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

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 dat...

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Bibliographic Details
Published in:Journal of sensors 2016-01, Vol.2016 (2016), p.1-14
Main Authors: Qian, Yuning, Yan, Ruqiang
Format: Article
Language:English
Subjects:
Decomposition
Fault diagnosis
Gearboxes
Sensors
Signal processing
Spectra
Subspaces
Wind turbines
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Summary: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.
ISSN:1687-725X
1687-7268
DOI:10.1155/2016/6971952