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An Enhanced Empirical Wavelet Transform and Its contribution to Features Extraction from Wind Turbine Condition Monitoring Signals

Lookup NU author(s): Dr Pu Shi, Dr Wenxian YangORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Influenced by the constantly varying loading and operational conditions, feature extraction from nonlinear and non-stationary (NNS) wind turbine (WT) condition monitoring (CM) signals is challenging. Previously, much effort has been spent to develop advanced signal processing techniques for dealing with the CM signals of this kind. The Empirical Wavelet Transform (EWT) is one of the achievements attributed to these efforts. The EWT takes the advantages of Empirical Mode Decomposition (EMD) in dealing with NNS signals, but is superior to the EMD in mode decomposition and robustness against noise. However, the conventional EWT meets difficulty in properly segmenting the frequency spectrum of the signal, which would significantly lower the accuracy of the EWT result. To address this issue, an enhanced EWT is proposed in this paper by developing a feasible and efficient spectrum segmentation method. The effectiveness of the proposed method has been verified by using the bearing and gearbox condition monitoring data that are open to public for the purpose of research. Experiment has shown that after adopting the proposed method, it becomes much easier and more confident to segment the frequency spectrum of the signal. Moreover, benefited from the correct segmentation of the signal spectrum, the fault-related features of the CM signals are presented more explicitly in the time-frequency map by the enhanced EWT despite the considerable noise contained in the signal and lack of knowledge about the machine being investigated.

Publication metadata

Author(s): Shi P, Yang W, Sheng M, Wang M

Publication type: Article

Publication status: Published

Journal: Energies

Year: 2017

Volume: 10

Issue: 7

Online publication date: 11/07/2017

Acceptance date: 05/07/2017

Date deposited: 08/07/2017

ISSN (electronic): 1996-1073

Publisher: MDPI AG


DOI: 10.3390/en10070972


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