To avoid direct interpretation of the complicated ultrasonic guided wave signal and the effect of dispersion, probability-based diagnostic imaging is proposed to identify damages for guided wave–based structural health monitoring. However, the probability-based diagnostic imaging algorithm usually requires a relatively large number of actuator–sensor paths to cover the monitoring area. Therefore, a weight-compensated probability-based diagnostic imaging approach is developed to improve the ability of damage localization with higher precision under the same sensor configuration. The weight-compensated probability-based diagnostic imaging algorithm compensates for the weight difference from sensing paths with the unit weight of various positions, inhibited by the path weight difference effect on diagnostic image. The validity of the approach is assessed by identifying damages at different locations with different groups of sensing paths on a stiffened composite panel. Accurate localization results have demonstrated the effectiveness of the developed probability-based diagnostic imaging approach.
ChangFKMarkmillerJFYangJ. Structural health monitoring. In: JohnsonSBGormleyTKesslerS. (eds) System health management: with aerospace applications. Chichester: John Wiley & Sons, Ltd, 2011, pp. 419–428.
2.
BollerCChangFKFujinoY.Encyclopedia of structural health monitoring. Chichester: John Wiley & Sons, Ltd, 2009.
3.
WuZJQingXPGhoshK. Health monitoring of bonded composite repair in bridge rehabilitation. Smart Mater Struct2008; 17: 45–59.
4.
SohnHParkGWaitJR. Wavelet-based active sensing for delamination detection in composite structures. Smart Mater Struct2004; 13: 153–160.
5.
DiligentOLoweMJSCawleyP.Reflection and scattering of the S-0 Lamb mode from 3-D circular defects in plates. In: ThompsonDOChimentiDE (eds) Review of progress in quantitative nondestructive evaluation, vol. 615, Brunswick, Maine, 29 July–3 August 2001, pp. 231–238.
6.
HongMSuZWangQ. Modeling nonlinearities of ultrasonic waves for fatigue damage characterization: theory, simulation, and experimental validation. Ultrasonics2014; 54: 770–778.
7.
DemmaACawleyPLoweM.Scattering of the fundamental shear horizontal mode from steps and notches in plates. J Acoust Soc Am2003; 113: 1880–1891.
8.
SuZYeL.Identification of damage using Lamb waves: from fundamentals to applications. London: Springer, 2009.
9.
QingXLPBeardSJKumarA. Advances in the development of built-in diagnostic system for filament wound composite structures. Compos Sci Technol2006; 66: 1694–1702.
10.
MichaelsJE.Detection, localization and characterization of damage in plates with an in situ array of spatially distributed ultrasonic sensors. Smart Mater Struct2008; 17: 03503501–03503515.
11.
YeLLinYDongW. Damage detection in a large composite panel of five stiffeners using Lamb wave signals. Mater Forum2009; 33: 136–142.
12.
YuLGiurgiutiuV.In-situ optimized PWAS phased arrays for Lamb wave structural health monitoring. J Mech Mater Struct2007; 2: 459–487.
13.
PrasadSMBalasubramaniamKKrishnamurthyCV.Structural health monitoring of composite structures using Lamb wave tomography. Smart Mater Struct2004; 13: 73–79.
14.
BreonLJVan VelsorJKRoseJL.Guided wave damage detection tomography for structural health monitoring in critical zones of pipelines. Mater Eval2007; 65: 1215–1219.
15.
WangCHRoseJTChangFK.A synthetic time-reversal imaging method for structural health monitoring. Smart Mater Struct2004; 13: 415–423.
16.
IhnJBChangFK.Pitch-catch active sensing methods in structural health monitoring for aircraft structures. Struct Health Monit2008; 7: 5–19.
17.
CaiJShiLHYuanSF. High spatial resolution imaging for structural health monitoring based on virtual time reversal. Smart Mater Struct2011; 20: 1–11.
18.
ZhouCSuZChengL.Probability-based diagnostic imaging using hybrid features extracted from ultrasonic Lamb wave signals. Smart Mater Struct2011; 20: 12500501–12500514.
19.
ZhaoXGaoHZhangG. Active health monitoring of an aircraft wing with embedded piezoelectric sensor/actuator network: I—defect detection, localization and growth monitoring. Smart Mater Struct2007; 16: 1208–1217.
20.
KoduruJPRoseJL.Guided wave annular array sensor design for improved tomographic imaging. In: ThompsonDOChimentiDE (eds) Review of progress in quantitative nondestructive evaluation, vol. 28. Chicago, IL: AIP Publishing, 2009, pp. 658–665.
21.
WangDYeLLuY. Probability of the presence of damage estimated from an active sensor network in a composite panel of multiple stiffeners. Compos Sci Technol2009; 69: 2054–2063.
22.
WangDYeLSuZQ. Probabilistic damage identification based on correlation analysis using guided wave signals in aluminum plates. Struct Health Monit2010; 9: 133–144.
23.
WuZLiuKWangY. Validation and evaluation of damage identification using probability-based diagnostic imaging on a stiffened composite panel. J Intel Mat Syst Str2014; 26: 2181–2195.
24.
WangDYeLLuY. A damage diagnostic imaging algorithm based on the quantitative comparison of Lamb wave signals. Smart Mater Struct2010; 19: 065008.
25.
IhnJBChangFK.Detection and monitoring of hidden fatigue crack growth using a built-in piezoelectric sensor/actuator network: I—diagnostics. Smart Mater Struct2004; 13: 609–620.
26.
QiuLLiuMLQingXL. A quantitative multidamage monitoring method for large-scale complex composite. Struct Health Monit2013; 12: 183–196.
27.
BeardSLiuC-CChangF-K. Design of a robust SHM system for composite structures. In: Industrial and commercial applications of smart structures technologies 2007, San Diego, CA, 19–20 March 2007, p. 652709. Bellingham, WA: SPIE.
