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Proceedings Paper

Mode extraction on wind turbine blades via phase-based video motion estimation
Author(s): Aral Sarrafi; Peyman Poozesh; Christopher Niezrecki; Zhu Mao
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Paper Abstract

In recent years, image processing techniques are being applied more often for structural dynamics identification, characterization, and structural health monitoring. Although as a non-contact and full-field measurement method, image processing still has a long way to go to outperform other conventional sensing instruments (i.e. accelerometers, strain gauges, laser vibrometers, etc.,). However, the technologies associated with image processing are developing rapidly and gaining more attention in a variety of engineering applications including structural dynamics identification and modal analysis. Among numerous motion estimation and image-processing methods, phase-based video motion estimation is considered as one of the most efficient methods regarding computation consumption and noise robustness. In this paper, phase-based video motion estimation is adopted for structural dynamics characterization on a 2.3-meter long Skystream wind turbine blade, and the modal parameters (natural frequencies, operating deflection shapes) are extracted. Phase-based video processing adopted in this paper provides reliable full-field 2-D motion information, which is beneficial for manufacturing certification and model updating at the design stage. The phase-based video motion estimation approach is demonstrated through processing data on a full-scale commercial structure (i.e. a wind turbine blade) with complex geometry and properties, and the results obtained have a good correlation with the modal parameters extracted from accelerometer measurements, especially for the first four bending modes, which have significant importance in blade characterization.

Paper Details

Date Published: 19 April 2017
PDF: 12 pages
Proc. SPIE 10171, Smart Materials and Nondestructive Evaluation for Energy Systems 2017, 101710E (19 April 2017); doi: 10.1117/12.2260406
Show Author Affiliations
Aral Sarrafi, Univ. of Massachusetts Lowell (United States)
Peyman Poozesh, Univ. of Massachusetts Lowell (United States)
Christopher Niezrecki, Univ. of Massachusetts Lowell (United States)
Zhu Mao, Univ. of Massachusetts Lowell (United States)

Published in SPIE Proceedings Vol. 10171:
Smart Materials and Nondestructive Evaluation for Energy Systems 2017
Norbert G. Meyendorf, Editor(s)

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