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.
SuZYeL. Identification of damage using Lamb waves: from fundamentals to applications. New York: Springer Science & Business Media, 2009.
2.
DiamantiKSoutisCHodgkinsonJM. Piezoelectric transducer arrangement for the inspection of large composite structures. Compos A2007; 38: 1121–1130.
3.
AzarbayejaniMEl-OseryAIChoiKK, et al. A probabilistic approach for optimal sensor allocation in structural health monitoring. Smart Mater Struct2008; 17: 055019.
4.
StaszewskiWBollerCTomlinsonGR. Health monitoring of aerospace structures: smart sensor technologies and signal processing. Chichester: John Wiley & Sons, 2004.
5.
PagetC. Active health monitoring of aerospace composite structures by embedded piezoceramic transducers. Stockholm: Institutionen för flygteknik, 2001.
6.
WuZQingXPChangF-K. Damage detection for composite laminate plates with a distributed hybrid PZT/FBG sensor network. J Intell Mater Syst Struct2009; 20: 1069–1077.
7.
TakagakiKMinakuchiSTakedaN. Process-induced strain and distortion in curved composites. Part I: development of fiber-optic strain monitoring technique and analytical methods. Compos Part A2017; 103: 236–251.
8.
KhaliliPCawleyP. Relative ability of wedge-coupled piezoelectric and meander coil EMAT probes to generate single-mode Lamb waves. IEEE Trans Ultrason Ferroelectr Freq Control2018; 65: 648–656.
9.
RatnamDBalasubramaniamKMaxfieldBW. Generation and detection of higher-order mode clusters of guided waves (HOMC-GW) using meander-coil EMATs. IEEE Trans Ultrason Ferroelectr Freq Control2012; 59: 727–737.
10.
RaoJSainiAYangJ, et al. Ultrasonic imaging of irregularly shaped notches based on elastic reverse time migration. NDT & E Int2019; 107: 102135.
11.
YuXFanZPuliyakoteS, et al. Remote monitoring of bond line defects between a composite panel and a stiffener using distributed piezoelectric sensors. Smart Mater Struct2018; 27: 035014.
12.
LiaoYZhouPPanD, et al. An ultra-thin printable nanocomposite sensor network for structural health monitoring. Struct Health Monit. Epub ahead of print 1 July 2019. DOI: 10.1177/1475921719859338.
13.
ZhouPLiaoYLiY, et al. An inkjet-printed, flexible, ultra-broadband nanocomposite film sensor for in-situ acquisition of high-frequency dynamic strains. Compos A2019; 125: 105554.
14.
QiuLLiuBYuanS, et al. Impact imaging of aircraft composite structure based on a model-independent spatial-wavenumber filter. Ultrasonics2016; 64: 10–24.
15.
YuanSLaiXZhaoX, et al. Distributed structural health monitoring system based on smart wireless sensor and multi-agent technology. Smart Mater Struct2005; 15: 1–8.
16.
LynchJPLohKJ. A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vibrat Digest2006; 38: 91–130.
17.
KudelaPOstachowiczWŻakA. Damage detection in composite plates with embedded PZT transducers. Mech Syst Signal Pr2008; 22: 1327–1335.
18.
TangH-YWinkelmannCLestariW, et al. Composite structural health monitoring through use of embedded PZT sensors. J Intell Mater Syst Struct2011; 22: 739–755.
19.
ParkGFarrarCRdi ScaleaFL, et al. Performance assessment and validation of piezoelectric active-sensors in structural health monitoring. Smart Mater Struct2006; 15: 1673–1683.
20.
HanLWangXDSunY. The effect of bonding layer properties on the dynamic behaviour of surface-bonded piezoelectric sensors. Int J Solids Struct2008; 45: 5599–5612.
21.
PagetCLevinKDelebarreC. Behavior of an embedded piezoceramic transducer for Lamb wave generation in mechanical loading. In: Proceedings of SPIE, Newport Beach, CA, 22 June 2000.
22.
CampanellaCECuccovilloACampanellaC, et al. Fibre Bragg grating based strain sensors: review of technology and applications. Sensors2018; 18: 3115.
23.
TakedaNOkabeYMizutaniT. Damage detection in composites using optical fibre sensors. Proc Inst Mech Eng Part G J Aerosp Eng2007; 221: 497–508.
24.
OstachowiczWGüemesA. New trends in structural health monitoring. Vienna: Springer, 2016.
25.
LohKJHouT-CLynchJP, et al. Carbon nanotube sensing skins for spatial strain and impact damage identification. J Nondestruct Eval2009; 28: 9–25.
26.
NaghashpourAVan HoaS. A technique for real-time detecting, locating, and quantifying damage in large polymer composite structures made of carbon fibers and carbon nanotube networks. Struct Health Monit2015; 14: 35–45.
27.
TallmanTNWangKW. Damage and strain identification in multifunctional materials via electrical impedance tomography with constrained sine wave solutions. Struct Health Monit2016; 15: 235–244.
28.
GarcíaDTrendafilovaIInmanDJ. A study on the vibration-based self-monitoring capabilities of nano-enriched composite laminated beams. Smart Mater Struct2016; 25: 045011.
29.
LiYWangKSuZ. Dispersed sensing networks in nano-engineered polymer composites: from static strain measurement to ultrasonic wave acquisition. Sensors2018; 18: 1398.
30.
ZengZLiuMXuH, et al. Ultra-broadband frequency responsive sensor based on lightweight and flexible carbon nanostructured polymeric nanocomposites. Carbon2017; 121: 490–501.
XuBYuLGiurgiutiuV. Advanced methods for time-of-flight estimation with application to Lamb wave structural health monitoring. In: Proceedings of the 7th international workshop on structural health monitoring, Palo Alto, CA, 9–11 September 2009.
33.
GiurgiutiuV. Coupling of PWAS transducers to the monitored structure. In: GiurgiutiuV (ed.) Structural health monitoring with piezoelectric wafer active sensors. 2nd ed. Oxford: Academic Press, 2014, pp. 395–443.
34.
LiYLiaoYSuZ. Graphene-functionalized polymer composites for self-sensing of ultrasonic waves: an initiative towards ‘sensor-free’ structural health monitoring. Compos Sci Technol2018; 168: 203–213.
QiuLLiuMQingX, et al. A quantitative multidamage monitoring method for large-scale complex composite. Struct Health Monit2013; 12: 183–196.
37.
GuanRLuYWangK, et al. Fatigue crack detection in pipes with multiple mode nonlinear guided waves. Struct Health Monit2019; 18: 180–192.
38.
GiurgiutiuV. Wave tuning with piezoelectric wafer active sensors. In: GiurgiutiuV (ed.) Structural health monitoring with piezoelectric wafer active sensors. 2nd ed. Oxford: Academic Press, 2014, pp. 573–637.
39.
XuHZengZWuZ, et al. Broadband dynamic responses of flexible carbon black/poly (vinylidene fluoride) nanocomposites: a sensitivity study. Compos Sci Technol2017; 149: 246–253.
40.
QiuLYuanSZhangX, et al. A time reversal focusing based impact imaging method and its evaluation on complex composite structures. Smart Mater Struct2011; 20: 105014.
41.
WangQHongMSuZ. An in-situ structural health diagnosis technique and its realization via a modularized system. IEEE Trans Instrum Meas2015; 64: 873–887.
42.
SohnHLimHJDeSimioMP, et al. Nonlinear ultrasonic wave modulation for online fatigue crack detection. J Sound Vib2014; 333: 1473–1484.
43.
ZhaoXGaoHZhangG, et al. Active health monitoring of an aircraft wing with embedded piezoelectric sensor/actuator network: I. Defect detection, localization and growth monitoring. Smart Mater Struct2007; 16: 1218–1225.
44.
HongMMaoZToddMD, et al. Uncertainty quantification for acoustic nonlinearity parameter in Lamb wave-based prediction of barely visible impact damage in composites. Mech Syst Signal Pr2017; 82: 448–460.
45.
ChandrasekaranSSatoNTölleF, et al. Fracture toughness and failure mechanism of graphene based epoxy composites. Compos Sci Technol2014; 97: 90–99.
46.
JiaJDuXChenC, et al. 3D network graphene interlayer for excellent interlaminar toughness and strength in fiber reinforced composites. Carbon2015; 95: 978–986.
