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Cited by 216

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Neural networks aided on-line diagnostics of induction motor rotor faults

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Filippetti, F., Franceschini, G., Tassoni, C.·1995·IEEE Transactions on Industry Applications

Abstract

An improvement of induction-machine rotor fault diagnosis based on a neural network approach is presented. A neural network can replace in a more effective way the faulted machine models used to formalize the knowledge base of the diagnostic system with suitably chosen inputs and outputs. Training the neural network by data obtained from experimental tests on healthy machines and from simulation in the case of faulted machines, the diagnostic system can discern between healthy and faulty machines. This procedure replaces the formulation of a trigger threshold, required in the diagnostic procedure based on the machine models. >

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Metadata

Authors
Filippetti, F., Franceschini, G., & Tassoni, C.
Venue
IEEE Transactions on Industry Applications
Published
1995
Volume
31
Issue
4
Pages
892-899
ISSN
0093-9994 (ISSN-L)
OpenAlex
W2166361139
Cited by
216
References
0
FWCI
4.10
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Neural networks aided on-line diagnostics of induction motor rotor faults. https://app.cochat.ai/papers/10.1109/28.395301
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Filippetti, F., Franceschini, G., & Tassoni, C. (1995). Neural networks aided on-line diagnostics of induction motor rotor faults. IEEE Transactions on Industry Applications. https://doi.org/10.1109/28.395301

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.