Extension of Lamb Waves Defect Location Techniques to the Case of Low Power Excitation by Compressing Chirped Interrogating Pulses

2013 ◽  
Vol 569-570 ◽  
pp. 940-947
Author(s):  
Luca de Marchi ◽  
Nicola Testoni ◽  
Alessandro Perelli ◽  
Alessandro Marzani

In this work a signal processing algorithm for Lamb waves based defect detection/localization procedures is proposed. In particular, the proposed signal processing allows active-passive networks of piezosensors to use chirp shaped pulses in actuation, instead of classically applied spiky pulses. Thus, defect detection/localization can be achieved by using low power voltages in actuation. Basically, the proposed processing is capable to compensate the acquired time-waveforms from the dispersion due to the traveled distance as well as to compress the additional pulse spreading due to the chirp actuation. A processed time-waveform is thus directly transformed into a distance of propagation. Next, the compensated and compressed signals are exploited to feed an imaging algorithm aimed at providing the position of the defect on the plate. As a result, an automatic detection procedure to locate defect-induced reflections is demonstrated and successfully tested by analyzing experimental Lamb waves propagating in an aluminum plate. The proposed method is suitable for defect detection and can be easily implemented in real applications for structural health monitoring.

Author(s):  
Wiesław J Staszewski ◽  
Amy N Robertson

Signal processing is one of the most important elements of structural health monitoring. This paper documents applications of time-variant analysis for damage detection. Two main approaches, the time–frequency and the time–scale analyses are discussed. The discussion is illustrated by application examples relevant to damage detection.


Author(s):  
Michelangelo Maria Malatesta ◽  
Denis Bogomolov ◽  
Marco Messina ◽  
Dennis D’Ippolito ◽  
Nicola Testoni ◽  
...  

Increased attentiveness on the environmental and effects of aging, deterioration and extreme events on civil infrastructure has created the need for more advanced damage detection tools and structural health monitoring (SHM). Today, these tasks are performed by signal processing, visual inspection techniques along with traditional well known impedance based health monitoring EMI technique. New research areas have been explored that improves damage detection at incipient stage and when the damage is substantial. Addressing these issues at early age prevents catastrophe situation for the safety of human lives. To improve the existing damage detection newly developed techniques in conjugation with EMI innovative new sensors, signal processing and soft computing techniques are discussed in details this paper. The advanced techniques (soft computing, signal processing, visual based, embedded IOT) are employed as a global method in prediction, to identify, locate, optimize, the damage area and deterioration. The amount and severity, multiple cracks on civil infrastructure like concrete and RC structures (beams and bridges) using above techniques along with EMI technique and use of PZT transducer. In addition to survey advanced innovative signal processing, machine learning techniques civil infrastructure connected to IOT that can make infrastructure smart and increases its efficiency that is aimed at socioeconomic, environmental and sustainable development.


2019 ◽  
Vol 30 (18-19) ◽  
pp. 2919-2931 ◽  
Author(s):  
Ali Nokhbatolfoghahai ◽  
Hossein M Navazi ◽  
Roger M Groves

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.


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