Algorithm for damage detection in wind turbine blades using a hybrid dense sensor network with feature level data fusion
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.