Simultaneous Multiparameter Measurement in Pulsed Eddy Current Steam Generator Data Using Artificial Neural Networks

2016 ◽  
Vol 65 (3) ◽  
pp. 672-679 ◽  
Author(s):  
Jeremy A. Buck ◽  
Peter Ross Underhill ◽  
Jordan E. Morelli ◽  
Thomas W. Krause
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steam Generator ◽  
Eddy Current ◽  
Pulsed Eddy Current ◽  
Artificial Neural

Identification of Conductivity Distribution Using Eddy Current Tomography System and Artificial Neural Networks

2010 ◽  
Vol 670 ◽  
pp. 336-344
Author(s):  
Tomasz Chady ◽  
Ireneusz Spychalski ◽  
Takashi Todaka
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Base ◽  
Eddy Current ◽  
Current System ◽  
Identification Problem ◽  
Conductivity Distribution ◽  
Tomography System ◽  
Dimensional Distribution ◽  
Artificial Neural

In certain applications (security, biomedical, food and wood testing etc.) it is necessary to detect and identify position of small metal particles with high precision. This paper presents an eddy current system designated for evaluation of conductivity distribution. The system was modeled using the finite element method as well as it was constructed and the measurements were carried out. Using these results a data base of the signals achieved for various configurations of the test objects were created. The data base was utilized to solve the identification problem. Artificial neural networks were utilized as the inverse models in order to reconstruct two-dimensional distribution of conductivity. Selected results achieved for simulated signals were presented.

Artificial neural networks for modelling the starting-up of a solar steam-generator

1998 ◽  
Vol 60 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Soteris A Kalogirou ◽  
Constantinos C Neocleous ◽  
Christos N Schizas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steam Generator ◽  
Artificial Neural

scholarly journals Evaluation of Loose Assemblies Using a Multi-frequency Eddy Current Method and Artificial Neural Networks 

2021 ◽  
Vol 31 (3) ◽  
pp. 78-82
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Eddy Current ◽  
Current Method ◽  
Eddy Current Method ◽  
The Neural Network ◽  
Artificial Neural ◽  
Aluminum Plates ◽  
The Relationship ◽  
Non Destructive

The paper deals with the non-destructive evaluation of the airgap existing between parts in loose metallic assemblies, using the eddy current (EC) method. In this study, the relationship between the variations of the impedance of a ferrite-cored coil sensor and an assembly featuring two aluminum plates is analyzed. Then artificial neural networks, based on statistical learning of the relationship between a sensor and an assembly are proposed and developed using both simulated and measured multi-frequency EC data, so as to estimate the distance between the assembly parts in a range from 0 µm to 500 µm. For the neural network built on experiment data, the inaccuracy of obtained results is smaller than 1.06%.

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