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Cited by 544

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors

Gandhi, Arun, Corrigan, Timothy, Parsa, Leila·2011·IEEE Transactions on Industrial Electronics
DOI 10.1109/tie.2010.2089937
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Abstract

Online fault diagnosis plays a crucial role in providing the required fault tolerance to drive systems used in safety-critical applications. Short-circuit faults are among the common faults occurring in electrical machines. This paper presents a review of existing techniques available for online stator interturn fault detection and diagnosis (FDD) in electrical machines. Special attention is given to short-circuit-fault diagnosis in permanent-magnet machines, which are fast replacing traditional machines in a wide variety of applications. Recent techniques that use signals analysis, models, or knowledge-based systems for FDD are reviewed in this paper. Motor current is the most commonly analyzed signal for fault diagnosis. Hence, motor current signature analysis is a topic of elaborate discussion in this paper. Additionally, parametric and finite-element models that were designed to simulate interturn-fault conditions are reviewed.

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Metadata

Authors
Gandhi, A., Corrigan, T., & Parsa, L.
Venue
IEEE Transactions on Industrial Electronics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2011-05
Volume
58
Issue
5
Pages
1564-1575
ISSN
  • 0278-0046 (ISSN-L)
  • 1557-9948
DOI
10.1109/tie.2010.2089937
OpenAlex
W2145223406
Cited by
544
References
1
FWCI
43.02
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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CoChat. (2026). Citation check: Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors. https://app.cochat.ai/papers/10.1109/tie.2010.2089937
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Gandhi, A., Corrigan, T., & Parsa, L. (2011). Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2010.2089937

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