Jul 2026· Journal of Civil Structural Health Monitoring· Vol 16· 0 citations· 41 references
Abstract
This study investigates the dynamic behaviour of an onshore wind turbine tower throughout the entire assembly process, with particular emphasis on the challenge of identifying closely spaced modes in operational modal analysis. A comprehensive measurement campaign, involving up to 23 accelerometers, was conducted to capture vibration responses under environmental excitation across seven construction stages. Modal parameters and their associated uncertainties were identified using the covariance-driven stochastic subspace identification (SSI-COV) within a robust operational modal analysis scheme. A novel uncertainty-based mode selection approach was introduced and applied to reliably extract the modes of the first two bending mode pairs. Additionally, a bending mode indicator was developed to assess the purity of the identified bending shapes. The results show that the natural frequencies of the first bending mode pair decrease continuously as construction progresses. A similar trend was observed for the fore-aft mode of the second bending mode pair, while the side-to-side mode remained largely unaffected. Modal contribution analysis reveals that, particularly in the later assembly stages, the structural dynamics are dominated by the first bending mode pair. These findings highlight the effectiveness of the uncertainty-based mode selection framework and provide new insights into the dynamics of wind turbine towers during assembly. The results support model validation for structural health monitoring and may inform future design and construction practices.
The damping ratio is the modal parameter that is hardest to identify reliably and exhibits the largest scatter in structural health monitoring and operational modal analysis; a trustworthy baseline relies on multi-estimator cross-validation and uncertainty quantification. Measured damping data for high-rise reinforced-...
Dong Lan, Gui-Lian Deng, Yang-Kai Ou· Measurement and control (Lon...· 0 citations
This study presents a high-fidelity, simulation-based Finite Element – Operational Modal Analysis – Machine Learning (FE–OMA–ML) framework for damage diagnosis of a jacket-type offshore wind turbine support structure. Finite-element-simulated acceleration responses are processed using Enhanced Frequency-Domain Decomp...
Hao-Yan Wu, F. Pugliese, Jessica Christie et al.· Scientific Reports· 0 citations
The carbody commonly exhibits a low first-order diamond modal frequency, degrading ride comfort and operational safety of the railway vehicle. Existing optimization methods yield results that are often difficult to implement directly under standardized manufacturing constraints, and discrete dynamic optimization is pro...
Chengyu Song, Yaohui Lu, Fei Huang et al.· Proceedings of the Instituti...· 0 citations
In the identification of railway bridges using train-induced vibrations, moving heavy train mass continuously affects the measured responses, making the modal parameters of the train–bridge system time-varying. A method is developed to identify such time-varying modes, utilizing small overlapping time windows sweepin...
Adrita Kundu, A. Pal, S. Mukhopadhyay· Journal of engineering mecha...· 0 citations
The accurate identification of random dynamic loads under thermal–mechanical coupling is essential for ensuring the structural reliability and safety of aircraft structures. This paper presents a thermally corrected load identification method that integrates power-exponent-weighted regularization with finite element mo...
Qixiao Zhu, Rui-Guo Zhu, Ye Yuan et al.· Journal of Aircraft· 0 citations
Two data-driven modal analysis approaches, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), are applied to analyze the unsteady flow obtained by solving the Reynolds-averaged Navier–Stokes (RANS) equations in a 1.5-stage axial turbine. The reduced-order reconstructed pressure, dominant mode s...
Ya-Lu Zhu, Feng Liu· AIAA Journal· 1 citation
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