This paper describes a technique that allows one to identify the faulty condition (open or short circuit) at a termination of a multiconductor transmission line structure by measuring the induced voltage at the other end. The wavelet theory is used to filter out from the signal the components due to unwanted sources, and to decompose it to obtain the fault's signature. The comparison (or matching) algorithm is substituted by an artificial neural network. Two differently designed neural networks are used to validate the results and the overall procedure is also tested on an experimental set-up

Diagnosing transmission line termination faults by means of wavelet based crosstalk signature recognition

BUCCELLA, CONCETTINA;ORLANDI, Antonio
2000-01-01

Abstract

This paper describes a technique that allows one to identify the faulty condition (open or short circuit) at a termination of a multiconductor transmission line structure by measuring the induced voltage at the other end. The wavelet theory is used to filter out from the signal the components due to unwanted sources, and to decompose it to obtain the fault's signature. The comparison (or matching) algorithm is substituted by an artificial neural network. Two differently designed neural networks are used to validate the results and the overall procedure is also tested on an experimental set-up
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/2310
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 13
social impact