Thermal Fatigue Damage Assessment in an Isotropic Pipe Using Nonlinear Ultrasonic Guided Waves

2014 ◽  
Vol 54 (8) ◽  
pp. 1309-1318 ◽  
Author(s):  
W. Li ◽  
Y. Cho
Keyword(s):  
Fatigue Damage ◽  
Thermal Fatigue ◽  
Damage Assessment ◽  
Guided Waves ◽  
Ultrasonic Guided Waves ◽  
Nonlinear Ultrasonic

Nonlinear ultrasonic guided waves for stress monitoring in prestressing tendons for post-tensioned concrete structures

2009 ◽  
Author(s):  
Ivan Bartoli ◽  
Claudio Nucera ◽  
Ankit Srivastava ◽  
Salvatore Salamone ◽  
Robert Phillips ◽  
...  
Keyword(s):  
Guided Waves ◽  
Concrete Structures ◽  
Ultrasonic Guided Waves ◽  
Stress Monitoring ◽  
Nonlinear Ultrasonic ◽  
Post Tensioned

Delamination Detection in Composite Plates Using Linear and Nonlinear Ultrasonic Guided Waves

2019 ◽  
Author(s):  
Yanfeng Shen ◽  
Mingjing Cen
Keyword(s):  
Wave Propagation ◽  
Guided Waves ◽  
Fiber Composite ◽  
Composite Plates ◽  
Ultrasonic Guided Waves ◽  
Carbon Fiber Composite ◽  
Laser Doppler Vibrometry ◽  
Delamination Detection ◽  
Nonlinear Ultrasonic ◽  
Linear And Nonlinear

Abstract This paper presents a delamination detection strategy for composite plates using linear and nonlinear ultrasonic guided waves via the wave field imaging and signal processing based on Scanning Laser Doppler Vibrometry (SLDV). The anisotropic elastodynamics in composite plates is first studied. Two numerical methods are deployed to analyze the wave mechanics within the composite plates. The Semi-analytical Finite Element (SAFE) method is utilized to obtain the dispersion curves and mode shapes for a carbon fiber composite plate by bonding two quasi-isotropic carbon fiber composite panels together. The Local Interaction Simulation Approach has been employed to investigate the wave propagation and interaction with the delamination. Contact Acoustic Nonlinearity (CAN) between the delamination interfaces during wave damage interaction is presented as a potential mechanism for delamination detection. After developing an in-depth understanding of the wave propagation and wave damage interaction mechanism, active sensing experiments are conducted using the Piezoelectric Wafer Active Sensors (PWAS) and the Scanning Laser Doppler Vibrometry (SLDV). Two delamination imaging methodologies are presented. The first one utilizes the total wave energy to detect the delamination, taking advantage of the trapped modes within the delaminated area. The second one adopts the nonlinear second harmonic imaging algorithm, highlighting the nonlinear interaction traces at the delamination region. The damage detection images are finally compared and fused to provide detailed diagnostic information of the delamination. The damage imaging technique presented in this paper possesses great potential in material evaluation and characterization applications. This paper finishes with summary, concluding remarks, and suggestions for future work.

scholarly journals Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 406
Author(s):  
Christopher Schnur ◽  
Payman Goodarzi ◽  
Yevgeniya Lugovtsova ◽  
Jannis Bulling ◽  
Jens Prager ◽  
...  
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Damage Assessment ◽  
Guided Waves ◽  
Industrial Condition ◽  
Guided Wave ◽  
Ultrasonic Guided Waves ◽  
Classification Rate ◽  
Feature Extraction And Selection ◽  
Combination Of Methods

Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.

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