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      A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach

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          Abstract

          The complex dynamics of operational wind turbine (WT) structures challenges the applicability of existing structural health monitoring (SHM) strategies for condition assessment. At the center of Europe’s renewable energy strategic planning, WT systems call for implementation of strategies that may describe the WT behavior in its complete operational spectrum. The framework proposed in this paper relies on the symbiotic treatment of acting environmental/operational variables and the monitored vibration response of the structure. The approach aims at accurate simulation of the temporal variability characterizing the WT dynamics, and subsequently at the tracking of the evolution of this variability in a longer-term horizon. The bi-component analysis tool is applied on long-term data, collected as part of continuous monitoring campaigns on two actual operating WT structures located in different sites in Germany. The obtained data-driven structural models verify the potential of the proposed strategy for development of an automated SHM diagnostic tool.

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          Most cited references39

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          The Homogeneous Chaos

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            The Orthogonal Development of Non-Linear Functionals in Series of Fourier-Hermite Functionals

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              Condition monitoring and fault detection of wind turbines and related algorithms: A review

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                30 March 2017
                April 2017
                : 17
                : 4
                : 720
                Affiliations
                [1 ]Faculty of Civil Engineering, University Ss. Cyril and Methodius, Skopje 1000, Macedonia; simona.bogoevska@ 123456gf.ukim.edu.mk (S.B.); dumova@ 123456gf.ukim.edu.mk (E.D.-J.)
                [2 ]Department of Civil, Environmental and Geomatic Engineering, ETH, Zürich CH-8093, Switzerland; mspyridonakos@ 123456gmail.com
                [3 ]Department of Civil and Environmental Engineering, Ruhr-University Bochum, Bochum 44801, Germany; ruediger.hoeffer@ 123456ruhr-uni-bochum.de
                Author notes
                [* ]Correspondence: chatzi@ 123456ibk.baug.ethz.ch ; Tel.: +41-44-633-6755
                Article
                sensors-17-00720
                10.3390/s17040720
                5421680
                28358346
                1aab106d-a8a1-430a-b0db-2e95cdf8f163
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 31 January 2017
                : 21 March 2017
                Categories
                Article

                Biomedical engineering
                wind turbines,data-driven framework,uncertainty propagation,operational spectrum,time varying autoregressive moving average (tv-arma) models,polynomial chaos expansion (pce)

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