Waveform design for high-resolution damage detection using lamb waves [Correspondence]

2013 ◽  
Vol 60 (5) ◽  
pp. 1025-1029 ◽  
Author(s):  
Liang Zeng ◽  
Jing Lin ◽  
Yaguo Lei ◽  
Hang Xie
Keyword(s):  
High Resolution ◽  
Damage Detection ◽  
Lamb Waves ◽  
Waveform Design

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.

Use of delay and sum for sparse reconstruction improvement for structural health monitoring

2019 ◽  
Vol 30 (18-19) ◽  
pp. 2919-2931 ◽  
Author(s):  
Ali Nokhbatolfoghahai ◽  
Hossein M Navazi ◽  
Roger M Groves
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Guided Waves ◽  
Lamb Waves ◽  
Spot Size ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Structural Health ◽  
Computational Performance

To perform active structural health monitoring, guided Lamb waves for damage detection have recently gained extensive attention. Many algorithms are used for damage detection with guided waves and among them, the delay-and-sum method is the most commonly used algorithm because of its robustness and simplicity. However, delay-and-sum images tend to have poor accuracy with a large spot size and a high noise floor, especially in the presence of multiple damages. To overcome these problems, another method that is based on sparse reconstruction can be used. Although the images produced by the sparse reconstruction method are superior to the conventional delay-and-sum method, it has the challenges of the time and cost of computations in comparison with the delay-and-sum method. Also, in some cases in multi-damage detection, the sparse reconstruction method totally fails. In this article, using prior support information of the structure achieved by the delay-and-sum method, a hybrid method based on sparse reconstruction method is proposed to improve the computational performance and robustness of sparse reconstruction method in the case of multi-damage presence. The effectiveness of the proposed method in detecting damages is demonstrated experimentally and numerically on a simple aluminum plate. The technique is also shown to accurately identify and localize multi-site damages as well as single damage with low sampled signals.

Lamb Waves Signal Analysis in Structural Health Monitoring Systems

2011 ◽  
Vol 368-373 ◽  
pp. 2402-2405
Author(s):  
Nai Zhi Zhao ◽  
Chang Tie Huang ◽  
Xin Chen
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Changes ◽  
Lamb Waves ◽  
Detection Algorithm ◽  
Operational Conditions ◽  
Structural Health ◽  
Detection Algorithms ◽  
Baseline Subtraction

Many of the wave propagation based structural health monitoring techniques rely on some knowledge of the structure in a healthy state in order to identify damage. Baseline measurements are recorded when a structure is pristine and are stored for comparison to future data. A concern with the use of baseline subtraction methods is the ability to discern structural changes from the effects of varying environmental and operational conditions when analyzing the vibration response of a system. The use of a standard baseline subtraction technique may falsely indicate damage when environmental or operational variations are present between baseline measurements and new measurements. A procedure was outlined for the method, including excitation and recording of Lamb waves, and the use of damage detection algorithms. In this paper, several tests are performed and the results are used to help develop the damage detection algorithms previously described, and to evaluate the performance of the instantaneous baseline SHM technique. Analytical testing is first performed by feeding known input signals into each damage detection algorithm and analyzing the output data. The results of the analytical testing are used to help develop the damage detection algorithms.

Damage Detection for 2003 Bam, Iran, Earthquake Using Terra-ASTER Satellite Imagery

2005 ◽  
Vol 21 (1_suppl) ◽  
pp. 267-274 ◽  
Author(s):  
Masayuki Kohiyama ◽  
Fumio Yamazaki
Keyword(s):  
High Resolution ◽  
Damage Detection ◽  
Prior Knowledge ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Ground Surface ◽  
Random Variable ◽  
Surface Change ◽  
High Resolution Satellite Image ◽  
Event Image

The damaged areas of the 2003 Bam, Iran, earthquake were detected using 15-meter-resolution satellite imagery acquired by Terra-ASTER. First, fluctuation of digital numbers was modeled as a normal random variable based on 17 pre-event images on a pixel-by-pixel basis. Then, the deviation value of each digital number in the post-event image was evaluated and converted into the confidence level, which indicates the possibility of an abnormal ground surface change. The detected damaged areas were verified with a high-resolution satellite image and it was observed that the areas with earthquake influence were mostly identified. However, the pixels with significant change were induced not only from heavily damaged buildings but also dusty roads, possibly due to demolition work. It was suggested that prior knowledge like a high-resolution pre-event image would assist the interpretation of the detected result.

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