The paper illustrates the technique to handle optical signals coming from optical fiber lines equipped with Fiber Bragg Grating (FBG) strain sensors. The case of interest is that of vibration monitoring of very large structures equipped with Glass Fibers Reinforced Polymers (GFRP) with an embedded set of optical fiber sensors. Due to the production process of such elements, it is difficult to locate precisely the FBG along the reinforcement bar, therefore it is difficult to recover the actual position of the measurement points knowing the acquired strain signals only. A technique for the identification of the placements of these sensors is here proposed, which relies on two main algorithms. The first one is based on a modal approximation of the elastic shape of the beam, allowing a theorical estimation of the sensor placements; the second algorithm is based on an Artificial Neural Network, which is able to predict the robustness of the previous estimation. Numerical results are shown to illustrate the methodology.
Embedded optical sensors for vibration monitoring of large structures / Pinto, M; Roveri, N; Pepe, G; Nicoletti, A; Balconi, G; Carcaterra, A. - (2018), pp. 3875-3886. (Intervento presentato al convegno 28th International conference on noise and vibration engineering, ISMA 2018 and 7th International conference on uncertainty in structural dynamics, USD 2018 tenutosi a Leuven; Belgium).
Embedded optical sensors for vibration monitoring of large structures
Pinto, M
;Roveri, N;Pepe, G;Carcaterra, A
2018
Abstract
The paper illustrates the technique to handle optical signals coming from optical fiber lines equipped with Fiber Bragg Grating (FBG) strain sensors. The case of interest is that of vibration monitoring of very large structures equipped with Glass Fibers Reinforced Polymers (GFRP) with an embedded set of optical fiber sensors. Due to the production process of such elements, it is difficult to locate precisely the FBG along the reinforcement bar, therefore it is difficult to recover the actual position of the measurement points knowing the acquired strain signals only. A technique for the identification of the placements of these sensors is here proposed, which relies on two main algorithms. The first one is based on a modal approximation of the elastic shape of the beam, allowing a theorical estimation of the sensor placements; the second algorithm is based on an Artificial Neural Network, which is able to predict the robustness of the previous estimation. Numerical results are shown to illustrate the methodology.File | Dimensione | Formato | |
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