Articles | Volume 6, issue 2
J. Sens. Sens. Syst., 6, 389–394, 2017
https://doi.org/10.5194/jsss-6-389-2017

Special issue: Sensor/IRS2 2017

J. Sens. Sens. Syst., 6, 389–394, 2017
https://doi.org/10.5194/jsss-6-389-2017

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 et al.

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Cited articles

Baviskar, S. and Heimovaara, T.: Quantification of soil water retention parameters using multi-section TDR-waveform analysis, J. Hydrol., 549, 404–415, https://doi.org/10.1016/j.jhydrol.2017.03.068, 2017.
Coccorese, E., Martone, R., and Morabito, F. C.: A neural network approach for the solution of electric and magnetic inverse problems, IEEE T. Magn., 30, 2829–2839, https://doi.org/10.1109/20.312527, 1994.
Höhne, R., Kostka, P., and Modler, N.: Cyclic testing of novel carbon fiber based strain sensor with spatial resolution, 17th European Conference on Composite Materials, Munich, 2016.
Höhne, R., Ehrig, T., Kostka, P., and Modler, N.: Phenomenological investigation of a carbon fiber based strain sensor with spatial resolution by means of time domain reflectometry, Materialwiss. Werkst., 47, 1024–1033, 2017a.
Höhne, R., Kostka, P., and Modler, N.: Characterization of the spatial resolution capability of a novel carbon fiber strain sensor based on characteristic impedance measurements, Proceedings Sensor 2017, 166–171, 2017b.
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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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