Data-driven matched field processing for Lamb wave structural health monitoring

2014 ◽  
Vol 135 (3) ◽  
pp. 1231-1244 ◽  
Author(s):  
Joel B. Harley ◽  
José M. F. Moura
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Data Driven ◽  
Matched Field Processing ◽  
Structural Health

Structural Health Monitoring Algorithm for Composite Plates using Lamb Wave Response Measured by Piezo Transducers

2019 ◽  
Author(s):  
Luis Eduardo Jaramillo Bustamante ◽  
Edison jair Bejarano sepulveda ◽  
Volnei Tita ◽  
Marcelo Leite Ribeiro
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Composite Plates ◽  
Structural Health ◽  
Wave Response ◽  
Monitoring Algorithm

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

2021 ◽  
pp. 136943322110384
Author(s):  
Xingyu Fan ◽  
Jun Li ◽  
Hong Hao
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Low Frequency ◽  
Data Driven ◽  
Mode Shapes ◽  
Monitoring Methods ◽  
Destructive Testing ◽  
Electromechanical Impedance ◽  
Structural Health

Vibration based structural health monitoring methods are usually dependent on the first several orders of modal information, such as natural frequencies, mode shapes and the related derived features. These information are usually in a low frequency range. These global vibration characteristics may not be sufficiently sensitive to minor structural damage. The alternative non-destructive testing method using piezoelectric transducers, called as electromechanical impedance (EMI) technique, has been developed for more than two decades. Numerous studies on the EMI based structural health monitoring have been carried out based on representing impedance signatures in frequency domain by statistical indicators, which can be used for damage detection. On the other hand, damage quantification and localization remain a great challenge for EMI based methods. Physics-based EMI methods have been developed for quantifying the structural damage, by using the impedance responses and an accurate numerical model. This article provides a comprehensive review of the exciting researches and sorts out these approaches into two categories: data-driven based and physics-based EMI techniques. The merits and limitations of these methods are discussed. In addition, practical issues and research gaps for EMI based structural health monitoring methods are summarized.

scholarly journals Arrays of Conformable Ultrasonic Lamb Wave Transducers for Structural Health Monitoring with Real-time Electronics

2014 ◽  
Vol 87 ◽  
pp. 1266-1269 ◽  
Author(s):  
L. Capineri ◽  
A. Bulletti ◽  
M. Calzolai ◽  
P. Giannelli ◽  
D. Francesconi
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Structural Health
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