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http://dx.doi.org/10.14775/ksmpe.2021.20.09.035

Corrosion Failure Diagnosis of Rolling Bearing with SVM  

Go, Jeong-Il (Department of Mechanical System Engineering, Kumoh National institute of Technology)
Lee, Eui-Young (Department of Mechanical System Engineering, Kumoh National institute of Technology)
Lee, Min-Jae (Department of Mechanical System Engineering, Kumoh National institute of Technology)
Choi, Seong-Dae (Department of Mechanical System Engineering, Kumoh National institute of Technology)
Hur, Jang-Wook (Department of Mechanical System Engineering, Kumoh National institute of Technology)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.20, no.9, 2021 , pp. 35-41 More about this Journal
Abstract
A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.
Keywords
Failure Prognostics; Rolling Bearing; Machine Learning; Failure Mode and Effect Analysis;
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