Bridges, skyscrapers, blast furnaces, and other industrial structures, requires continuous monitoring in order to prevent serious accidents caused by structural health degradation due to various factors. Acoustic Emission (AE) testing represents an effective non-destructive technique for Structural Health Monitoring (SHM), and allows us to investigate and evaluate the failure processes of different structural components. Nowadays, this technique is frequently required for maintenance. This paper presents a novel Data Acquisition (DAQ) hardware architecture which can make easier the AE instrumentation design for SHM to be Internet-of-Things (IoT) integrated. The technical proposal of this DAQ architecture is flexible due to the: (i) on-board digital signal processing, (ii) local Decision Support System (DDS), and (iii) capability of synchronization and triggering of multiple AE sensors.

A Flexible DAQ Hardware Architecture using SoCs for IoT based Structural Health Monitoring Systems

Tudosa I.
;
Picariello F.;Balestrieri E.;Lamonaca F.
2019-01-01

Abstract

Bridges, skyscrapers, blast furnaces, and other industrial structures, requires continuous monitoring in order to prevent serious accidents caused by structural health degradation due to various factors. Acoustic Emission (AE) testing represents an effective non-destructive technique for Structural Health Monitoring (SHM), and allows us to investigate and evaluate the failure processes of different structural components. Nowadays, this technique is frequently required for maintenance. This paper presents a novel Data Acquisition (DAQ) hardware architecture which can make easier the AE instrumentation design for SHM to be Internet-of-Things (IoT) integrated. The technical proposal of this DAQ architecture is flexible due to the: (i) on-board digital signal processing, (ii) local Decision Support System (DDS), and (iii) capability of synchronization and triggering of multiple AE sensors.
2019
978-1-7281-0429-4
Data Acquisition System; Instrumentation; Internet-of-Things; Measurement; Structural Health Monitoring; System-on-Chip
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/43818
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