Advanced search
1 file | 1.57 MB Add to list

The reflection of evolving bearing faults in the stator current’s extended park vector approach for induction machines

Bram Corne (UGent) , Bram Vervisch (UGent) , Stijn Derammelaere (UGent) , Jos Knockaert (UGent) and Jan Desmet (UGent)
Author
Organization
Abstract
Stator current analysis has the potential of becoming the most cost-effective condition monitoring technology regarding electric rotating machinery. Since both electrical and mechanical faults are detected by inexpensive and robust current-sensors, measuring current is advantageous on other techniques such as vibration, acoustic or temperature analysis. However, this technology is struggling to breach into the market of condition monitoring as the electrical interpretation of mechanical machine-problems is highly complicated. Recently, the authors built a test-rig which facilitates the emulation of several representative mechanical faults on an 11 kW induction machine with high accuracy and reproducibility. Operating this test-rig, the stator current of the induction machine under test can be analyzed while mechanical faults are emulated. Furthermore, while emulating, the fault-severity can be manipulated adaptively under controllable environmental conditions. This creates the opportunity of examining the relation between the magnitude of the well-known current fault components and the corresponding fault-severity. This paper presents the emulation of evolving bearing faults and their reflection in the Extended Park Vector Approach for the 11 kW induction machine under test. The results confirm the strong relation between the bearing faults and the stator current fault components in both identification and fault-severity. Conclusively, stator current analysis increases reliability in the application as a complete, robust, on-line condition monitoring technology.
Keywords
Condition monitoring, Fault diagnosis, Induction motors, Emulation, Ball bearings, MCSA

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.57 MB

Citation

Please use this url to cite or link to this publication:

MLA
Corne, Bram, et al. “The Reflection of Evolving Bearing Faults in the Stator Current’s Extended Park Vector Approach for Induction Machines.” MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 107, Elsevier, 2018, pp. 168–82, doi:10.1016/j.ymssp.2017.12.010.
APA
Corne, B., Vervisch, B., Derammelaere, S., Knockaert, J., & Desmet, J. (2018). The reflection of evolving bearing faults in the stator current’s extended park vector approach for induction machines. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 107, 168–182. https://doi.org/10.1016/j.ymssp.2017.12.010
Chicago author-date
Corne, Bram, Bram Vervisch, Stijn Derammelaere, Jos Knockaert, and Jan Desmet. 2018. “The Reflection of Evolving Bearing Faults in the Stator Current’s Extended Park Vector Approach for Induction Machines.” MECHANICAL SYSTEMS AND SIGNAL PROCESSING 107: 168–82. https://doi.org/10.1016/j.ymssp.2017.12.010.
Chicago author-date (all authors)
Corne, Bram, Bram Vervisch, Stijn Derammelaere, Jos Knockaert, and Jan Desmet. 2018. “The Reflection of Evolving Bearing Faults in the Stator Current’s Extended Park Vector Approach for Induction Machines.” MECHANICAL SYSTEMS AND SIGNAL PROCESSING 107: 168–182. doi:10.1016/j.ymssp.2017.12.010.
Vancouver
1.
Corne B, Vervisch B, Derammelaere S, Knockaert J, Desmet J. The reflection of evolving bearing faults in the stator current’s extended park vector approach for induction machines. MECHANICAL SYSTEMS AND SIGNAL PROCESSING. 2018;107:168–82.
IEEE
[1]
B. Corne, B. Vervisch, S. Derammelaere, J. Knockaert, and J. Desmet, “The reflection of evolving bearing faults in the stator current’s extended park vector approach for induction machines,” MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 107, pp. 168–182, 2018.
@article{8548104,
  abstract     = {{Stator current analysis has the potential of becoming the most cost-effective condition monitoring technology regarding electric rotating machinery. Since both electrical and mechanical faults are detected by inexpensive and robust current-sensors, measuring current is advantageous on other techniques such as vibration, acoustic or temperature analysis. However, this technology is struggling to breach into the market of condition monitoring as the electrical interpretation of mechanical machine-problems is highly complicated. Recently, the authors built a test-rig which facilitates the emulation of several representative mechanical faults on an 11 kW induction machine with high accuracy and reproducibility. Operating this test-rig, the stator current of the induction machine under test can be analyzed while mechanical faults are emulated. Furthermore, while emulating, the fault-severity can be manipulated adaptively under controllable environmental conditions. This creates the opportunity of examining the relation between the magnitude of the well-known current fault components and the corresponding fault-severity. This paper presents the emulation of evolving bearing faults and their reflection in the Extended Park Vector Approach for the 11 kW induction machine under test. The results confirm the strong relation between the bearing faults and the stator current fault components in both identification and fault-severity. Conclusively, stator current analysis increases reliability in the application as a complete, robust, on-line condition monitoring technology.}},
  author       = {{Corne, Bram and Vervisch, Bram and Derammelaere, Stijn and Knockaert, Jos and Desmet, Jan}},
  issn         = {{0888-3270}},
  journal      = {{MECHANICAL SYSTEMS AND SIGNAL PROCESSING}},
  keywords     = {{Condition monitoring,Fault diagnosis,Induction motors,Emulation,Ball bearings,MCSA}},
  language     = {{eng}},
  pages        = {{168--182}},
  publisher    = {{Elsevier}},
  title        = {{The reflection of evolving bearing faults in the stator current’s extended park vector approach for induction machines}},
  url          = {{http://doi.org/10.1016/j.ymssp.2017.12.010}},
  volume       = {{107}},
  year         = {{2018}},
}

Altmetric
View in Altmetric
Web of Science
Times cited: