Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.755167
Title: Imaging-based fault detection of wind turbines
Author: Yu, Songyang
ISNI:       0000 0004 7428 1650
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2018
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Abstract:
With the development of renewable energy, the wind-energy generation is no longer a brand-new field. Considering the complex work environment and huge maintenance fee, windmill detection plays a significant role in the wind industry. Therefore, combining with the application of digital image technology in windmill in recent years, the thesis proposes a new non-destructive detection method based on digital image process algorithms, including Optical Intensity for frequency and cycle time measurement, Frame Difference for motion tracking, and EVM (Eulerian Video Magnification) for invisible motion enhancement.
Supervisor: Zhang, Yang Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.755167  DOI: Not available
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