THERMAL SCIENCE

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Yalcin Cekic

BEARING FAULT DETECTION BY FOUR-BAND WAVELET PACKET DECOMPOSITION

ABSTRACT
Bearing problems are by far the biggest cause of induction motor failures in the industry. Since induction machines are used heavily by the industry, their unexpected failure may disturb the production process. Motor condition monitoring (MCM) is employed widely to avoid such unexpected failures. The data that can be obtained from induction machines are nonstationary by nature since the loading may vary during their operation. Wavelet packet decomposition seems to better handle non-stationary nature of induction machines, the use of this method in monitoring applications is limited, since the computational complexity is higher than other methods. In this work four-band wavelet packet decomposition of motor vibration data is proposed to reduce the computational complexity without compromising accuracy. The proposed method is very suitable for parallel computational processing by its nature, and as a result it is predicted that the calculation time will be shortened further if FPGA is used in design.
KEYWORDS
bearing fault detection, four-band wavelet packet decomposition, computational complexity
PAPER SUBMITTED: 2018-09-27
PAPER REVISED: 2018-10-18
PAPER ACCEPTED: 2018-11-20
PUBLISHED ONLINE: 2018-12-16
DOI REFERENCE: https://doi.org/10.2298/TSCI180927333C
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2019, VOLUME 23, ISSUE Supplement 1, PAGES [S91 - S98]
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Bearing fault detection by four-band wavelet packet decomposition

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence