Hyperspectral imaging for erosion detection in wind turbine blades
Young, Andrew and Kay, Andy and Marshall, Stephen and Torr, Ralph and Gray, Alison; (2016) Hyperspectral imaging for erosion detection in wind turbine blades. In: Proceedings of HSI 2016, 12-13th October 2016. UNSPECIFIED, GBR.
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Abstract
Inspection of wind turbine blades is required to identify any defects or failures and decide on any remedial actions e.g. blade repair or replacement. Traditionally, inspections have been performed by rope access technicians who visually inspect the blades and record damage using standard photographic equipment. Recent developments have seen an increase in popularity in the use of remote based inspection techniques using ground mounted cameras and cameras installed on Remotely Operated Aerial Vehicles, more commonly referred to as drones. Whilst these techniques remove the need for human access to the blades, imaging is performed remotely and does not always provide adequate image quality using standard high definition cameras. As a result, there is a growing interest in imaging techniques based on other regions of the electromagnetic spectrum. Laboratory and field based trials are required to properly examine this potential and understand which frequencies can be applied to imaging blades. This paper demonstrates a Hyperspectral Imaging technique in its application to imaging surface defects on a section of wind turbine blade in a laboratory.
ORCID iDs
Young, Andrew ORCID: https://orcid.org/0000-0001-6338-6631, Kay, Andy, Marshall, Stephen ORCID: https://orcid.org/0000-0001-7079-5628, Torr, Ralph and Gray, Alison ORCID: https://orcid.org/0000-0002-6273-0637;-
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Item type: Book Section ID code: 58291 Dates: DateEvent12 October 2016Published26 August 2016Accepted10 June 2016SubmittedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
Faculty of Science > Mathematics and StatisticsDepositing user: Pure Administrator Date deposited: 26 Oct 2016 11:48 Last modified: 11 Nov 2024 15:05 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/58291