Hyperspectral imaging for erosion detection in wind turbine blades

Andrew Young, Andy Kay, Stephen Marshall, Ralph Torr, Alison Gray

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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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.
Original languageEnglish
Title of host publicationProceedings of HSI 2016, 12-13th October 2016
Subtitle of host publicationHyperspectral Imaging and Applications Conference
Place of PublicationEngland
Number of pages2
Publication statusPublished - 12 Oct 2016
EventHyperspectral Imaging and Applications Conference - Ricoh Arena, Coventry, United Kingdom
Duration: 12 Oct 201613 Oct 2016

Conference

ConferenceHyperspectral Imaging and Applications Conference
Abbreviated titleHSI 2016
CountryUnited Kingdom
CityCoventry
Period12/10/1613/10/16

Keywords

  • hyperspectral
  • Image processing
  • condition monitoring
  • renewable energy
  • leading edge erosion
  • classification

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  • Cite this

    Young, A., Kay, A., Marshall, S., Torr, R., & Gray, A. (2016). Hyperspectral imaging for erosion detection in wind turbine blades. In Proceedings of HSI 2016, 12-13th October 2016: Hyperspectral Imaging and Applications Conference