Damage detection in composite structures using Lamb wave analysis and time-frequency approach

2011 ◽  
Author(s):  
Yingtao Liu ◽  
Masoud Yekani Fard ◽  
Seung B. Kim ◽  
Aditi Chattopadhyay ◽  
Derek Doyle
Keyword(s):  
Damage Detection ◽  
Lamb Wave ◽  
Composite Structures ◽  
Wave Analysis ◽  
Time Frequency

scholarly journals Machine Learning-Enriched Lamb Wave Approaches for Automated Damage Detection

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1790
Author(s):  
Zi Zhang ◽  
Hong Pan ◽  
Xingyu Wang ◽  
Zhibin Lin
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Damage Detection ◽  
Lamb Wave ◽  
Lamb Waves ◽  
Feature Selection Method ◽  
Support Vector ◽  
Time Frequency ◽  
Damage States

Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb wave when it propagates, scatters and disperses. Machine learning in recent years has created transformative opportunities for accelerating knowledge discovery and accurately disseminating information where conventional Lamb wave approaches cannot work. Therefore, the learning framework was proposed with a workflow from dataset generation, to sensitive feature extraction, to prediction model for lamb-wave-based damage detection. A total of 17 damage states in terms of different damage type, sizes and orientations were designed to train the feature extraction and sensitive feature selection. A machine learning method, support vector machine (SVM), was employed for the learning model. A grid searching (GS) technique was adopted to optimize the parameters of the SVM model. The results show that the machine learning-enriched Lamb wave-based damage detection method is an efficient and accuracy wave to identify the damage severity and orientation. Results demonstrated that different features generated from different domains had certain levels of sensitivity to damage, while the feature selection method revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. These features were also much more robust to noise. With increase of noise, the accuracy of the classification dramatically dropped.

scholarly journals Damage Detection of Composite Plates by Lamb Wave Ultrasonic Tomography with a Sparse Hexagonal Network Using Damage Progression Trends

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
C. J. Keulen ◽  
M. Yildiz ◽  
A. Suleman
Keyword(s):  
Damage Detection ◽  
Lamb Wave ◽  
Composite Structures ◽  
Composite Plates ◽  
Reconstruction Algorithm ◽  
Detection Algorithm ◽  
Ultrasonic Tomography ◽  
Large Area ◽  
Sparse Network ◽  
Damage Progression

Lamb wave based structural health monitoring shows a lot of potential for damage detection of composite structures. However, currently there is no agreement upon optimal network arrangement or detection algorithm. The objective of this research is to develop a sparse network that can be expanded to detect damage over a large area. To achieve this, a novel technique based on damage progression history has been developed. This technique gives an amplification factor to data along actuator-sensor paths that show a steady reduction in transmitted power as induced damage progresses and is implemented with the reconstruction algorithm for probabilistic inspection of damage (RAPID) technique. Two damage metrics are used with the algorithm and a comparison is made to the more commonly used signal difference coefficient (SDC) metric. Best case results show that damage is detected within 12 mm. The algorithm is also run on a more sparse network with no damage detection, therefore indicating that the selected arrangement is the most sparse arrangement with this configuration.

Smart Patch Repair with Low Profile PVDF Sensors

2017 ◽  
Vol 754 ◽  
pp. 359-362 ◽  
Author(s):  
Florian Lambinet ◽  
Zahra Sharif Khodaei
Keyword(s):  
Health Monitoring ◽  
Lamb Wave ◽  
Composite Structures ◽  
Polyvinylidene Fluoride ◽  
Composite Panel ◽  
Patch Repair ◽  
Wave Analysis ◽  
Electromechanical Impedance ◽  
Bonded Repair ◽  
Low Profile

Bonded repair of composite structures still remains a major concern for the airworthiness authorities because of the uncertainty about the repair quality. This work, investigates the applicability of conventional Structural Health Monitoring (SHM) techniques for monitoring of bonded repair with ring-shaped low profile sensors. A repaired composite panel has been sensorized with two Ring-Shaped Polyvinylidene fluoride piezopolymer Sensors (RSPS) and a piezoelectric (PZT) transducer. An electromechanical impedance (EMI) and Lamb wave analysis have been carried out to check the sensitivity of these sensors to detect an artificially introduced damage simulating a disbond of the repair. The state of the repair have been successfully monitored and reported by both methods.

Lamb Wave Instantaneous Phase Based Method for Quantitative Level of Delamination Damage in Composite Structures

2012 ◽  
Author(s):  
Dulip Samaratunga ◽  
Ruisheng Wang ◽  
Ratneshwar Jha
Keyword(s):  
Finite Element ◽  
Lamb Wave ◽  
Composite Structures ◽  
Low Frequency ◽  
Wave Dispersion ◽  
Finite Element Simulations ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Instantaneous Phase ◽  
Quantitative Level

In this study, we investigate the interactions of Lamb wave A0 mode with different sizes of delaminations in composites using finite element code Abaqus®. According to Lamb wave dispersion curves, the group velocity of A0 mode increases rapidly as the frequency-thickness increases in the relatively low frequency region. In the delamination region, the frequency-thickness product decreases compared to the healthy laminate since the damage causes ply separation at the lamina interface. In the current study this observation is investigated in detail using finite element simulations. The resulting phase delay is analyzed by Empirical Mode Decomposition (EMD) and instantaneous phase approach. Finite element simulations are performed using Abaqus® and signal processing is performed in joint time-frequency domain using Hilbert-Huang Transform (HHT) method. The unwrapped instantaneous phase difference is correlated with the extent of delamination (quantitative level of damage).

scholarly journals Non-contact damage detection on a rotating blade by Lamb wave analysis

2017 ◽  
Vol 92 ◽  
pp. 159-166 ◽  
Author(s):  
Daniel Veira Canle ◽  
Ari Salmi ◽  
Edward Hæggström
Keyword(s):  
Damage Detection ◽  
Lamb Wave ◽  
Wave Analysis ◽  
Contact Damage ◽  
Rotating Blade

Selection of Materials and Sensors for Health Monitoring of Composite Structures

2003 ◽  
Vol 785 ◽  
Author(s):  
Seth S. Kessler ◽  
S. Mark Spearing
Keyword(s):  
Health Monitoring ◽  
Lamb Wave ◽  
Composite Structures ◽  
Structural Damage ◽  
Performance Metrics ◽  
Full Range ◽  
Transportation Systems ◽  
Health Monitoring Systems ◽  
The Relationship ◽  
Selection Of

ABSTRACTEmbedded structural health monitoring systems are envisioned to be an important component of future transportation systems. One of the key challenges in designing an SHM system is the choice of sensors, and a sensor layout, which can detect unambiguously relevant structural damage. This paper focuses on the relationship between sensors, the materials of which they are made, and their ability to detect structural damage. Sensor selection maps have been produced which plot the capabilities of the full range of available sensor types vs. the key performance metrics (power consumption, resolution, range, sensor size, coverage). This exercise resulted in the identification of piezoceramic Lamb wave transducers as the sensor of choice. Experimental results are presented for the detailed selection of piezoceramic materials to be used as Lamb wave transducers.

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