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Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique
Abstract
The presence of electrical and mechanical faults in the induction motors (IMs) can be detected by analysis of the stator current spectrum. However, when an IM is fed by a frequency converter, the spectral analysis of stator current signal becomes difficult. For this reason, the monitoring must depend on multiple signatures in order to reduce the effect of harmonic disturbance on the motor-phase current. The aim of this paper is the description of a new approach for fault detection and diagnosis of IMs using signal-based method. It is based on signal processing and an unsupervised classification technique called the artificial ant clustering. The proposed approach is tested on a squirrel-cage IM of 5.5 kW in order to detect broken rotor bars and bearing failure at different load levels. The experimental results prove the efficiency of our approach compared with supervised classification methods in condition monitoring of electrical machines.
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Cited by
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Khan, S., & Yairi, T. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.024 · Cited by 1,133
Leite, V. C. M. N., Borges da Silva, J. G., Veloso, G. F. C., Borges da Silva, L. E., Lambert-Torres, G., Bonaldi, E. L., & de Lacerda de Oliveira, L. E. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2345330 · Cited by 274
Giantomassi, A., Ferracuti, F., Iarlori, S., Ippoliti, G., & Longhi, S. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2370936 · Cited by 168
Hasan, M. J., & Kim, J.-M. (2018) · Applied Sciences · doi:10.3390/app8122357 · Cited by 105
Model-Based Fault Detection and Isolation Scheme for a Rudder Servo System
Xu, Q.-N., Lee, K.-M., Zhou, H., & Yang, H.-Y. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2361795 · Cited by 71
Metadata
- Authors
- Soualhi, A., Clerc, G., & Razik, H.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2013-09
- Volume
- 60
- Issue
- 9
- Pages
- 4053-4062
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2063492964
- Cited by
- 264
- References
- 0
- FWCI
- 27.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- Soualhi, A., Clerc, G., & Razik, H. (2013). Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2012.2230598
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