Articles | Volume 6, issue 2
J. Sens. Sens. Syst., 6, 389–394, 2017

Special issue: Sensor/IRS2 2017

J. Sens. Sens. Syst., 6, 389–394, 2017

Regular research article 19 Dec 2017

Regular research article | 19 Dec 2017

Inverse calculation of strain profiles from ETDR measurements using artificial neural networks

Robin Höhne, Pawel Kostka, and Niels Modler Robin Höhne et al.
  • Institute of Lightweight Engineering and Polymer Technology, Technische Universität Dresden, Holbeinstraße 3, Dresden, Germany

Abstract. A novel carbon fibre sensor is developed for the spatially resolved strain measurement. A unique feature of the sensor is the fibre-break resistive measurement principle and the two-core transmission line design. The electrical time domain reflectometry (ETDR) is used in order to realize a spatially resolved measurement of the electrical parameters of the sensor. In this contribution, the process of mapping between the ETDR signals to the existing strain profile is described. Artificial neural networks (ANNs) are used to solve the inverse electromagnetic problem. The investigations were carried out with a sensor patch in a cantilever arm configuration. Overall, 136 experiments with varying strain distribution over the sensor length were performed to generate the necessary training data to learn the ANN model. The validation of the ANN highlights the feasibility as well as the current limits concerning the quantitative accuracy of mapping ETDR signals to strain profiles.

Short summary
This paper focuses on a novel carbon fibre sensor technology that exploits the low-cost and low-energy electrical reflectometry method for a spatially resolved strain measurement. The application of artificial neural networks for mapping the measured electrical signal to the existing strain profile is demonstrated. The potential and current limits are highlighted. The sensor is a promising part for the next generation of light-weight structures with operando health monitoring systems.
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