• DocumentCode
    658088
  • Title

    Sensor and actuator fault detection and isolation based on artificial neural networks and fuzzy logic applicated on induction motor

  • Author

    Adouni, Amel ; Ben Hamed, Mouna ; Flah, Aymen ; Sbita, Lassaad

  • Author_Institution
    Electr. Dept., Nat. Eng. Sch. of Gabes, Gabes, Tunisia
  • fYear
    2013
  • fDate
    6-8 May 2013
  • Firstpage
    917
  • Lastpage
    922
  • Abstract
    This paper presents a scheme for fault detection and isolation (FDI). It deals with sensors and actuator fault of an induction machine. This scheme is established with artificial intelligent techniques in order to resolve two big troubles. The first is the detection problem. It is resolved with the neural network and the second is the isolation difficulty, it solved using the fuzzy logic. The proposed FDI approach is implemented on Matlab/Simulink software and tested under three types of fault (current, speed sensor fault and inverter fault). The obtained results improving the importance of this method. Then, the actuator and sensor fault are detected and isolated successfully.
  • Keywords
    fault diagnosis; fuzzy logic; induction motors; neural nets; sensors; Matlab/Simulink software; actuator fault detection; artificial intelligent techniques; artificial neural networks; fault isolation; fuzzy logic; induction machine; induction motor; sensor fault; Actuators; Analytical models; Circuit faults; Fuzzy logic; Induction motors; Inverters; Neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Decision and Information Technologies (CoDIT), 2013 International Conference on
  • Conference_Location
    Hammamet
  • Print_ISBN
    978-1-4673-5547-6
  • Type

    conf

  • DOI
    10.1109/CoDIT.2013.6689665
  • Filename
    6689665