An Iterative Signal Fusion Method for Reconstruction of InPlane Strain Maps from Strain Measurements by Hybrid Dense Sensor Networks

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Sadoughi, Mohammadkazem
Downey, Austin
Hu, Chao
Laflamme, Simon
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Mechanical EngineeringCivil, Construction and Environmental EngineeringElectrical and Computer EngineeringCenter for Nondestructive Evaluation (CNDE)

Flexible skin-like membranes have received considerable research interest for the costeffective 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 strain (i.e. εx + εy). It follows that an algorithm is required for the decomposition of the signal into unidirectional strain maps. In this study, a new method enabling such decomposition that leverages 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 method adaptively fuses the different sources of signal information (i.e. from SECs and RSGs) to build the best fit unidirectional strain maps that can model strain. Each step of the ISF contains an update process for strain maps based on the Kriging model. The proposed method is validated using finite element analysis of a cantilever plate in the Abaqus. The results show that ISF outperforms an existing method in most cases.


This is a manuscript of a proceeding published as Mohammadkazem Sadoughi, Austin Downey, Chao Hu, and Simon Laflamme. "An Iterative Signal Fusion Method for Reconstruction of In-Plane Strain Maps from Strain Measurements by Hybrid Dense Sensor Networks", 2018 AIAA Information Systems-AIAA Infotech @ Aerospace, AIAA SciTech Forum, (AIAA 2018-0467). DOI: 10.2514/6.2018-0467. Posted with permission.

Mon Jan 01 00:00:00 UTC 2018