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Local Regularity Analysis with Wavelet Transform in Gear Tooth Failure Detection Cover

Local Regularity Analysis with Wavelet Transform in Gear Tooth Failure Detection

By: Juhani Nissilä  
Open Access
|Aug 2017

Abstract

Diagnosing gear tooth and bearing failures in industrial power transition situations has been studied a lot but challenges still remain. This study aims to look at the problem from a more theoretical perspective. Our goal is to find out if the local regularity i.e. smoothness of the measured signal can be estimated from the vibrations of epicyclic gearboxes and if the regularity can be linked to the meshing events of the gear teeth. Previously it has been shown that the decreasing local regularity of the measured acceleration signals can reveal the inner race faults in slowly rotating bearings. The local regularity is estimated from the modulus maxima ridges of the signal’s wavelet transform. In this study, the measurements come from the epicyclic gearboxes of the Kelukoski water power station (WPS). The very stable rotational speed of the WPS makes it possible to deduce that the gear mesh frequencies of the WPS and a frequency related to the rotation of the turbine blades are the most significant components in the spectra of the estimated local regularity signals.

DOI: https://doi.org/10.1515/mspe-2017-0026 | Journal eISSN: 2450-5781 | Journal ISSN: 2299-0461
Language: English
Page range: 176 - 182
Submitted on: Oct 1, 2016
Accepted on: Apr 1, 2017
Published on: Aug 1, 2017
Published by: STE Group sp. z.o.o.
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2017 Juhani Nissilä, published by STE Group sp. z.o.o.
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.