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Motor bearing damage detection using stator current monitoring
Abstract
This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. Vibration monitoring of mechanical bearing frequencies is currently used to detect the presence of a fault condition. Since these mechanical vibrations are associated with variations in the physical air gap of the machine, the air gap flux density is modulated and stator currents are generated at predictable frequencies related to the electrical supply and vibrational frequencies. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by correlating the relationship between vibration and current frequencies caused by incipient bearing failures. The bearing failure modes are reviewed and the characteristic bearing frequencies associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and the related frequencies determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. Experimental results which show the vibration and current spectra of an induction machine with different bearing faults are used to verify the relationship between the vibrational and current frequencies. The test results clearly illustrate that the stator current signature can be used to identify the presence of a bearing fault.>
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Citations
Cited by
Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review
Nandi, S., Toliyat, H. A., & Li, X. (2005) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2005.847955 · Cited by 2,382
Advances in Diagnostic Techniques for Induction Machines
Bellini, A., Filippetti, F., Tassoni, C., & Capolino, G.-A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2008.2007527 · Cited by 1,140
A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
Rai, A., & Upadhyay, S. H. (2016) · Tribology International · doi:10.1016/j.triboint.2015.12.037 · Cited by 804
A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier
Eren, L., Ince, T., & Kiranyaz, S. (2019) · Journal of Signal Processing Systems · doi:10.1007/s11265-018-1378-3 · Cited by 723
Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition
Cusid�Cusido, J., Romeral, L., Ortega, J. A., Rosero, J. A., & Garc�aGarcia Espinosa, A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2007.911960 · Cited by 512
Metadata
- Authors
- Schoen, R. R., Habetler, T. G., Kamran, F., & Bartheld, R. G.
- Venue
- Proceedings of 1994 IEEE Industry Applications Society Annual Meeting
- Publisher
- IEEE
- Published
- 2002-12-17
- Pages
- 110-116
- ISSN
- 0197-2618 (ISSN-L)
- OpenAlex
- W2109725353
- Cited by
- 638
- References
- 0
- FWCI
- 35.45
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Motor bearing damage detection using stator current monitoring. https://app.cochat.ai/papers/10.1109/ias.1994.345491
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- Schoen, R. R., Habetler, T. G., Kamran, F., & Bartheld, R. G. (2002). Motor bearing damage detection using stator current monitoring. Proceedings of 1994 IEEE Industry Applications Society Annual Meeting. https://doi.org/10.1109/ias.1994.345491
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