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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    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.
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    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.

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