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Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

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Blodt, M., Granjon, P., Raison, B., Rostaing, G.·2008·IEEE Transactions on Industrial Electronics

Abstract

This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

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Metadata

Authors
Blodt, M., Granjon, P., Raison, B., & Rostaing, G.
Venue
IEEE Transactions on Industrial Electronics
Published
2008-04
Volume
55
Issue
4
Pages
1813-1822
ISSN
0278-0046 (ISSN-L)
OpenAlex
W2099017769
Cited by
568
References
0
FWCI
39.49
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring. https://app.cochat.ai/papers/10.1109/tie.2008.917108
The paper
Blodt, M., Granjon, P., Raison, B., & Rostaing, G. (2008). Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2008.917108

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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.