AI-Based Bearing Defect Detection Using Variable Reluctance Sensor Signal

Authors

• Collin Daly, Georgia Southern University
• Rami J. Haddad, Georgia Southern UniversityFollow

Document Type

Conference Proceeding

Publication Date

3-15-2024

Publication Title

SoutheastCon 2024 Proceedings

DOI

10.1109/SoutheastCon52093.2024.10500113

ISBN

9798350317107

Abstract

This paper presents a method for the detection of bearing wear and the prediction of bearing failure within a rotational assembly, utilizing commonly employed sensors. In the context of a fluid measurement device, a straightforward variable reluctance sensor is employed to register the passage of a turbine vane, ensuring volumetric measurement. The proposed methodology harnesses neural networks to classify various types of bearing damage by analyzing the raw sensor output signal. The results showcased herein underscore a remarkable level of accuracy, even when applied to a relatively constrained data set.

Comments

Georgia Southern University faculty member, Rami J. Haddad co-authored "AI-Based Bearing Defect Detection Using Variable Reluctance Sensor Signal."

Recommended Citation

Daly, Collin, Rami J. Haddad. 2024. "AI-Based Bearing Defect Detection Using Variable Reluctance Sensor Signal." SoutheastCon 2024 Proceedings: 892-893: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/SoutheastCon52093.2024.10500113 isbn: 9798350317107
https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/181

Copyright

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