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Journal of Sensors
Volume 2016 (2016), Article ID 6971952, 14 pages
http://dx.doi.org/10.1155/2016/6971952
Research Article

Gearbox Fault Diagnosis in a Wind Turbine Using Single Sensor Based Blind Source Separation

School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China

Received 28 January 2015; Accepted 19 March 2015

Academic Editor: Mehmet Karakose

Copyright © 2016 Yuning Qian and Ruqiang Yan. 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

This paper presents a single sensor based blind source separation approach, namely, the wavelet-assisted stationary subspace analysis (WSSA), for gearbox fault diagnosis in a wind turbine. Continuous wavelet transform (CWT) is used as a preprocessing tool to decompose a single sensor measurement data into a set of wavelet coefficients to meet the multidimensional requirement of the stationary subspace analysis (SSA). The SSA is a blind source separation technique that can separate the multidimensional signals into stationary and nonstationary source components without the need for independency and prior information of the source signals. After that, the separated nonstationary source component with the maximum kurtosis value is analyzed by the enveloping spectral analysis to identify potential fault-related characteristic frequencies. Case studies performed on a wind turbine gearbox test system verify the effectiveness of the WSSA approach and indicate that it outperforms independent component analysis (ICA) and empirical mode decomposition (EMD), as well as the spectral-kurtosis-based enveloping, for wind turbine gearbox fault diagnosis.