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Semi-supervised Bayesian classification of materials with impact-echo signals.

Igual J, Salazar A, Safont G, Vergara L - Sensors (Basel) (2015)

Bottom Line: This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians.The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio.We test the method with real specimens made of aluminum alloy.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Comunicaciones, Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain. jigual@dcom.upv.es.

ABSTRACT
The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their defective status (homogeneous, one defect or multiple defects) and kind of defect (hole or crack, passing through or not). Every specimen is impacted by a hammer, and the spectrum of the propagated wave is recorded. This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians. The parameters of the Gaussian mixtures and the class probabilities are estimated using an extended expectation-maximization algorithm. The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio. We test the method with real specimens made of aluminum alloy. The results show that the algorithm works very well. This technique could be applied in many industrial problems, such as the optimization of the marble cutting process.

No MeSH data available.


Related in: MedlinePlus

Setup experiment for a piece with a hole and a crack.
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f3-sensors-15-11528: Setup experiment for a piece with a hole and a crack.

Mentions: As an example, in Figure 3, we show the setup for a piece with a hole and a crack defect. We use seven sensors located on different surfaces of the parallelepiped in order to capture the information coming from different directions and distances from the impact and defects. In the example provided in the figure, the hammer impacts on the front face. There is a hole in the Y axis far from the impact surface and one crack in the XZ plane near the impact plane.


Semi-supervised Bayesian classification of materials with impact-echo signals.

Igual J, Salazar A, Safont G, Vergara L - Sensors (Basel) (2015)

Setup experiment for a piece with a hole and a crack.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481956&req=5

f3-sensors-15-11528: Setup experiment for a piece with a hole and a crack.
Mentions: As an example, in Figure 3, we show the setup for a piece with a hole and a crack defect. We use seven sensors located on different surfaces of the parallelepiped in order to capture the information coming from different directions and distances from the impact and defects. In the example provided in the figure, the hammer impacts on the front face. There is a hole in the Y axis far from the impact surface and one crack in the XZ plane near the impact plane.

Bottom Line: This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians.The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio.We test the method with real specimens made of aluminum alloy.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Comunicaciones, Universitat Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain. jigual@dcom.upv.es.

ABSTRACT
The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their defective status (homogeneous, one defect or multiple defects) and kind of defect (hole or crack, passing through or not). Every specimen is impacted by a hammer, and the spectrum of the propagated wave is recorded. This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians. The parameters of the Gaussian mixtures and the class probabilities are estimated using an extended expectation-maximization algorithm. The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio. We test the method with real specimens made of aluminum alloy. The results show that the algorithm works very well. This technique could be applied in many industrial problems, such as the optimization of the marble cutting process.

No MeSH data available.


Related in: MedlinePlus