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An unsupervised, on-line system for induction motor fault detection using stator current monitoring
Abstract
A new method for online induction motor fault detection is presented in this paper. This system utilizes artificial neural networks to learn the spectral characteristics of a good motor operating online. This learned spectrum may contain many harmonics due to the load which correspond to normal operating conditions. In order to reduce the number of harmonics which are continuously monitored to a manageable number, a selective frequency filter is employed. This frequency filter only passes those harmonics which are known to be of importance in fault detection, or which are continuously above a set level, to a neural net clustering algorithm. After a sufficient training period, the neural network signals a potential failure condition when a new cluster is formed and persists for some time. Since a fault condition is found by comparison to a prior condition of the machine, online failure prediction is possible with this system without requiring information on the motor or load characteristics. The detection algorithm was implemented and its performance verified on various fault types.
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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
Grubic, S., Aller, J. M., Lu, B., & Habetler, T. G. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2008.2004665 · Cited by 490
Self-citationKim, K.-H. (2011) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2010.2060463 · Cited by 184
Comprehensive review on inter‐turn fault indexes in permanent magnet motors
Faiz, J., Nejadi‐Koti, H., & Valipour, Z. (2017) · IET Electric Power Applications · doi:10.1049/iet-epa.2016.0196 · Cited by 86
Study on the Permanent Magnet Demagnetization Fault in Permanent Magnet Synchronous Machines
Rosero, J. A., Cusido, J., Garcia, A., Ortega, J. A., & Romeral, L. (2006) · IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics · doi:10.1109/iecon.2006.347598 · Cited by 67
Metadata
- Authors
- Schoen, R. R., Lin, B. K., Habetler, T. G., Schlag, J. H., & Farag, S.
- Venue
- IEEE Transactions on Industry Applications
- Published
- 1995
- Volume
- 31
- Issue
- 6
- Pages
- 1280-1286
- ISSN
- 0093-9994 (ISSN-L)
- OpenAlex
- W4238387670
- Cited by
- 239
- References
- 0
- FWCI
- 2.46
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: An unsupervised, on-line system for induction motor fault detection using stator current monitoring. https://app.cochat.ai/papers/10.1109/28.475698
- The paper
- Schoen, R. R., Lin, B. K., Habetler, T. G., Schlag, J. H., & Farag, S. (1995). An unsupervised, on-line system for induction motor fault detection using stator current monitoring. IEEE Transactions on Industry Applications. https://doi.org/10.1109/28.475698
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