The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIX-B2-2026
https://doi.org/10.5194/isprs-archives-XLIX-B2-2026-175-2026
https://doi.org/10.5194/isprs-archives-XLIX-B2-2026-175-2026
23 Jul 2026
 | 23 Jul 2026

Non-Contact Modal Analysis of Wind Turbine Blades Using Terrestrial Laser Scanner

Martina Goering

Keywords: TLS, profile mode, wind turbine, rotor blade, eigenfrequencies, modal shapes

Abstract. This contribution presents a methodology for non-contact, marker-free modal analysis of wind turbine blades using terrestrial laser scanning (TLS). The approach aims to determine key modal properties, such as natural frequencies and mode shapes, which are essential for assessing structural behaviour and service life. The methodology is systematically evaluated using simulated data, laboratory experiments, and full-scale field measurements. Simulations are used to analyse the influence of measurement noise and sampling rate, demonstrating that dominant frequencies can be identified with an accuracy of approximately 0.1 Hz. In addition, the first two bending mode shapes are reliably reconstructed, confirming the robustness of the segment-based processing workflow. In laboratory experiments, TLS and photogrammetry are used to capture vibrations of a 4 m long test object. Photogrammetric data, based on 3D coordinates of circular markers, serve as a reference for frequency identification using Fast Fourier Transform (FFT). TLS data are processed segment-wise, consistent with the field application, and show good agreement with the reference measurements. The method is subsequently applied to a full-scale rotor blade (88 m) in a field experiment. TLS profile measurements are transformed into a blade-aligned coordinate system and analysed to determine eigenfrequencies and mode shapes along the blade span. The results demonstrate that TLS enables reliable identification of dominant modal properties and provides a cost-effective alternative to conventional sensor-based approaches, with strong potential for practical applications in wind turbine monitoring.

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