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Structural Health Monitoring of Wind Turbine Blades: Acoustic Source Localization Using Wireless Sensor Networks

Lookup NU author(s): Omar Bouzid, Professor Gui Yun TianORCiD, Dr Kanapathippillai Cumanan

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


Abstract

Structural health monitoring (SHM) is important for reducing the maintenance and operation cost of safety-critical components and systems in offshore wind turbines. This paper proposes an in situ wireless SHM system based on an acoustic emission (AE) technique. By using this technique a number of challenges are introduced due to high sampling rate requirements, limitations in the communication bandwidth, memory space, and power resources. To overcome these challenges, this paper focused on two elements: (1) the use of an in situ wireless SHM technique in conjunction with the utilization of low sampling rates; (2) localization of acoustic sources which could emulate impact damage or audible cracks caused by different objects, such as tools, bird strikes, or strong hail, all of which represent abrupt AE events and could affect the structural health of a monitored wind turbine blade. The localization process is performed using features extracted from aliased AE signals based on a developed constraint localization model. To validate the performance of these elements, the proposed system was tested by testing the localization of the emulated AE sources acquired in the field.


Publication metadata

Author(s): Bouzid OM, Tian GY, Cumanan K, Moore D

Publication type: Article

Publication status: Published

Journal: Journal of Sensors

Year: 2015

Volume: 2015

Print publication date: 01/01/2015

Acceptance date: 15/12/2014

Date deposited: 18/09/2015

ISSN (print): 1687-725X

ISSN (electronic): 1687-7268

Publisher: Hindawi Publishing Corporation

URL: http://dx.doi.org/10.1155/2015/139695

DOI: 10.1155/2015/139695


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Funding

Funder referenceFunder name
269202FP7 HEMOW Project, "Health Monitoring of Offshore Wind Farms" (FP7-PEOPLE-IRSES)

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