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

  • Pradeep Kumar Shetty
Conference paper

DOI: 10.1007/978-3-540-30499-9_121

Volume 3316 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Shetty P.K. (2004) A Long Memory Process Based Parametric Modeling and Recognition of PD Signal. In: Pal N.R., Kasabov N., Mudi R.K., Pal S., Parui S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Pradeep Kumar Shetty
    • 1
  1. 1.Dept. of HVEIndian Institute of ScienceBangaloreIndia