Damage Assessment for a Sandwich-Like Panel Using Experimental and Numerical Analysis of Guided Waves

Author(s):  
Kaleeswaran Balasubramaniam ◽  
Piotr Fiborek ◽  
Shirsendu Sikdar ◽  
Pawel H. Malinowski
1986 ◽  
Vol 11 (9) ◽  
pp. 590 ◽  
Author(s):  
L. Leine ◽  
Ch. Wachter ◽  
U. Langbein ◽  
F. Lederer

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 406
Author(s):  
Christopher Schnur ◽  
Payman Goodarzi ◽  
Yevgeniya Lugovtsova ◽  
Jannis Bulling ◽  
Jens Prager ◽  
...  

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.


1987 ◽  
Vol 12 (9) ◽  
pp. 747 ◽  
Author(s):  
L. Leine ◽  
C. Wachter ◽  
U. Langbein ◽  
F. Lederer

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