• DocumentCode
    738306
  • Title

    Ball bearing damage detection using traditional signal processing algorithms

  • Author

    Bediaga, I. ; Mendizabal, X. ; Arnaiz, Aitor ; Munoa, Jokin

  • Volume
    16
  • Issue
    2
  • fYear
    2013
  • fDate
    4/1/2013 12:00:00 AM
  • Firstpage
    20
  • Lastpage
    25
  • Abstract
    Fault detection and diagnosis of ball bearings has always been a challenge when monitoring rotating machinery. Specifically, bearing diagnostics have seen extensive research in the field of fault detection and diagnosis. This article reviews traditional algorithms used to detect and diagnose faulty bearings in heavy-duty milling machine tool spindle heads. Different kinds of faults have been created deliberately on the bearings of a test spindle head. The prediction effectiveness of several detection methods are tested when faults are in different stages of development.
  • Keywords
    ball bearings; condition monitoring; fault diagnosis; machine tool spindles; mechanical engineering computing; milling machines; signal processing; ball bearing damage detection; fault detection; fault diagnosis; heavy-duty milling machine tool spindle heads; rotating machinery monitoring; signal processing; Ball bearings; Demodulation; Frequency measurement; Frequency modulation; Resonant frequency; Transforms; Vibrations;
  • fLanguage
    English
  • Journal_Title
    Instrumentation & Measurement Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1094-6969
  • Type

    jour

  • DOI
    10.1109/MIM.2013.6495676
  • Filename
    6495676