• DocumentCode
    2287751
  • Title

    Detection of Bearing Failure in Rotating Machine Using Adaptive Neuro-Fuzzy Inference System

  • Author

    Wadhwani, Sulochana ; Wadhwani, A.K. ; Gupta, S.P. ; Kumar, Vinod

  • Author_Institution
    Madhav Inst. of Technol. & Sci., Gwalior
  • fYear
    2006
  • fDate
    12-15 Dec. 2006
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    This paper proposes a novel approach for bearing health evaluation using Lempel-Ziv complexity and time domain statistical parameters in conjunction with ANFIS. Compared to conventional techniques the presented approach works well for a non linear physical system and is thus suited for condition monitoring of machine system under varying operating and loading conditions. The performance of this technique is investigated through experimental study of realistic vibration signals. The results demonstrate that complexity analysis and time domain parameters in conjunction with ANFIS provide an effective measure forebearing health evaluation.
  • Keywords
    adaptive systems; condition monitoring; electric machine analysis computing; failure analysis; fault diagnosis; fuzzy neural nets; inference mechanisms; machine bearings; statistical analysis; vibrations; ANFIS; Lempel-Ziv complexity; adaptive neuro-fuzzy inference system; bearing failure detection; condition monitoring; health evaluation; non linear physical system; rotating machine; time domain statistical parameters; vibration signals; Adaptive systems; Condition monitoring; Fault detection; Fault diagnosis; Frequency; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Neural networks; Rotating machines; ANFIS; Bearing fault; Fault detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Electronics, Drives and Energy Systems, 2006. PEDES '06. International Conference on
  • Conference_Location
    New Delhi
  • Print_ISBN
    0-7803-9772-X
  • Electronic_ISBN
    0-7803-9772-X
  • Type

    conf

  • DOI
    10.1109/PEDES.2006.344317
  • Filename
    4148024