TY - JOUR
T1 - Unwrapping the influence of multiple parameters on the Magnetic Barkhausen Noise signal using self-organizing maps
AU - Pérez-Benítez, J. A.
AU - Espina-Hernández, J. H.
AU - Martínez-Ortiz, P.
PY - 2013
Y1 - 2013
N2 - The main advantage of Magnetic Barkhausen Noise as a non-destructive testing method is its sensitivity to several parameters such as microstructure, applied tension and plastic deformation. However, this noticeable property of the MBN sometimes could be a drawback. Usually, in measurements of industrial steel samples the variation of parameters occurs simultaneously. Then it is difficult to separate the influence of multiple parameters from the raw signal. This work proposes a method using trajectories traced in a type of neural network known as Self-Organizing Maps, in order to separate the influence of varying parameters on the Magnetic Barkhausen Noise row signal.
AB - The main advantage of Magnetic Barkhausen Noise as a non-destructive testing method is its sensitivity to several parameters such as microstructure, applied tension and plastic deformation. However, this noticeable property of the MBN sometimes could be a drawback. Usually, in measurements of industrial steel samples the variation of parameters occurs simultaneously. Then it is difficult to separate the influence of multiple parameters from the raw signal. This work proposes a method using trajectories traced in a type of neural network known as Self-Organizing Maps, in order to separate the influence of varying parameters on the Magnetic Barkhausen Noise row signal.
KW - Carbon content
KW - Magnetic barkhausen noise
KW - Plastic deformation
KW - Self-organizing maps
UR - http://www.scopus.com/inward/record.url?scp=84885179808&partnerID=8YFLogxK
U2 - 10.1016/j.ndteint.2012.10.006
DO - 10.1016/j.ndteint.2012.10.006
M3 - Artículo
SN - 0963-8695
VL - 54
SP - 166
EP - 170
JO - NDT and E International
JF - NDT and E International
ER -