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http://dx.doi.org/10.5050/KSNVE.2013.23.4.347

Health Monitoring Method for Monopile Support Structure of Offshore Wind Turbine Using Committee of Neural Networks  

Lee, Jong Won (Namseoul University)
Kim, Sang Ryul (Korea Institute of Machinery and Materials)
Kim, Bong Ki (Korea Institute of Machinery and Materials)
Lee, Jun Shin (Korea Electric Power Research Institute)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.23, no.4, 2013 , pp. 347-355 More about this Journal
Abstract
A damage estimation method for monopile support structure of offshore wind turbine using modal properties and committee of neural networks is presented for effective structural health monitoring. An analytical model for a monopile support structure is established, and the natural frequencies, mode shapes, and mode shape slopes for the support structure are calculated considering soil condition and added mass. The input to the neural networks consists of the modal properties and the output is composed of the stiffness indices of the support structure. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated stiffness indices from different neural networks are averaged. Ten damage cases are estimated using the proposed method, and the identified damage locations and severities agree reasonably well with the exact values. The accuracy of the estimation can be improved by applying the committee of neural networks which is a statistical approach averaging the damage indices in the functional space.
Keywords
Damage Estimation; Support Structure; Offshore Wind Turbine; Committee of Neural Networks; Structural Health Monitoring;
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Times Cited By KSCI : 1  (Citation Analysis)
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