An analysis of influencing factors for structural damage imaging using Lamb waves

2012 ◽  
Vol 131 (4) ◽  
pp. 3443-3443
Author(s):  
Haiyan Zhang
Keyword(s):  
Influencing Factors ◽  
Structural Damage ◽  
Lamb Waves ◽  
Damage Imaging

scholarly journals Structural damage imaging based on time-reversal theory for focusing of Lamb waves

2010 ◽  
Vol 59 (10) ◽  
pp. 7111
Author(s):  
Zhang Hai-Yan ◽  
Sun Xiu-Li ◽  
Cao Ya-Ping ◽  
Chen Xian-Hua ◽  
Yu Jian-Bo
Keyword(s):  
Time Reversal ◽  
Structural Damage ◽  
Lamb Waves ◽  
Reversal Theory ◽  
Damage Imaging

scholarly journals Damage Identification of Large Generator Stator Insulation Based on PZT Sensor Systems and Hybrid Features of Lamb Waves

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2745 ◽  
Author(s):  
Ruihua Li ◽  
Hao Li ◽  
Bo Hu
Keyword(s):  
Power Systems ◽  
Lamb Wave ◽  
Damage Identification ◽  
Lamb Waves ◽  
Fem Simulation ◽  
Correlation Coefficients ◽  
Hybrid Features ◽  
Material Inhomogeneity ◽  
Time Frequency ◽  
Damage Imaging

Large generators are the principal pieces of equipment in power systems, and their operation reliability critically depends on the stator insulation. Damages in stator insulation will gradually lead to the failure and breakdown of generator. Due to the advantages of Lamb waves in Structural health monitoring (SHM), in this study, a distributed piezoelectric (PZT) sensor system and hybrid features of the Lamb waves are introduced to identify stator insulation damage of large generator. A hierarchical probability damage-imaging (PDI) algorithm is proposed to tackle the material inhomogeneity and anisotropy of the stator insulation. The proposed method includes three steps: global detection using correlation coefficients, local detection using Time of flight (ToF) along with the amplitude of damage-scattered Lamb wave, and final images fusion. Wavelet Transform was used to extract the ToF of Lamb wave in terms of the time-frequency domain. Finite Element Modeling (FEM) simulation and experimental work were carried out to identify four typical stator insulation damages for validation, including inner void, inner delamination, puncture, and crack. Results show that the proposed method can precisely identify the location of stator insulation damage, and the reconstruction image can be used to identify the size of stator insulation damage.

A Method of Dispersion Compensation Based on Warped Frequency Transform

2013 ◽  
Vol 718-720 ◽  
pp. 2062-2067 ◽  
Author(s):  
Shang Chen Fu ◽  
Zhen Jian Lv ◽  
Ding Ma ◽  
Li Hua Shi
Keyword(s):  
High Resolution ◽  
Velocity Dispersion ◽  
Lamb Waves ◽  
Group Velocity Dispersion ◽  
Dispersion Compensation ◽  
Imaging Method ◽  
Finite Element Software ◽  
Damage Imaging ◽  
Resolution Imaging ◽  
Multi Mode

The use of Lamb waves for structural health monitoring (SHM) has complicated by its multi-mode character and dispersion effect, which impacts the damage positioning and high-resolution imaging. The group velocity dispersion curves of Lamb waves can be employed to warp the frequency axis, and then to establish warped frequency transform (WFT) to process Lamb waves. In this paper, received signals are directly compensated with warped frequency transform to suppress dispersion, and a new imaging method is proposed based on warped frequency transform. The propagation of Lamb waves in damaged aluminum plate is simulated by finite element software ABAQUS, results show that warped frequency transform can effectively compensate dispersive wave-packets, and high-resolution damage imaging can be obtained by the proposed method.

Least-squares reverse time migration (LSRTM) for damage imaging using Lamb waves

2019 ◽  
Vol 28 (6) ◽  
pp. 065010 ◽  
Author(s):  
Jiaze He ◽  
Daniel C Rocha ◽  
Patrick E Leser ◽  
Paul Sava ◽  
William P Leser
Keyword(s):  
Least Squares ◽  
Lamb Waves ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Damage Imaging

The use of time reversal methods with Lamb waves to identify structural damage in a pipeline system

2006 ◽  
Author(s):  
Andrew B. Thien ◽  
Anthony D. Puckett ◽  
Gyuhae Park ◽  
Charles R. Farrar
Keyword(s):  
Time Reversal ◽  
Structural Damage ◽  
Lamb Waves ◽  
Pipeline System ◽  
Use Of Time

Damage imaging using Lamb waves for SHM applications

2015 ◽  
Author(s):  
Tadeusz Stepinski ◽  
Łukasz Ambroziński ◽  
Tadeusz Uhl
Keyword(s):  
Lamb Waves ◽  
Damage Imaging

A time reversal damage imaging method for structure health monitoring using Lamb waves

2010 ◽  
Vol 19 (11) ◽  
pp. 114301 ◽  
Author(s):  
Hai-Yan Zhang ◽  
Ya-Ping Cao ◽  
Xiu-Li Sun ◽  
Xian-Hua Chen ◽  
Jian-Bo Yu
Keyword(s):  
Health Monitoring ◽  
Time Reversal ◽  
Lamb Waves ◽  
Imaging Method ◽  
Structure Health Monitoring ◽  
Damage Imaging

Guided Lamb Wave Interrogation of a Curved Composite Plate [0/90] Using the Hilbert-Huang Transform Approach

2008 ◽  
Author(s):  
Byungseok Yoo ◽  
Darryll Pines ◽  
Ashish S. Purekar
Keyword(s):  
Structural Damage ◽  
Lamb Waves ◽  
Damage Index ◽  
Composite Panel ◽  
Progressive Damage ◽  
Damage Growth ◽  
Monitoring Tool ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Torque Load

This paper discusses a study of the Hilbert-Huang transform approach as a structural health monitoring tool for a curved composite panel with a [90/0/0/90]sym lay-up sequence. The Hilbert amplitude, phase, and frequency coupled with empirical mode decomposition (EMD) process are defined as the Hilbert-Huang transform (HHT) and used to evaluate structural damage in the curved composite panel as the level of damage increases. Piezoelectric (PZT) sheets are used as an actuator and receivers, akin to the pitch-catch method to excite the guided Lamb waves and to obtain the vibratory response of the curved composite panel. In this paper, a new damage metric using the Hilbert-Huang transformed energy and amplitude in conjunction with the Hilbert-Huang transformed phase are proposed and investigated as a new structural damage index. Results show that these damage metrics are capable of tracking progressive damage growth in the form of loss of torque load.

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