IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i2p461-d1069886.html
   My bibliography  Save this article

Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN

Author

Listed:
  • Jiabo Wang

    (College of Engineering, Nanjing Agricultural University, No. 40 Dianjiangtai Road, Pukou District, Nanjing 210031, China
    College of Mechanical and Electrical Engineering, Jiangsu Vocational College of Agriculture and Forestry, No. 19 Wenchang East Road, Jurong 212400, China)

  • Zhixiong Lu

    (College of Engineering, Nanjing Agricultural University, No. 40 Dianjiangtai Road, Pukou District, Nanjing 210031, China)

  • Guangming Wang

    (College of Mechanical and Electronic Engineering, Shandong Agricultural University, No. 61 Daizong Street, Taishan District, Taian 271018, China)

  • Ghulam Hussain

    (Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Tarbela Road, District Swabi, Khyber Pakhtoon Khwa, Topi 23460, Pakistan)

  • Shanhu Zhao

    (Jiangsu Yueda Intelligent Agricultural Equipment Co., Ltd., No. 9 Nenjiang Road, Economic and Technological Development Zone, Yancheng 224100, China)

  • Haijun Zhang

    (College of Engineering, Nanjing Agricultural University, No. 40 Dianjiangtai Road, Pukou District, Nanjing 210031, China)

  • Maohua Xiao

    (College of Engineering, Nanjing Agricultural University, No. 40 Dianjiangtai Road, Pukou District, Nanjing 210031, China)

Abstract

There are some problems in the shifting process of hydraulic CVT, such as irregularity, low stability and high failure rate. In this paper, the BP neural network and convolutional neural network are used for fault diagnosis of the HMCVT hydraulic system. Firstly, through experiments, 120 groups of pressure and flow data under normal and four typical fault modes were obtained and preprocessed; they were divided into 80 groups of training samples and 40 groups of test samples via random extraction, using the BP neural network model and convolutional neural network model for fault classification. The results show that compared with BP, PSO-BP and other models, the fault diagnosis rate of the BAS-BP neural network model can reach 92.5%, and the average diagnosis accuracy rate of the convolutional neural network can reach 97.5%, which can be effectively applied to the fault diagnosis of the HMCVT hydraulic system and provide some reference for the shifting reliability of hydraulic CVT.

Suggested Citation

  • Jiabo Wang & Zhixiong Lu & Guangming Wang & Ghulam Hussain & Shanhu Zhao & Haijun Zhang & Maohua Xiao, 2023. "Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN," Agriculture, MDPI, vol. 13(2), pages 1-17, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:461-:d:1069886
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/2/461/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/2/461/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Fengyun Xie & Gang Li & Hui Liu & Enguang Sun & Yang Wang, 2024. "Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement," Agriculture, MDPI, vol. 14(1), pages 1-16, January.
    2. Fengyun Xie & Yang Wang & Gan Wang & Enguang Sun & Qiuyang Fan & Minghua Song, 2024. "Fault Diagnosis of Rolling Bearings in Agricultural Machines Using SVD-EDS-GST and ResViT," Agriculture, MDPI, vol. 14(8), pages 1-16, August.

    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:2:p:461-:d:1069886. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.