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