IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/9873268.html
   My bibliography  Save this article

Research on CEEMD-AGA Denoising Method and Its Application in Feed Mixer

Author

Listed:
  • Dong Yang
  • Yu Sun
  • Kai Wu

Abstract

Rotating shaft is the key part of rotating machinery, which directly affects the performance of the whole machine. Field test is an easy and quick way to obtain the load data in engineering practice. However, because of various reasons, the load data are often mixed with many noise components. Based on the autocorrelation function, the CEEMD (complementary ensemble empirical mode decomposition) denoising method is proposed in this paper. The AGA (adaptive genetic algorithm) is adopted to solve parameter optimization problems in CEEMD. A new similarity function is proposed as the fitness function. Lastly, the proposed denoising method is applied to a feed mixer’s load which is obtained by field test. The result shows that the CEEMD-AGA method has good robustness, noise components of small stress amplitude and large stress mean are removed, and there is a high correlation between the original data and the reconstructed data, which demonstrate that the CEEMD-AGA method can reduce the influence of noise components effectively.

Suggested Citation

  • Dong Yang & Yu Sun & Kai Wu, 2020. "Research on CEEMD-AGA Denoising Method and Its Application in Feed Mixer," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, January.
    • Handle: RePEc:hin:jnlmpe:9873268
    DOI: 10.1155/2020/9873268
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2020/9873268.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2020/9873268.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/9873268?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:9873268. 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.