Abstract

High-frequency voltages across the steel ball bearings and the corresponding currents can cause premature bearing failures in electric machines driven by PWM converters. The bearing voltage, one of the most commonly-used failure indicators, depends heavily on the bearing capacitance. This paper presents a combined numerical and experimental approach for the calculation of ball bearing capacitances to address the uncertainty introduced by lubricant property, lubrication status and other metal parts, such as seals and ball retainers. Based on the obtained capacitance breakdown, the influences of temperature, speed and bearing load (radial, axial or combined) on the capacitance are studied. Measurements and associated results of bearing capacitances are provided to validate the proposed method.

Document Type

Conference Proceeding

Publication Date

10-2020

Notes/Citation Information

Published in 2020 IEEE Energy Conversion Congress and Exposition (ECCE).

© 2020 IEEE Copyright Notice. “Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

The document available for download is the authors’ manuscript version that is accepted for publication. The final published version is copyrighted by IEEE and available as: P. Han, G. Heins, D. Patterson, M. Thiele, and D.M. Ionel, “Combined numerical and experimental determination of ball bearing capacitances for bearing current prediction,” 2020 IEEE Energy Conversion Congress and Exposition (ECCE), Detroit, MI, Oct. 11-15, 2020, pp. 5590-5594, doi: 10.1109/ECCE44975.2020.9235700.

Digital Object Identifier (DOI)

https://doi.org/10.1109/ECCE44975.2020.9235700

Funding Information

The support of Regal Beloit Corporation, University of Kentucky, the L. Stanley Pigman Endowment, and ANSYS Inc., is gratefully acknowledged.

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