IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i6p1133-d1157885.html
   My bibliography  Save this article

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

Author

Listed:
  • Meng Yang

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Xiaoxu Sun

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Xiaoting Deng

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Zhixiong Lu

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Tao Wang

    (College of Emergency Management, Nanjing Tech University, Nanjing 210009, China)

Abstract

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.

Suggested Citation

  • Meng Yang & Xiaoxu Sun & Xiaoting Deng & Zhixiong Lu & Tao Wang, 2023. "Extrapolation of Tractor Traction Resistance Load Spectrum and Compilation of Loading Spectrum Based on Optimal Threshold Selection Using a Genetic Algorithm," Agriculture, MDPI, vol. 13(6), pages 1-20, May.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1133-:d:1157885
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/6/1133/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/6/1133/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhun Cheng & Zhixiong Lu, 2022. "Research on Dynamic Load Characteristics of Advanced Variable Speed Drive System for Agricultural Machinery during Engagement," Agriculture, MDPI, vol. 12(2), pages 1-13, January.
    2. Zhun Cheng & Zhixiong Lu, 2021. "Research on Load Disturbance Based Variable Speed PID Control and a Novel Denoising Method Based Effect Evaluation of HST for Agricultural Machinery," Agriculture, MDPI, vol. 11(10), pages 1-18, October.
    3. Nils-Bastian Heidenreich & Anja Schindler & Stefan Sperlich, 2013. "Bandwidth selection for kernel density estimation: a review of fully automatic selectors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 403-433, October.
    4. Christoph Marty & Juliette Blanchet, 2012. "Long-term changes in annual maximum snow depth and snowfall in Switzerland based on extreme value statistics," Climatic Change, Springer, vol. 111(3), pages 705-721, April.
    5. Yu Wang & Ling Wang & Jianhua Zong & Dongxiao Lv & Shumao Wang, 2021. "Research on Loading Method of Tractor PTO Based on Dynamic Load Spectrum," Agriculture, MDPI, vol. 11(10), pages 1-14, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liming Sun & Mengnan Liu & Zhipeng Wang & Chuqiao Wang & Fuqiang Luo, 2023. "Research on Load Spectrum Reconstruction Method of Exhaust System Mounting Bracket of a Hybrid Tractor Based on MOPSO-Wavelet Decomposition Technique," Agriculture, MDPI, vol. 13(10), pages 1-18, September.
    2. Zhengkai Wu & Jiazhong Wang & Yazhou Xing & Shanshan Li & Jinggang Yi & Chunming Zhao, 2023. "Energy Management of Sowing Unit for Extended-Range Electric Tractor Based on Improved CD-CS Fuzzy Rules," Agriculture, MDPI, vol. 13(7), pages 1-18, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Inés Barbeito & Ricardo Cao & Stefan Sperlich, 2023. "Bandwidth selection for statistical matching and prediction," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 418-446, March.
    2. Zhenguo Zhang & Quanfeng Guo & Jin He & Minyi Zhao & Zhenyu Xing & Chao Zeng & Han Lin & Quanyu Wang, 2023. "Design and Experiment of Side-Shift Stubble Avoidance System for No-Till Wheat Seeder Based on Deviation-Perception Fusion Technology," Agriculture, MDPI, vol. 13(1), pages 1-18, January.
    3. Christoph Lambio & Tillman Schmitz & Richard Elson & Jeffrey Butler & Alexandra Roth & Silke Feller & Nicolai Savaskan & Tobia Lakes, 2023. "Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln," IJERPH, MDPI, vol. 20(10), pages 1-22, May.
    4. Max Köhler & Anja Schindler & Stefan Sperlich, 2014. "A Review and Comparison of Bandwidth Selection Methods for Kernel Regression," International Statistical Review, International Statistical Institute, vol. 82(2), pages 243-274, August.
    5. Peiyuan Zhang & Jiaming Li & Wenzhong Zhang, 2022. "Characteristics of High-Technology Industry Migration within Metropolitan Areas—A Case Study of Beijing Metropolitan Area," Sustainability, MDPI, vol. 14(19), pages 1-19, October.
    6. M. Hiabu & E. Mammen & M. D. Martìnez-Miranda & J. P. Nielsen, 2016. "In-sample forecasting with local linear survival densities," Biometrika, Biometrika Trust, vol. 103(4), pages 843-859.
    7. Yicheng Tang & Xinyan Zhu & Wei Guo & Xinyue Ye & Tao Hu & Yaxin Fan & Faming Zhang, 2017. "Non-Homogeneous Diffusion of Residential Crime in Urban China," Sustainability, MDPI, vol. 9(6), pages 1-17, June.
    8. Dong Dai & Du Chen & Shumao Wang & Song Li & Xu Mao & Bin Zhang & Zhenyu Wang & Zheng Ma, 2023. "Compilation and Extrapolation of Load Spectrum of Tractor Ground Vibration Load Based on CEEMDAN-POT Model," Agriculture, MDPI, vol. 13(1), pages 1-20, January.
    9. Jinxin Zhu & Xuerou Weng & Bing Guo & Xueting Zeng & Cong Dong, 2023. "Investigating Extreme Snowfall Changes in China Based on an Ensemble of High-Resolution Regional Climate Models," Sustainability, MDPI, vol. 15(5), pages 1-17, February.
    10. D.P. Amali Dassanayake & Igor Volobouev & A. Alexandre Trindade, 2017. "Local orthogonal polynomial expansion for density estimation," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(4), pages 806-830, October.
    11. José María Sarabia & Faustino Prieto & Vanesa Jordá & Stefan Sperlich, 2020. "A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis," Risks, MDPI, vol. 8(2), pages 1-14, April.
    12. Tingting Cheng & Jiti Gao & Xibin Zhang, 2014. "Semiparametric Localized Bandwidth Selection for Kernel Density Estimation," Monash Econometrics and Business Statistics Working Papers 27/14, Monash University, Department of Econometrics and Business Statistics.
    13. El Heda, Khadijetou & Louani, Djamal, 2018. "Optimal bandwidth selection in kernel density estimation for continuous time dependent processes," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 9-19.
    14. Stefan Sperlich, 2022. "Comments on: hybrid semiparametric Bayesian networks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 335-339, June.
    15. Tingting Cheng & Jiti Gao & Xibin Zhang, 2019. "Nonparametric localized bandwidth selection for Kernel density estimation," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 733-762, August.
    16. Yu Liu & Chen Zeng & Huatai Cui & Yanhua Song, 2018. "Sustainable Land Urbanization and Ecological Carrying Capacity: A Spatially Explicit Perspective," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    17. Xiang Li & Jiang Zhu & Tao Liu & Xiangdong Yin & Jiangchun Yao & Hao Jiang & Bing Bu & Jianlong Yan & Yixuan Li & Zhangcheng Chen, 2023. "Quota and Space Allocations of New Urban Land Supported by Urban Growth Simulations: A Case Study of Guangzhou City, China," Land, MDPI, vol. 12(6), pages 1-21, June.
    18. Dai, Xinliang & Qu, Sheng & Sui, Hao & Wu, Pingbo, 2022. "Reliability modelling of wheel wear deterioration using conditional bivariate gamma processes and Bayesian hierarchical models," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    19. Joseph Ngatchou-Wandji & Marwa Ltaifa & Didier Alain Njamen Njomen & Jia Shen, 2022. "Nonparametric Estimation of the Density Function of the Distribution of the Noise in CHARN Models," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    20. Zening Xu & Xiaolu Gao & Zhiqiang Wang & Jie Fan, 2019. "Big Data-Based Evaluation of Urban Parks: A Chinese Case Study," Sustainability, MDPI, vol. 11(7), pages 1-16, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1133-:d:1157885. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.