Generative Adversarial Neural Networks for Guided Wave Signal Synthesis

Author(s):  
Mateusz Heesch ◽  
Ziemowit Dworakowski ◽  
Krzysztof Mendrok
Author(s):  
Xinyao Sun ◽  
Jinggan Shao ◽  
Yang Zhou ◽  
Ci Yuan ◽  
Yang Li ◽  
...  

Aiming at the problem of bolt looseness in structures, this paper proposes an active control method of axial force monitoring through guided wave and axial force compensation via the inverse piezoelectric effect of a piezoelectric ceramic gasket. Based on the finite element model, the propagation process of guided wave wave in bolted connectors is analyzed, which shows that the transmitted wave energy increases with the increase of bolt clamping force. The analysis of the stress-strain characteristics of the axially polarized and radially polarized piezoelectric ceramic gasket shows that the axially polarized piezoelectric ceramic gasket is more suitable for the control of bolt clamping force. The finite element analysis of the application of piezoelectric ceramic gasket in bolt axial force control shows that the power of guided wave signal increases monotonously with the increase of loaded electric field strength. In accordance with these theoretical methods and research, an active control system for bolt axial force is established in this experiment. The system monitors the power of the guided wave signal in real time and controls the axial force of the bolt by adjusting the intensity of the piezoelectric effect, which achieves an accurate control effect.


Author(s):  
Weilei MU ◽  
Zhengxing ZOU ◽  
Hailiang SUN ◽  
Guijie LIU ◽  
Guangyin XIA ◽  
...  

2012 ◽  
Vol 32 (4) ◽  
pp. 410-417
Author(s):  
Doo-Song Gil ◽  
Yeon-Shik Ahn ◽  
Gye-Jo Jung ◽  
Sang-Gi Park ◽  
Yong-Gun Kim

2016 ◽  
Vol 28 (7) ◽  
pp. 851-861 ◽  
Author(s):  
Ziemowit Dworakowski ◽  
Krzysztof Dragan ◽  
Tadeusz Stepinski

Neural networks are commonly recognized tools for the classification of multidimensional data obtained in structural health monitoring (SHM) systems. Their configuration for a given scenario is, however, a challenging task, which limits the possibilities of their practical applications. In this article the authors propose using the neural network ensemble approach for the classification of SHM data generated by guided wave sensor networks. The overproduce and choose strategy is used for designing ensembles containing different types and sizes of neural networks. The proposed method allows for a significant increase of the state assessment reliability, which is illustrated by the results obtained from the practical industrial case of a full-scale aircraft test. The method is verified in the process of detecting fatigue cracks propagating in the aircraft load-carrying structure. The long-term experiments are performed in variable environmental conditions with a net of structure-embedded piezoelectric sensors.


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