• DocumentCode
    553221
  • Title

    Fault diagnosis for sensors of aero-engine based on improved least squares support vector regression

  • Author

    Shujing Duan ; Qiuhong Li ; Yongping Zhao

  • Author_Institution
    Coll. of Energy & Power Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
  • Volume
    3
  • fYear
    2011
  • fDate
    26-28 July 2011
  • Firstpage
    1962
  • Lastpage
    1966
  • Abstract
    In this paper, an intelligent online sensor faults diagnosis system of aero-engine is designed based on improved least squares support vector machine. In order to distinguish sensor offset fault and drift fault, the method, called threshold discriminance, is adopted. For common sensor faults, including the single or multiple sensor offset faults and drift faults, the sensor faults diagnosis system is able to accomplish the fault detection, isolation and signal reconstruction well. And the simulation experiments of aero-engine sensor faults show the effectiveness and validity of the proposed system.
  • Keywords
    aerospace engines; computerised instrumentation; fault diagnosis; intelligent sensors; least squares approximations; regression analysis; signal reconstruction; support vector machines; aeroengine sensor faults; fault detection; fault isolation; improved least square support vector regression; intelligent online sensor fault diagnosis system; sensor drift fault; sensor offset fault; signal reconstruction; threshold discriminance; Control systems; Fault diagnosis; Intelligent sensors; Redundancy; Sensor systems; Support vector machines; aero-engine; improved least squares support vector regression; one or two-sensor faults; sensor fault diagnosis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-61284-180-9
  • Type

    conf

  • DOI
    10.1109/FSKD.2011.6019897
  • Filename
    6019897