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Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring

Lookup NU author(s): Dr Wenxian YangORCiD


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Accessing difficulties and harsh environments require more advanced condition monitoring techniques to ensure the high availability of offshore wind turbines. Empirical mode decomposition (EMD) has been shown to be a promising technique for meeting this need. However, EMD was developed for one-dimensional signals, unable to carry out an information fusion function which is of importance to reach a reliable condition monitoring conclusion. Therefore, bivariate empirical mode decomposition (BEMD) is investigated in this paper to assess whether it could be a better solution for wind turbine condition monitoring. The effectiveness of the proposed technique in detecting machine incipient fault is compared with EMD and a recently developed wavelet-based ‘energy tracking’ technique. Experiments have shown that the proposed BEMD-based technique is more convenient than EMD for processing shaft vibration signals, and more powerful than EMD and wavelet-based techniques in terms of processing the non-stationary and nonlinear wind turbine condition monitoring signals and detecting incipient mechanical and electrical faults.

Publication metadata

Author(s): Yang W, Court R, Tavner P, Crabtree C

Publication type: Article

Publication status: Published

Journal: Journal of Sound and Vibration

Year: 2011

Volume: 330

Issue: 15

Pages: 3766-3782

Print publication date: 18/07/2011

ISSN (print): 0022-460X

ISSN (electronic): 1095-8568

Publisher: Elsevier Ltd.


DOI: 10.1016/j.jsv.2011.02.027


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