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Induction motor fault diagnosis based on neuropredictors and wavelet signal processing
Abstract
Early detection and diagnosis of incipient faults is desirable for online condition assessment, product quality assurance and improved operational efficiency of induction motors running off power supply mains. In this paper, a model-based fault diagnosis system is developed for induction motors, using recurrent dynamic neural networks for transient response prediction and multi-resolution signal processing for nonstationary signal feature extraction. In addition to nameplate information required for the initial setup, the proposed diagnosis system uses measured motor terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through staged motor faults of electrical and mechanical origin. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2-, 373-, and 597-kW induction motors. Incremental tuning is used to adapt the diagnosis system during commissioning on a new motor, significantly reducing the system development time.
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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 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
A Review of Stator Fault Monitoring Techniques of Induction Motors
Siddique, A., Yadava, G. S., & Singh, B. (2005) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2004.837304 · Cited by 591
Bearing fault detection by one-dimensional convolutional neural networks
Eren, L. (2017) · Mathematical Problems in Engineering · doi:10.1155/2017/8617315 · Cited by 250
Metadata
- Authors
- Kim, K., & Parlos, A. G.
- Venue
- IEEE/ASME Transactions on Mechatronics
- Published
- 2002-06
- Volume
- 7
- Issue
- 2
- Pages
- 201-219
- ISSN
- 1083-4435 (ISSN-L)
- OpenAlex
- W2154383858
- Cited by
- 212
- References
- 0
- FWCI
- 15.97
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kim, K., & Parlos, A. G. (2002). Induction motor fault diagnosis based on neuropredictors and wavelet signal processing. IEEE/ASME Transactions on Mechatronics. https://doi.org/10.1109/tmech.2002.1011258
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