Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Improved condition monitoring technique for wind turbine gearbox and shaft stress detection

    Access Status
    Fulltext not available
    Authors
    Salem, A.
    Abu-Siada, Ahmed
    Islam, S.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Salem, A. and Abu-Siada, A. and Islam, S. 2017. Improved condition monitoring technique for wind turbine gearbox and shaft stress detection. IET Science, Measurement and Technology. 11 (4): pp. 431-437.
    Source Title
    IET Science, Measurement and Technology
    DOI
    10.1049/iet-smt.2016.0338
    ISSN
    1751-8822
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/55873
    Collection
    • Curtin Research Publications
    Abstract

    © The Institution of Engineering and Technology 2017. Condition monitoring has been widely used to detect mechanical and electrical faults of wind turbine in order to avoid any potential catastrophic failures, reduce operational and maintenance cost, and enhance the reliability and availability of the equipment. Although several papers about wind turbine condition monitoring can be found in the literatures, adopting a reliable and cost-effective technique for wind turbines that usually experience severe mechanical stress due to the harsh weather conditions they are exposed to is still challenging. Although statistical studies show that gearbox failure rate is low, the resulted downtime and the replacement or repairing cost is substantial. This study introduces an improved technique to monitor the condition of the wind turbine gearbox based on gearbox vibration and shaft torque signatures analyses. In this context, a test rig that emulates real wind turbine operation has been developed to analyse the behaviour of wind turbine under various mechanical fault levels. Shaft torque and mechanical vibration signals are detected using high-resolution sensors and are analysed using two signal processing techniques: wavelet and order analyses to examine the impact of investigated fault levels on the wind turbine drive train. Both signal processing techniques are compared based on their sensitivity to detect incipient fault levels.

    Related items

    Showing items related by title, author, creator and subject.

    • Dynamic modelling of planetary gear systems for gear tooth fault
      Wang, Zhongwei (2010)
      Geared systems have been widely used in mechanical applications for more than a hundred years. A large range of literature has been published especially for spur/helical gear systems and the investigations into technical ...
    • On Line Condition Monitoring of Wind Turbine Generators
      Salem, Abdulwahed Almokhtar (2016)
      Condition monitoring has been widely used to detect mechanical and electrical faults of wind turbines in order to avoid catastrophic failures, enhance the reliability and availability of the equipment. This thesis introduces ...
    • Torsional vibration signal analysis as a diagnostic tool for planetary gear fault detection
      Xue, S.; Howard, Ian (2018)
      This paper aims to investigate the effectiveness of using the torsional vibration signal as a diagnostic tool for planetary gearbox faults detection. The traditional approach for condition monitoring of the planetary gear ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.