Journal
JOURNAL OF SENSORS
Volume 2016, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2016/6971952
Keywords
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Funding
- National Natural Science Foundation of China [51175080]
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University [sklms2012010]
- Scientific Research Foundation of Graduate School of Southeast University [YBJJ1424]
- Postgraduate Research & Innovation Project of Jiangsu Province
- Fundamental Research Funds for the Central Universities [CXZZ12-0096]
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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 systemverify 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.
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