Fast diagnosis of transmission lines using neural networks and principal component analysis

Mostafa-Kamel Smaïl; Yann Le Bihan; Lionel Pichon

Fast diagnosis of transmission lines using neural networks and principal component analysis

Mostafa-Kamel Smaïl ¹ Yann Le Bihan ² Lionel Pichon ¹

1 ICHAMS - Equipe Interaction Champs - Matériaux et Structures

LGEP - Laboratoire de génie électrique de Paris

2 COCODI - Equipe Conception, Commande et Diagnostic

LGEP - Laboratoire de génie électrique de Paris

Abstract : A fast diagnosis dedicated to embedded transmission lines and based on time domain reflectometry is presented. The forward problem allows to simulate the propagation along the wiring network as well as to create datasets for the inverse problem resolution. Neural networks (NNs) are used to solve the inverse problem which consists to find defects on the wire from the reflectometry response. The significant parameters of the reflectometry response data are extracted using principal component analysis. This method allows an efficient reduction of the dimension of the reflectometry data space.

Document type :

Journal articles

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https://hal-supelec.archives-ouvertes.fr/hal-00779080
Contributor : Olivier Schneegans <>
Submitted on : Monday, January 21, 2013 - 4:51:14 PM
Last modification on : Wednesday, December 12, 2018 - 1:26:02 AM

Fast diagnosis of transmission lines using neural networks and principal component analysis

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Fast diagnosis of transmission lines using neural networks and principal component analysis

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