A Long Memory Process Based Parametric Modeling and Recognition of PD Signal

Shetty, Pradeep Kumar (2004) A Long Memory Process Based Parametric Modeling and Recognition of PD Signal. In: 11th International Conference on Neural Information Processing, NOV 22-25, 2004, Calcutta, INDIA.

PDF A_Long_Memory.pdf - Published Version Restricted to Registered users only Download (176kB) | Request a copy

Abstract

We address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) buried in excessive noise. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) which has similar time-frequency characteristics as PD pulse. Therefore conventional frequency based DSP techniques are not useful in retrieving PD pulses. We employ statistical signal modeling based on combination of long-memory process and probabilistic principal component analysis (PPCA). An parametric analysis of the signal is exercised for extracting the features of desired pules. We incorporate a wavelet based bootstrap method for obtaining the noise training vectors from observed data. The procedure adopted in this work is completely different from the research work reported in the literature, which is generally based on deserved signal frequency and noise frequency.

Item Type:	Conference Paper
Publisher:	Springer-Verlag Berlin
Additional Information:	Copyright of this article belongs to Springer-Verlag Berlin.
Department/Centre:	Division of Electrical Sciences > High Voltage Engineering (merged with EE)
Date Deposited:	08 Mar 2012 06:11
Last Modified:	08 Mar 2012 06:11
URI:	http://eprints.iisc.ac.in/id/eprint/43773

Actions (login required)

View Item