IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i12p2322-d1546096.html
   My bibliography  Save this article

The Load Cycle Amplitude Model: An Efficient Time-Domain Extrapolation Technique for Non-Stationary Loads in Agricultural Machinery

Author

Listed:
  • Zihan Yang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, Beijing 100083, China
    Luoyang Smart Agricultural Equipment Institute Co., Ltd., Luoyang 471000, China)

  • Xuke Liu

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, Beijing 100083, China)

  • Zhenghe Song

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, Beijing 100083, China)

  • Hanting Liu

    (Luoyang Smart Agricultural Equipment Institute Co., Ltd., Luoyang 471000, China)

Abstract

In traditional time-domain extrapolation methods, the peak over threshold (POT) model is unable to accurately identify large load cycles in the load time history, resulting in distorted extrapolation results, particularly when addressing non-stationary loads. To address this problem, this paper proposes a time-domain extrapolation method based on the load cycle amplitude (LCA) model. The core of the method involves using load cycle amplitude features extracted from the measured loads as the basis for modelling, rather than extreme turning points based on threshold extraction. This approach prevents the load’s time-domain characteristics from compromising the accuracy of the extrapolation results. The case analysis results demonstrate that the extrapolation method based on the LCA model achieves more reliable results with both non-stationary and stationary loads. Furthermore, the streamlined modelling process results in reductions of 10.63% and 20.84% in the average computing time for the algorithm when addressing stress and vibration loads, respectively. The LCA model proposed in this paper further facilitates the integration of time-domain extrapolation methods into reliability analysis software.

Suggested Citation

  • Zihan Yang & Xuke Liu & Zhenghe Song & Hanting Liu, 2024. "The Load Cycle Amplitude Model: An Efficient Time-Domain Extrapolation Technique for Non-Stationary Loads in Agricultural Machinery," Agriculture, MDPI, vol. 14(12), pages 1-17, December.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:12:p:2322-:d:1546096
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/12/2322/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/12/2322/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. Fátima Brilhante, M. & Ivette Gomes, M. & Pestana, Dinis, 2013. "A simple generalisation of the Hill estimator," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 518-535.
    4. Xia Yang & Jing Zhang & Wei-Xin Ren, 2018. "Threshold selection for extreme value estimation of vehicle load effect on bridges," International Journal of Distributed Sensor Networks, , vol. 14(2), pages 15501477187, February.
    5. Ghosh, Souvik & Resnick, Sidney, 2010. "A discussion on mean excess plots," Stochastic Processes and their Applications, Elsevier, vol. 120(8), pages 1492-1517, August.
    Full references (including those not matched with items on IDEAS)

    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. Igor Fedotenkov, 2020. "A Review of More than One Hundred Pareto-Tail Index Estimators," Statistica, Department of Statistics, University of Bologna, vol. 80(3), pages 245-299.
    2. Ivanilda Cabral & Frederico Caeiro & M. Ivette Gomes, 2022. "On the comparison of several classical estimators of the extreme value index," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(1), pages 179-196, January.
    3. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    4. Søren Asmussen & Jaakko Lehtomaa, 2017. "Distinguishing Log-Concavity from Heavy Tails," Risks, MDPI, vol. 5(1), pages 1-14, February.
    5. Peter Grundke & Kamil Pliszka, 2018. "A macroeconomic reverse stress test," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 1093-1130, May.
    6. Xia Yang & Jing Zhang & Wei-Xin Ren, 2018. "Threshold selection for extreme value estimation of vehicle load effect on bridges," International Journal of Distributed Sensor Networks, , vol. 14(2), pages 15501477187, February.
    7. Cirillo, Pasquale, 2013. "Are your data really Pareto distributed?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5947-5962.
    8. Arthur Charpentier & Emmanuel Flachaire, 2022. "Pareto models for top incomes and wealth," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 1-25, March.
    9. Yuanyuan Gao & Yifei Yang & Yongyue Hu & Xing Han & Kangyao Feng & Peiying Li & Xinhua Wei & Changyuan Zhai, 2024. "Study on Operating Vibration Characteristics of Different No-Tillage Planter Row Units in Wheat Stubble Fields," Agriculture, MDPI, vol. 14(11), pages 1-18, October.
    10. Joan Del Castillo & Jalila Daoudi & Richard Lockhart, 2014. "Methods to Distinguish Between Polynomial and Exponential Tails," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 382-393, June.
    11. Xiao Wang & Lihong Wang, 2024. "A tail index estimation for long memory processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 87(8), pages 947-971, November.
    12. Liu, Shengli & Liang, Yongtu, 2021. "Statistics of catastrophic hazardous liquid pipeline accidents," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    13. Arthur Charpentier & Emmanuel Flachaire, 2021. "Pareto Models for Risk Management," Dynamic Modeling and Econometrics in Economics and Finance, in: Gilles Dufrénot & Takashi Matsuki (ed.), Recent Econometric Techniques for Macroeconomic and Financial Data, pages 355-387, Springer.
    14. Chunli Huang & Xu Zhao & Weihu Cheng & Qingqing Ji & Qiao Duan & Yufei Han, 2022. "Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors," Mathematics, MDPI, vol. 10(9), pages 1-25, April.
    15. Xin Gao & Gengxin Duan & Chunguang Lan, 2021. "Bayesian Updates for an Extreme Value Distribution Model of Bridge Traffic Load Effect Based on SHM Data," Sustainability, MDPI, vol. 13(15), pages 1-15, August.
    16. Taillardat, Maxime & Fougères, Anne-Laure & Naveau, Philippe & de Fondeville, Raphaël, 2023. "Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1448-1459.
    17. M. Ivette Gomes & Armelle Guillou, 2015. "Extreme Value Theory and Statistics of Univariate Extremes: A Review," International Statistical Review, International Statistical Institute, vol. 83(2), pages 263-292, August.
    18. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2017. "Risk quantification in turmoil markets," Risk Management, Palgrave Macmillan, vol. 19(3), pages 202-224, August.
    19. Vygantas Paulauskas & Marijus Vaičiulis, 2017. "A class of new tail index estimators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(2), pages 461-487, April.
    20. M. Roth & G. Jongbloed & T.A. Buishand, 2016. "Threshold selection for regional peaks-over-threshold data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1291-1309, July.

    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:14:y:2024:i:12:p:2322-:d:1546096. 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.