Campus Units

Civil, Construction and Environmental Engineering, Electrical and Computer Engineering, Mechanical Engineering, Center for Nondestructive Evaluation (CNDE)

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

9-2017

Journal or Book Title

Journal of Wind Engineering and Industrial Aerodynamics

Volume

168

First Page

288

Last Page

296

DOI

10.1016/j.jweia.2017.06.016

Abstract

Damage detection in wind turbine blades requires the ability to distinguish local faults over a global area. The implementation of dense sensor networks provides a solution to this local-global monitoring challenge. Here the authors propose a hybrid dense sensor network consisting of capacitive-based thin-film sensors for monitoring the additive strain over large areas and fiber Bragg grating sensors for enforcing boundary conditions. This hybrid dense sensor network is leveraged to derive a data-driven damage detection and localization method for wind turbine blades. In the proposed method, the blade's complex geometry is divided into less geometrically complex sections. Orthogonal strain maps are reconstructed from the sectioned hybrid dense sensor network by assuming different bidirectional shape functions and are solved using the least squares estimator. The error between the estimated strain maps and measured strains is extracted to define damage detection features that are dependent on the selected shape functions. This technique fuses sensor data into a single damage detection feature, providing a simple and robust method for inspecting large numbers of sensors without the need for complex model driven approaches. Numerical simulations demonstrate the proposed method's capability to distinguish healthy sections from possibly damaged sections on simplified 2D geometries.

Research Focus Area

Geotechnical/Materials Engineering

Comments

This is a manuscript of an article published as Downey, Austin, Filippo Ubertini, and Simon Laflamme. "Algorithm for damage detection in wind turbine blades using a hybrid dense sensor network with feature level data fusion." Journal of Wind Engineering and Industrial Aerodynamics 168 (2017): 288-296. DOI: 10.1016/j.jweia.2017.06.016. Posted with permission.

Copyright Owner

Elsevier Ltd.

Language

en

File Format

application/pdf

Published Version

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