• DocumentCode
    1586673
  • Title

    Remaining useful life prognostic estimation for aircraft subsystems or components: A review

  • Author

    Xiongzi, Chen ; Jinsong, Yu ; Diyin, Tang ; Yingxun, Wang

  • Author_Institution
    Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing, China
  • Volume
    2
  • fYear
    2011
  • Firstpage
    94
  • Lastpage
    98
  • Abstract
    The techniques of remaining useful life (RUL) estimation are playing a more and more important role in aircraft safety and condition based maintenance. This paper gives an overview of RUL prognostic estimation approaches applied to aircraft subsystems or components. Existing RUL estimation approaches are categorized into three types, namely model-based approaches, data-driven approaches and fusion approaches and their characteristics are comprehensively introduced. Moreover, three common and promising methods: particle filtering, neural network and relevant vector machine as well as their advantages and disadvantages are discussed in details. Finally, the future challenges concerned with RUL prediction are also presented.
  • Keywords
    aerospace computing; aircraft instrumentation; aircraft maintenance; neural nets; particle filtering (numerical methods); RUL prognostic estimation; aircraft components; aircraft safety; aircraft subsystems; condition based maintenance; neural network; particle filtering; remaining useful life prognostic estimation; vector machine; Aerospace electronics; Aircraft; Aircraft propulsion; Atmospheric modeling; Estimation; Hidden Markov models; Predictive models; aircraft component; fusion approach; prognostic estimation; remaining useful life;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronic Measurement & Instruments (ICEMI), 2011 10th International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-8158-3
  • Type

    conf

  • DOI
    10.1109/ICEMI.2011.6037773
  • Filename
    6037773