Reconstruction of unidirectional strain maps via iterative signal fusion for mesoscale structures monitored by a sensing skin

Flexible skin-like membranes have received considerable research interest for the cost-effective monitoring of mesoscale (large-scale) structures. The authors have recently proposed a large-area electronic consisting of a soft elastomeric capacitor (SEC) that transduces a structure's change in geometry (i.e. strain) into a measurable change in capacitance. The SEC sensor measures the summation of the orthogonal strains (i.e. εx+εy). It follows that an algorithm is required for the decomposition of the sensor signal into unidirectional strain maps. In this study, a new method enabling such decomposition, leveraging a dense sensor network of SECs and resistive strain gauges (RSGs), is proposed. This method, termed iterative signal fusion (ISF), combines the large-area sensing capability of SECs and the high-precision sensing capability of RSGs. The proposed ISF method adaptively fuses the different sources of signal information (e.g. from SECs and RSGs) to build a structure's best fit unidirectional strain maps. Each step of ISF contains an update process for strain maps based on the Kriging model. To demonstrate the accuracy of the proposed method, an experimental test bench is developed, which is the largest deployment of the SEC-based sensing skin to date in terms of both size and sensor count. A network of 40 SECs deployed on a grid (5 × 8) is utilized and an optimal sensor placement algorithm is used to select the optimal RSG sensor locations within the network of SECs. Results show that the proposed ISF method is capable of reconstructing unidirectional strain maps for the experimental test plate. In addition to the experimental data, a numerical validation for the ISF method is provided through a finite element analysis model of the experimental test bench.