scholarly journals Simulation assisted guided wave structural health monitoring of aerospace structures

2019 ◽  
Author(s):  
Elizabeth D. Gregory ◽  
William C. Schneck ◽  
Cara A. C. Leckey ◽  
Paul Swindell
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Guided Wave ◽  
Aerospace Structures ◽  
Structural Health

scholarly journals Research on the Progress of Interdigital Transducer (IDT) for Structural Damage Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wang Ziping ◽  
Xiong Xiqiang ◽  
Qian Lei ◽  
Wang Jiatao ◽  
Fei Yue ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Large Scale ◽  
Safety Information ◽  
Health And Safety ◽  
Guided Wave ◽  
Structural Safety ◽  
Structural Health ◽  
Damage Monitoring

In the application of Structural Health Monitoring (SHM) methods and related technologies, the transducer used for electroacoustic conversion has gradually become a key component of SHM systems because of its unique function of transmitting structural safety information. By comparing and analyzing the health and safety of large-scale structures, the related theories and methods of Structural Health Monitoring (SHM) based on ultrasonic guided waves are studied. The key technologies and research status of the interdigital guided wave transducer arrays which used for structural damage detection are introduced. The application fields of interdigital transducers are summarized. The key technical and scientific problems solved by IDT for Structural Damage Monitoring (SHM) are presented. Finally, the development of IDT technology and this research project are summarised.

Butterworth Filter Application for Structural Health Monitoring

2020 ◽  
pp. 733-748
Author(s):  
Ahmed Abdelgawad ◽  
Md Anam Mahmud ◽  
Kumar Yelamarthi
Keyword(s):  
Mathematical Model ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Guided Wave ◽  
Test Bed ◽  
Explicit Finite Element ◽  
Butterworth Filter ◽  
Structural Health ◽  
Aluminum Sheets ◽  
Set Up

Most of the existing Structural Health Monitoring (SHM) systems are vulnerable to environmental and operational damages. The majority of these systems cannot detect the size and location of the damage. Guided wave techniques are widely used to detect damage in structures due to its sensitivity to different changes in the structure. Finding a mathematical model for such system will help to implement a reliable and efficient low-cost SHM system. In this paper, a mathematical model is proposed to detect the size and location of damages in physical structures using the piezoelectric sensor. The proposed model combines both pitch-catch and pulse-echo techniques and has been verified throughout simulations using ABAQUS/ Explicit finite element software. For empirical verification, data was collected from an experimental set-up using an Aluminum sheets. Since the experimental data contains a lot of noises, a Butterworth filter was used to clean up the signal. The proposed mathematical model along with the Butterworth filter have been validated throughout real test bed.

scholarly journals Improving sensitivity and coverage of structural health monitoring using bulk ultrasonic waves

2020 ◽  
pp. 147592172096512
Author(s):  
Stefano Mariani ◽  
Yuan Liu ◽  
Peter Cawley
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Temperature Compensation ◽  
Signal Amplitude ◽  
Guided Wave ◽  
Ultrasonic Waves ◽  
Back Wall ◽  
Environmental Chamber ◽  
Structural Health ◽  
Specific Temperature

Practical ultrasonic structural health monitoring systems must be able to deal with temperature changes and some signal amplitude/phase drift over time; these issues have been investigated extensively with low-frequency-guided wave systems but much less work has been done on bulk wave systems operating in the megahertz frequency range. Temperature and signal drift compensation have been investigated on a thick copper block specimen instrumented with a lead zirconate titanate disc excited at a centre frequency of 2 MHz, both in the laboratory at ambient temperature and in an environmental chamber over multiple 20°C–70°C temperature cycles. It has been shown that the location-specific temperature compensation scheme originally developed for guided wave inspection significantly out-performs the conventional combined optimum baseline selection and baseline signal stretch method. The test setup was deliberately not optimised, and the signal amplitude and phase were shown to drift with time as the system was temperature cycled in the environmental chamber. It was shown that the ratio of successive back wall reflections at a given temperature was much more stable with time than the amplitude of a single reflection and that this ratio can be used to track changes in the reflection coefficient from the back wall with time. It was also shown that the location-specific temperature compensation method can be used to compensate for changes in the back wall reflection ratio with temperature. Clear changes in back wall reflection ratio were produced by the shadow effect of simulated damage in the form of 1-mm diameter flat-bottomed holes, and the signal-to-noise ratio was such that much smaller defects would be detectable.

scholarly journals Machine learning at the interface of structural health monitoring and non-destructive evaluation

2020 ◽  
Vol 378 (2182) ◽  
pp. 20190581
Author(s):  
P. Gardner ◽  
R. Fuentes ◽  
N. Dervilis ◽  
C. Mineo ◽  
S.G. Pierce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Ultrasonic Inspection ◽  
Guided Wave ◽  
Structural Health ◽  
Non Destructive Evaluation ◽  
Ultrasonic Guided Wave ◽  
Detection And Identification ◽  
Non Destructive

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

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