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Shock and Vibration
Volume 2016, Article ID 6748469, 10 pages
http://dx.doi.org/10.1155/2016/6748469
Research Article

Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

1EPC Elektroprivreda BiH, Kreka Coal Mines, 75000 Tuzla, Bosnia and Herzegovina
2Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
3Bruel and Kjær Vibro, 2850 Nærum, Denmark
4Technical University of Denmark, 2800 Lyngby, Denmark

Received 1 July 2015; Revised 17 September 2015; Accepted 27 September 2015

Academic Editor: Chao Tao

Copyright © 2016 Rusmir Bajric et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.