IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/9908074.html
   My bibliography  Save this article

Gear Fault Detection in a Planetary Gearbox Using Deep Belief Network

Author

Listed:
  • Hu Hao
  • Feng Fuzhou
  • Jiang Feng
  • Zhou Xun
  • Zhu Junzhen
  • Xue Jun
  • Jiang Pengcheng
  • Li Yazhi
  • Qian Yongchan
  • Sun Guanghui
  • Chen Caishen
  • Juan P. Amezquita-Sanchez

Abstract

Traditional prognostics and health management (PHM) methods for fault detection require complex signal processing and manual fault feature extraction, and the accuracy is low. To address these problems, a fault diagnosis method of planetary gearbox based on deep belief networks (DBNs) is proposed. Firstly, the vibration signals of the planetary gearbox are collected and analyzed in the time domain and the frequency domain. Then, the DBN model and optimal parameters are determined to meet the task requirements. Finally, the vibration data is divided into training set and test set and input into the DBN model, which can realize the automatic feature extraction and fault recognition of vibration signals. The results show that the identification accuracy reaches 97% under five working conditions of planetary gearbox.

Suggested Citation

  • Hu Hao & Feng Fuzhou & Jiang Feng & Zhou Xun & Zhu Junzhen & Xue Jun & Jiang Pengcheng & Li Yazhi & Qian Yongchan & Sun Guanghui & Chen Caishen & Juan P. Amezquita-Sanchez, 2022. "Gear Fault Detection in a Planetary Gearbox Using Deep Belief Network," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, March.
    • Handle: RePEc:hin:jnlmpe:9908074
    DOI: 10.1155/2022/9908074
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/9908074.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/9908074.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/9908074?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:9908074. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.