19 April 2017 Optimal statistical damage detection and classification in an experimental wind turbine blade using minimum instrumentation
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Abstract
The increasing demand for carbon neutral energy in a challenging economic environment is a driving factor for erecting ever larger wind turbines in harsh environments using novel wind turbine blade (WTBs) designs characterized by high flexibilities and lower buckling capacities. To counteract resulting increasing of operation and maintenance costs, efficient structural health monitoring systems can be employed to prevent dramatic failures and to schedule maintenance actions according to the true structural state. This paper presents a novel methodology for classifying structural damages using vibrational responses from a single sensor. The method is based on statistical classification using Bayes’ theorem and an advanced statistic, which allows controlling the performance by varying the number of samples which represent the current state. This is done for multivariate damage sensitive features defined as partial autocorrelation coefficients (PACCs) estimated from vibrational responses and principal component analysis scores from PACCs. Additionally, optimal DSFs are composed not only for damage classification but also for damage detection based on binary statistical hypothesis testing, where features selections are found with a fast forward procedure. The method is applied to laboratory experiments with a small scale WTB with wind-like excitation and non-destructive damage scenarios. The obtained results demonstrate the advantages of the proposed procedure and are promising for future applications of vibration-based structural health monitoring in WTBs.
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Simon Hoell, Piotr Omenzetter, "Optimal statistical damage detection and classification in an experimental wind turbine blade using minimum instrumentation", Proc. SPIE 10171, Smart Materials and Nondestructive Evaluation for Energy Systems 2017, 101710D (19 April 2017); doi: 10.1117/12.2257228; https://doi.org/10.1117/12.2257228
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