In this work, we propose an innovative, low-cost and low-weight Sensor Node (SN) which can yield fast deployable sensor networks for long–term and real–time acoustic emission (AE) monitoring. Thanks to the interface and architecture of the developed system, Digital Signal Processing (DSP) functionalities were embedded in the proposed SN which are exploited for AE features extraction and signal characterization, compliant with a complete Structural Monitoring analysis. The on–board DSP strategy, performed in close proximity to AE transducers at monitoring facility, permits to significantly minimize the sensitivity to external noise which is the key reason for the poor signal-to-noise ratio of AE equipment in situ. The results of versatile testing experimental campaign demonstrated the applicability of the developed low-cost AE prototype to confidently detect and monitor AE activity in various materials.

Acoustic emission structural monitoring through low-cost sensor nodes

Denis Bogomolov;Nicola Testoni;Federica Zonzini;Michelangelo Maria Malatesta;Luca de Marchi;Alessandro Marzani
2021

Abstract

In this work, we propose an innovative, low-cost and low-weight Sensor Node (SN) which can yield fast deployable sensor networks for long–term and real–time acoustic emission (AE) monitoring. Thanks to the interface and architecture of the developed system, Digital Signal Processing (DSP) functionalities were embedded in the proposed SN which are exploited for AE features extraction and signal characterization, compliant with a complete Structural Monitoring analysis. The on–board DSP strategy, performed in close proximity to AE transducers at monitoring facility, permits to significantly minimize the sensitivity to external noise which is the key reason for the poor signal-to-noise ratio of AE equipment in situ. The results of versatile testing experimental campaign demonstrated the applicability of the developed low-cost AE prototype to confidently detect and monitor AE activity in various materials.
International Conference on Structural Health Monitoring of Intelligent Infrastructure
383
388
Denis Bogomolov, Nicola Testoni, Federica Zonzini, Michelangelo Maria Malatesta, Luca de Marchi, Alessandro Marzani
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/856381
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