• DocumentCode
    991849
  • Title

    Fault classification and fault signature production for rolling element bearings in electric machines

  • Author

    Stack, Jason R. ; Habetler, Thomas G. ; Harley, Ronald G.

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
  • Volume
    40
  • Issue
    3
  • fYear
    2004
  • Firstpage
    735
  • Lastpage
    739
  • Abstract
    Most condition monitoring techniques for rolling element bearings are designed to detect the four characteristic fault frequencies. This has lead to the common practice of categorizing bearing faults according to fault location (i.e., inner race, outer race, ball, or cage fault). While the ability to detect the four characteristic fault frequencies is necessary, this approach neglects another important class of faults that arise in many industrial settings. This research introduces the notion of categorizing bearing faults as either single-point defects or generalized roughness. These classes separate bearing faults according to the fault signatures that are produced rather than by the physical location of the fault. Specifically, single-point defects produce the four predictable characteristic fault frequencies while faults categorized as generalized roughness produce unpredictable broadband changes in the machine vibration and stator current. Experimental results are provided from bearings failed in situ via a shaft current. These results illustrate the unpredictable and broadband nature of the effects produced by generalized roughness bearing faults. This issue is significant because a successful bearing condition monitoring scheme must be able to reliably detect both classes of faults.
  • Keywords
    condition monitoring; electric machines; fault location; machine bearings; rolling bearings; bearing faults; condition monitoring; electric machines; fault classification; fault frequencies; fault location; fault signature production; machine vibration; rolling element bearings; stator current; Condition monitoring; Electric machines; Electrical fault detection; Fault detection; Fault location; Frequency; Production; Rolling bearings; Shafts; Stators; Bearings; condition monitoring; fault diagnosis; mechanical; vibration;
  • fLanguage
    English
  • Journal_Title
    Industry Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0093-9994
  • Type

    jour

  • DOI
    10.1109/TIA.2004.827454
  • Filename
    1300726