ICA Mixture Modeling for the Classification of Materials in Impact-Echo Testing

Authors:
Addisson Salazar;Arturo Serrano;Raúl Llinares;Luis Vergara;Jorge Igual
Affiliations:
Departamento de Comunicaciones, Universidad Politecnica de Valencia, Valencia, Spain 46022;Departamento de Comunicaciones, Universidad Politecnica de Valencia, Valencia, Spain 46022;Departamento de Comunicaciones, Universidad Politecnica de Valencia, Valencia, Spain 46022;Departamento de Comunicaciones, Universidad Politecnica de Valencia, Valencia, Spain 46022;Departamento de Comunicaciones, Universidad Politecnica de Valencia, Valencia, Spain 46022
Venue:
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Year:
2009

Citing 8
Cited 0

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
ICA Mixture Models for Unsupervised Classification of Non-Gaussian Classes and Automatic Context Switching in Blind Signal Separation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational mixture of Bayesian independent component analyzers

Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
A Blind Source Separation Technique for Extracting Sinusoidal Interferences in Ultrasonic Non-Destructive Testing

Journal of VLSI Signal Processing Systems
Neural networks for defect detection in non-destructive evaluation by sonic signals

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Two applications of independent component analysis for non-destructive evaluation by ultrasounds

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel ICA mixture model applied to the classification of different kinds of defective materials evaluated by impact-echo testing. The approach considers different geometries of defects build from point flaws inside the material. The defects change the wave propagation between the impact and the sensors producing particular spectrum elements which are considered as the sources of the underlying ICA model. These sources and their corresponding transfer functions to the sensors make a signature of the resonance modes for different conditions of the material. We demonstrate the model with several finite element simulations and real experiments.