@inproceedings{4cb8e00953f041a1841a32a5d4fd90d0,
title = "AI-Based Bearing Defect Detection Using Variable Reluctance Sensor Signal",
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.",
keywords = "Bearing Wear Classification, Convolutional Neural Network, GoogleNet, Variable Reluctance, Wear Detection",
author = "Collin Daly and Haddad, {Rami J.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE SoutheastCon, SoutheastCon 2024 ; Conference date: 15-03-2024 Through 24-03-2024",
year = "2024",
doi = "10.1109/SoutheastCon52093.2024.10500113",
language = "English",
series = "Conference Proceedings - IEEE SOUTHEASTCON",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "892--893",
booktitle = "SoutheastCon 2024",
address = "United States",
}