Fault Detection, Neural Networks, Cable Television Networks
TITLE: Comparison of the Use of Neural Networks Versus
Statistical Models in Fault Detection for Cable Television Networks
AUTHORS: N. P. Kourounakis,
S. W. Neville and N. J. Dimopoulos
IN:
1998 IEEE Symposium on Advances in Digital Filtering and Signal
Processing, Victoria, BC, Canada, June 5-6, 1998
ABSTRACT:
In this work, we present a model based method for reliably detecting
Reverse Pilot faults within cable amplifier networks. This method has the
advantage over traditional fixed bound fault detection techniques in that
it is able to track changes in the environmental conditions and accurately
detect changes in signal behaviour. The resulting method offers increased
fault detection sensitivity and reduced false alarms rate.
We have implemented a general approach based on the use of a modeling
engine which is capable of capturing the behaviour of the Reverse Pilot
of cable television amplifiers. Two modeling engines were developed for
this purpose. The first one is based on the use of feed forward neural
networks, and the second one is based on the use of statistical analysis
techniques. The resulting fault detection system, employing either modeling
engine, was able to provide good temporal localization of the start of
fault conditions and a clear indication of the presence of the fault through
its occurrence.