IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v29y2018i6d10.1007_s10845-015-1174-x.html
   My bibliography  Save this article

Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory

Author

Listed:
  • Wentao Huang

    (Harbin Institute of Technology)

  • Fanzhao Kong

    (Harbin Institute of Technology)

  • Xuezeng Zhao

    (Harbin Institute of Technology)

Abstract

The gearbox is an important component in industrial drives, providing safe and reliable operation for industrial production. Wavelet packet transform (WPT) analysis was used to extract fault features in the vibration signals generated by a gearbox. The extracted features from the WPT were used as input in a rough set (RS) for attribute reduction and then combined with a genetic algorithm to obtain global optimal attribute reduction results. The fault features gained after the attribute reductions were used to generate decision rules. The unknown gear status signal attributes were used as input to match the generated decision rules for fault diagnosis purposes. Gearbox vibration signals contain a significant amount of gear status information; a WPT has an acute portion-locked ability to extract attribute information from the vibration signals. However, WPT frequency aliasing would lead to the generation of spurious frequency components, affecting gear fault diagnosis. In this paper, we introduce an improved WPT to eliminate frequency aliasing, thus improving the accuracy of fault diagnosis. This paper studies the use of wavelet packet for feature extraction and the RS for classification; the results demonstrate that this method can accurately and reliably detect failure modes in a gearbox.

Suggested Citation

  • Wentao Huang & Fanzhao Kong & Xuezeng Zhao, 2018. "Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1257-1271, August.
  • Handle: RePEc:spr:joinma:v:29:y:2018:i:6:d:10.1007_s10845-015-1174-x
    DOI: 10.1007/s10845-015-1174-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-015-1174-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-015-1174-x?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Guoping An & Qingbin Tong & Yanan Zhang & Ruifang Liu & Weili Li & Junci Cao & Yuyi Lin, 2021. "An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing," Energies, MDPI, vol. 14(4), pages 1-24, February.
    2. Wei Zhang & Hong Lu & Yongquan Zhang & Zhangjie Li & Yongjing Wang & Jun Zhou & Jiangnuo Mei & Yuzhan Wei, 2022. "A Fault Diagnosis Scheme for Gearbox Based on Improved Entropy and Optimized Regularized Extreme Learning Machine," Mathematics, MDPI, vol. 10(23), pages 1-28, December.
    3. Guo, Sheng & Yang, Tao & Hua, Haochen & Cao, Junwei, 2021. "Coupling fault diagnosis of wind turbine gearbox based on multitask parallel convolutional neural networks with overall information," Renewable Energy, Elsevier, vol. 178(C), pages 639-650.
    4. Rubén Medina & Jean Carlo Macancela & Pablo Lucero & Diego Cabrera & René-Vinicio Sánchez & Mariela Cerrada, 2022. "Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1031-1055, April.

    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:spr:joinma:v:29:y:2018:i:6:d:10.1007_s10845-015-1174-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.