Acousto-ultrasonics-based health monitoring for nano-engineered composites using a dispersive graphene-networked sensing system
Autor(en): |
Yehai Li
Kai Wang Qiang Wang Jianwei Yang Pengyu Zhou Yiyin Su Shifeng Guo Zhongqing Su |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Structural Health Monitoring, März 2020, n. 1, v. 20 |
Seite(n): | 240-254 |
DOI: | 10.1177/1475921720929749 |
Abstrakt: |
Sensing is a fundamental yet crucial part of a functional structural health monitoring system. Substantial research has been invested in developing new sensing techniques to enhance sensing efficiency and accuracy. Practical applications of structural health monitoring approaches to real engineering structures require strict criteria for the sensing system (e.g. weight, position, intrusion and endurance), which challenge existing sensing techniques. The boom in nanotechnology has offered promising solutions for the development of new sensing approaches. However, a bottleneck still exists when considering the density of sensors and surface-mounted modality of installation. In this study, graphene nanoparticles are dispersed into a glass fibre/epoxy composite to form a dispersive network sensing system. The piezoresistivity of the graphene-formed network changes locally as a result of the change of inter-nanoparticle distances which triggers the ‘tunnelling effect’ and drives the sensor to respond to propagating elastic waves. Due to the dense graphene network formed within the composite, only a small area is required, functioning as a single sensing element to capture ultrasonic waves. To validate such capability, passive acoustic emission tests and active guided ultrasonic wave tests are performed individually. The graphene-networked sensing system can precisely capture wave signals which contain effective features to identify impact spot or damage location. Integrating passive graphene-formed network and active lead zirconate titanate wafers can form a dense network, capable of fulfilling general structural health monitoring tasks. |
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Datenseite - Reference-ID
10562452 - Veröffentlicht am:
11.02.2021 - Geändert am:
19.02.2021