IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/3124048.html
   My bibliography  Save this article

Parameter Estimation Method of Mixture Distribution for Construction Machinery

Author

Listed:
  • Xinting Zhai
  • Jixin Wang
  • Jinshi Chen

Abstract

Due to the harsh working environment of the construction machinery, a simple distribution cannot be used to approximate the shape of the rainflow matrix. In this paper, the Weibull-normal (W-n) mixture distribution is used. The lowest Akaike information criterion (AIC) value is employed to determine the components number of the mixture. A parameter estimation method based on the idea of optimization is proposed. The method estimates parameters of the mixture by maximizing the log likelihood function (LLF) using an intelligent optimization algorithm (IOA), genetic algorithm (GA). To verify the performance of the proposed method, one of the already existing methods is applied in the simulation study and the practical case study. The fitting effects of the fitted distributions are compared by calculating the AIC and chi-square ( ) value. It can be concluded that the proposed method is feasible and effective for parameter estimation of the mixture distribution.

Suggested Citation

  • Xinting Zhai & Jixin Wang & Jinshi Chen, 2018. "Parameter Estimation Method of Mixture Distribution for Construction Machinery," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-9, September.
    • Handle: RePEc:hin:jnlmpe:3124048
    DOI: 10.1155/2018/3124048
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2018/3124048.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2018/3124048.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/3124048?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:3124048. 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.