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Precise feature extraction from wind turbine condition monitoring signals by using optimised variational mode decomposition

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



This is the authors' accepted manuscript of an article that has been published in its final definitive form by IET, 2017.

For re-use rights please refer to the publisher's terms and conditions.


Reliable condition monitoring (CM) highly relies on the correct extraction of fault-related features from CM signals. This equally applies to the CM of wind turbines (WTs). But influenced by slowly rotating speeds and constantly varying loading, extracting fault characteristics from lengthy, nonlinear, non-stationary WT CM signals is extremely difficult, which makes WT CM one of the most challenge tasks in wind power asset management despites that lots of efforts have been spent. Attributed to the superiorities to Empirical Mode Decomposition (EMD) and its extension form Hilbert-Huang Transform (HHT) in dealing with nonlinear, non-stationary CM signals, the recently developed Variational Mode Decomposition (VMD) casts a glimmer of light for the solution for this issue. However, the original proposed VMD adopts default values for both number of modes and filter frequency bandwidth. It is not adaptive to the signal being inspected. As a consequence, it would lead to inaccurate feature extraction thus unreliable WT CM result sometimes. For this reason, a precise feature extraction method based on optimised VMD is investigated in this paper. The experiments have shown that thanks to the use of the proposed optimisation strategies, the fault-related features buried in WT CM signals have been extracted out successfully.

Publication metadata

Author(s): Shi P, Yang W

Publication type: Article

Publication status: Published

Journal: IET Renewable Power Generation

Year: 2017

Volume: 11

Issue: 3

Pages: 245-252

Print publication date: 22/02/2017

Online publication date: 12/10/2016

Acceptance date: 03/10/2016

Date deposited: 13/10/2016

ISSN (print): 1752-1416

ISSN (electronic): 1752-1424

Publisher: IET


DOI: 10.1049/iet-rpg.2016.0716


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