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Shock and Vibration
Volume 2016 (2016), Article ID 2562949, 13 pages
http://dx.doi.org/10.1155/2016/2562949
Research Article

Damage Detection and Localization from Dense Network of Strain Sensors

1Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
2Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA
3Institute of Structural Engineering, Department of Civil, Environmental & Geomatic Engineering, ETH Zürich, 8093 Zürich, Switzerland
4Department of Civil and Environmental Engineering, University of Perugia, 06125 Perugia, Italy

Received 2 July 2015; Accepted 27 September 2015

Academic Editor: Sakdirat Kaewunruen

Copyright © 2016 Simon Laflamme et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Structural health monitoring of large systems is a complex engineering task due to important practical issues. When dealing with large structures, damage diagnosis, localization, and prognosis necessitate a large number of sensors, which is a nontrivial task due to the lack of scalability of traditional sensing technologies. In order to address this challenge, the authors have recently proposed a novel sensing solution consisting of a low-cost soft elastomeric capacitor that transduces surface strains into measurable changes in capacitance. This paper demonstrates the potential of this technology for damage detection, localization, and prognosis when utilized in dense network configurations over large surfaces. A wind turbine blade is adopted as a case study, and numerical simulations demonstrate the effectiveness of a data-driven algorithm relying on distributed strain data in evidencing the presence and location of damage, and sequentially ranking its severity. Numerical results further show that the soft elastomeric capacitor may outperform traditional strain sensors in damage identification as it provides additive strain measurements without any preferential direction. Finally, simulation with reconstruction of measurements from missing or malfunctioning sensors using the concepts of virtual sensors and Kriging demonstrates the robustness of the proposed condition assessment methodology for sparser or malfunctioning grids.