouraging farmers to participate in ASS.

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Section: Discussionmentioning

confidence: 99%

The empirical effect of agricultural social services on pesticide inputs

Na,

Yan,

Xing

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…Blades are the direct components that perform operational functions in various agricultural machines, such as plows, trenchers, agitators, mud spreaders, ridgers, weeders, and field management tools, playing a pivotal role in determining the operational performance of these implements [5][6][7][8][9][10][11]. Scholars have extensively researched the operational performance of blades used in agricultural machinery in soil environments.…”

Section: Introductionmentioning

confidence: 99%

Simulation Analysis and Optimization Design of Paddy Field Mud Spreader Blades for Uniform Dispersion

Ren,

Chen,

Bao

et al. 2024

Agriculture

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To improve the distribution of mud particles collected in the tray during the operation of paddy field mud spreader blades, the optimal combination of parameters for the blades that results in the best uniformity of mud dispersion needs to be identified. In this study, a thorough force analysis was conducted on the spreading process, and computational equations were formulated to describe the motion of mud particles. By utilizing the discrete element simulation technique, a simulation model was developed to accurately represent the intricate interaction between the blades and mud particles. Through the single-factor simulation experiments, the ranges of key parameters such as the rotation radius, bending angle, sub-blade tilt angle, forward velocity, and rotational speed of the blade were determined. A secondary orthogonal rotational combination design was employed to establish a regression prediction model between the non-uniformity of mud dispersion and the key blade parameters. Subsequently, a multivariate single-objective optimization method was used to develop an optimization model for the non-uniformity of mud dispersion. The results indicate that the hierarchical order of factors influencing the non-uniformity of mud dispersion is as follows: rotation radius > rotation speed > bending angle > forward velocity > sub-blade tilt angle. To achieve a minimum spreading non-uniformity of 29.63%, a specific configuration is required, which includes a blade rotation radius of 188 mm, a bending angle of 121°, a sub-blade tilt angle of 30°, a forward velocity of 400 mm/s, and a rotation speed of 191 r/min. Finally, the accuracy of the optimization results was verified by means of bench tests. The research results provide a crucial reference for enhancing the uniformity of mud dispersion in paddy field mud spreader blades.

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Rolling Bearing Fault Diagnosis in Agricultural Machinery Based on Multi-Source Locally Adaptive Graph Convolution

Xie,

Sun,

Wang

et al. 2024

Agriculture

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Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically, diagnosing faults in rolling bearings, which are essential rotating components, is of significant importance. Domain-adaptive technology often addresses the challenge of limited labeled data from a single source domain. However, information transfer can sometimes fall short in providing adequate relevant details for supporting target diagnosis tasks, leading to poor recognition performance. This paper introduces a novel fault diagnosis model based on a multi-source locally adaptive graph convolution network to diagnose rolling bearing faults in agricultural machinery. The model initially employs an overlapping sampling method to enhance sample data. Recognizing that two-dimensional time–frequency signals possess richer spatial characteristics in neural networks, wavelet transform is used to convert time series samples into time–frequency graph samples before feeding them into the feature network. This approach constructs a sample data pair from both source and target domains. Furthermore, a feature extraction network is developed by integrating the strengths of deep residual networks and graph convolutional networks, enabling the model to better learn invariant features across domains. The locally adaptive method aids the model in more effectively aligning features from the source and target domains. The model incorporates a Softmax layer as the bearing state classifier, which is set up after the graph convolutional network layer, and outputs bearing state recognition results upon reaching a set number of iterations. The proposed method’s effectiveness was validated using a bearing dataset from Jiangnan University. For three different groups of bearing fault diagnosis tasks under varying working conditions, the proposed method achieved recognition accuracies above 99%, with an improvement of 0.30%-4.33% compared to single-source domain diagnosis models. Comparative results indicate that the proposed method can effectively identify bearing states even without target domain labels, showcasing its practical engineering application value.

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Research Progress on the Wear Resistance of Key Components in Agricultural Machinery

Cited by 3 publications

References 74 publications

The empirical effect of agricultural social services on pesticide inputs

The empirical effect of agricultural social services on pesticide inputs

Simulation Analysis and Optimization Design of Paddy Field Mud Spreader Blades for Uniform Dispersion

Rolling Bearing Fault Diagnosis in Agricultural Machinery Based on Multi-Source Locally Adaptive Graph Convolution

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