IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i6p893-d843430.html
   My bibliography  Save this article

Research on Comprehensive Operation and Maintenance Based on the Fault Diagnosis System of Combine Harvester

Author

Listed:
  • Weipeng Zhang

    (The State Key Laboratory of Soil-Plant-Machinery System Technology, Beijing 100083, China
    Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Bo Zhao

    (The State Key Laboratory of Soil-Plant-Machinery System Technology, Beijing 100083, China
    Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Liming Zhou

    (The State Key Laboratory of Soil-Plant-Machinery System Technology, Beijing 100083, China
    Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Jizhong Wang

    (The State Key Laboratory of Soil-Plant-Machinery System Technology, Beijing 100083, China
    Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Kang Niu

    (The State Key Laboratory of Soil-Plant-Machinery System Technology, Beijing 100083, China
    Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Fengzhu Wang

    (The State Key Laboratory of Soil-Plant-Machinery System Technology, Beijing 100083, China
    Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Ruixue Wang

    (The State Key Laboratory of Soil-Plant-Machinery System Technology, Beijing 100083, China
    Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

Abstract

In view of the difficulty in diagnosing and discriminating fault conditions during the operation of combine harvesters, difficulty in real-time processing of health status, and low timeliness of fault processing, a comprehensive operation and maintenance platform for combine harvesters was developed in this study which realized the functions of data monitoring and the full operation and maintenance of a combine harvester. At the same time, through the comprehensive operation and maintenance platform, the harvester information was obtained in real-time, the diagnosis results were obtained, and the maintenance service was effectively carried out through the platform. The IPSO-SVM fault diagnosis algorithm was proposed, and the performance of the fault diagnosis of the combine harvester was verified by the simulation test. The experimental verification showed that the system met the requirements of remote monitoring of combine harvesters, and the prediction accuracy of this method was 97.96%. Compared with SVM (87.51%), GA-SVM (89.44%), and PSO-SVM (92.56%), this system had better generalization ability and effectively improved the management level of the comprehensive operation and maintenance of the combine harvester. A theoretical basis and technical reference will be provided for the follow-up research for the comprehensive operation and maintenance platform of the combine harvester in this paper.

Suggested Citation

  • Weipeng Zhang & Bo Zhao & Liming Zhou & Jizhong Wang & Kang Niu & Fengzhu Wang & Ruixue Wang, 2022. "Research on Comprehensive Operation and Maintenance Based on the Fault Diagnosis System of Combine Harvester," Agriculture, MDPI, vol. 12(6), pages 1-17, June.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:6:p:893-:d:843430
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/6/893/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/6/893/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Peng Zhang & Hongbo Xu & Zhichao Hu & Youqing Chen & Mingzhu Cao & Zhaoyang Yu & Enrong Mao, 2021. "Characteristics of Agricultural Dust Emissions from Harvesting Operations: Case Study of a Whole-Feed Peanut Combine," Agriculture, MDPI, vol. 11(11), pages 1-14, October.
    3. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    4. Jianwei Fu & Chao Ji & Haopeng Liu & Weikang Wang & Guozhong Zhang & Yuan Gao & Yong Zhou & Mohamed Anwer Abdeen, 2022. "Research Progress and Prospect of Mechanized Harvesting Technology in the First Season of Ratoon Rice," Agriculture, MDPI, vol. 12(5), pages 1-33, April.
    5. Feng Han & Ying Tian & Qiang Zou & Xin Zhang, 2020. "Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System," Energies, MDPI, vol. 13(10), pages 1-18, May.
    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. Wang, Bingqing & Li, Yongping & Huang, Guohe & Gao, Pangpang & Liu, Jing & Wen, Yizhuo, 2023. "Development of an integrated BLSVM-MFA method for analyzing renewable power-generation potential under climate change: A case study of Xiamen," Applied Energy, Elsevier, vol. 337(C).
    2. Yehong Liu & Xin Wang & Dong Dai & Can Tang & Xu Mao & Du Chen & Yawei Zhang & Shumao Wang, 2023. "Knowledge Discovery and Diagnosis Using Temporal-Association-Rule-Mining-Based Approach for Threshing Cylinder Blockage," Agriculture, MDPI, vol. 13(7), pages 1-21, 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. Debesh Mishra & Suchismita Satapathy, 2024. "Modified reaper for small-scale farmers: an approach for sustainable agriculture," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(1), pages 1451-1480, January.
    2. Dong, Yutong & Jiang, Hongkai & Wu, Zhenghong & Yang, Qiao & Liu, Yunpeng, 2023. "Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Reza Ghasemi & Mehdi Sedighi & Mostafa Ghasemi & Bita Sadat Ghazanfarpoor, 2023. "Design of a Fuzzy Adaptive Voltage Controller for a Nonlinear Polymer Electrolyte Membrane Fuel Cell with an Unknown Dynamical System," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    4. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Yuan Liu & Long Shao & Wanzhang Wang & Jinfan Chen & Heng Zhang & Yue Yang & Baichen Hu, 2022. "Study on Fugitive Dust Control Technologies of Agricultural Harvesting Machinery," Agriculture, MDPI, vol. 12(7), pages 1-22, July.
    6. Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
    7. F. H. Abanda & N. Jian & S. Adukpo & V. V. Tuhaise & M. B. Manjia, 2025. "Digital twin for product versus project lifecycles’ development in manufacturing and construction industries," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 801-831, February.
    8. Guo, Jianchun & Si, Zetian & Liu, Yi & Li, Jiahao & Li, Yanting & Xiang, Jiawei, 2022. "Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    9. Wang, Jinrui & Zhang, Zongzhen & Liu, Zhiliang & Han, Baokun & Bao, Huaiqian & Ji, Shanshan, 2023. "Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. Yu-Yue Yu & Da-Ming Shi & Han Ding & Xiao-Ming Zhang, 2025. "Prediction of thin-walled workpiece machining error: a transfer learning approach," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2803-2827, April.
    11. Dorijan Radočaj & Ivan Plaščak & Mladen Jurišić, 2023. "Global Navigation Satellite Systems as State-of-the-Art Solutions in Precision Agriculture: A Review of Studies Indexed in the Web of Science," Agriculture, MDPI, vol. 13(7), pages 1-17, July.
    12. Zhe Li & Kexin Liu & Xudong Wang & Xiaofang Yuan & He Xie & Yaonan Wang, 2025. "A signal-to-image fault classification method based on multi-sensor data for robotic grinding monitoring," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 537-550, January.
    13. Li, Qi & Chen, Liang & Kong, Lin & Wang, Dong & Xia, Min & Shen, Changqing, 2023. "Cross-domain augmentation diagnosis: An adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    14. Lu, Feiyu & Tong, Qingbin & Jiang, Xuedong & Feng, Ziwei & Liu, Ruifang & Xu, Jianjun & Huo, Jingyi, 2024. "DPICEN: Deep physical information consistency embedded network for bearing fault diagnosis under unknown domain," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    15. Zou, Xinyu & Tao, Laifa & Sun, Lulu & Wang, Chao & Ma, Jian & Lu, Chen, 2023. "A case-learning-based paradigm for quantitative recommendation of fault diagnosis algorithms: A case study of gearbox," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    16. Gao, Dawei & Huang, Kai & Zhu, Yongsheng & Zhu, Linbo & Yan, Ke & Ren, Zhijun & Guedes Soares, C., 2024. "Semi-supervised small sample fault diagnosis under a wide range of speed variation conditions based on uncertainty analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    17. Ding, Yifei & Jia, Minping & Zhuang, Jichao & Cao, Yudong & Zhao, Xiaoli & Lee, Chi-Guhn, 2023. "Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    18. Zhou, Taotao & Zhang, Laibin & Han, Te & Droguett, Enrique Lopez & Mosleh, Ali & Chan, Felix T.S., 2023. "An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    19. Zhengyang Fan & Wanru Li & Kuo-Chu Chang, 2023. "A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation," Mathematics, MDPI, vol. 11(24), pages 1-17, December.
    20. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Dually attentive multiscale networks for health state recognition of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

    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:12:y:2022:i:6:p:893-:d:843430. 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.