The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
An unsupervised, on-line system for induction motor fault detection using stator current monitoring
Abstract
A new method for on-line 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 on-line. This learned spectrum may contain many harmonics due to the load which corresponds 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 a relative comparison to a good condition, on-line failure prediction is possible with this 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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Cites
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Neural Networks: Algorithms, Applications and Programming Techniques.
Leave, C., Freeman, J. A., & Skapura, D. M. (1992) · The Journal of the Operational Research Society · doi:10.2307/2584109 · Cited by 924
Motor bearing damage detection using stator current monitoring
Schoen, R. R., Habetler, T. G., Kamran, F., & Bartheld, R. G. (2002) · Proceedings of 1994 IEEE Industry Applications Society Annual Meeting · doi:10.1109/ias.1994.345491 · Cited by 638
Self-citationNeural networks aided on-line diagnostics of induction motor rotor faults
Filippetti, F., Franceschini, G., & Tassoni, C. (1995) · IEEE Transactions on Industry Applications · doi:10.1109/28.395301 · Cited by 216
Filippetti, F., Martelli, M., Franceschini, G., et al. (2003) · doi:10.1109/ias.1992.244459 · Cited by 149
Diagnosis of rotating machines by utilizing a backpropagation neural net
Nam, K., & Lee, S. (2003) · doi:10.1109/iecon.1992.254465 · Cited by 12
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
A review of induction motors signature analysis as a medium for faults detection
El Hachemi Benbouzid, M. (2000) · IEEE Transactions on Industrial Electronics · doi:10.1109/41.873206 · Cited by 1,602
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.06.004 · Cited by 1,594
Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review
Zhang, S., Zhang, S., Wang, B., & Habetler, T. G. (2020) · IEEE Access · doi:10.1109/access.2020.2972859 · Cited by 913
Self-citationFrom Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis
Dai, X., & Gao, Z. (2013) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2013.2243743 · Cited by 728
Metadata
- Authors
- Schoen, R. R., Lin, B. K., Habetler, T. G., Schlag, J. H., & Farag, S.
- Venue
- Proceedings of 1994 IEEE Industry Applications Society Annual Meeting
- Publisher
- IEEE
- Published
- 2002-12-17
- Pages
- 103-109
- ISSN
- 0197-2618 (ISSN-L)
- OpenAlex
- W2119811001
- Cited by
- 264
- References
- 6
- FWCI
- 33.41
- Type
- conference paper
- 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/ias.1994.345492
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- Schoen, R. R., Lin, B. K., Habetler, T. G., Schlag, J. H., & Farag, S. (2002). An unsupervised, on-line system for induction motor fault detection using stator current monitoring. Proceedings of 1994 IEEE Industry Applications Society Annual Meeting. https://doi.org/10.1109/ias.1994.345492
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