TY - GEN
T1 - A combined fBm and PPCA based signal model for on-line recognition of PD signal
AU - Shetty, Pradeep Kumar
PY - 2005
Y1 - 2005
N2 - The problem of on-line recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD), buried in excessive noise has been addressed in this paper. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI), due to, overlapping broad band frequency spectrum of PI and PD pulses. Therefore, on-line, on-site, PD measurement is hardly possible in conventional frequency based DSP techniques. We provide new methods to model and recognize the PD signal, on-line. The observed noisy PD signal is modeled as linear combination of systematic and random components employing probabilistic principal component analysis (PPCA). Being a natural signal, PD exhibits long-range dependencies. Therefore, we model the random part of the signal with fractional Brownian motion (fBm) process and pdf of the underlying stochastic process is obtained. The PD/PI pulses are assumed as the mean of the process and non-parametric analysis based on smooth FIR filter is undertaken. The method proposed by the Author found to be effective in recognizing and retrieving the PD pulses, automatically, without any user interference.
AB - The problem of on-line recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD), buried in excessive noise has been addressed in this paper. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI), due to, overlapping broad band frequency spectrum of PI and PD pulses. Therefore, on-line, on-site, PD measurement is hardly possible in conventional frequency based DSP techniques. We provide new methods to model and recognize the PD signal, on-line. The observed noisy PD signal is modeled as linear combination of systematic and random components employing probabilistic principal component analysis (PPCA). Being a natural signal, PD exhibits long-range dependencies. Therefore, we model the random part of the signal with fractional Brownian motion (fBm) process and pdf of the underlying stochastic process is obtained. The PD/PI pulses are assumed as the mean of the process and non-parametric analysis based on smooth FIR filter is undertaken. The method proposed by the Author found to be effective in recognizing and retrieving the PD pulses, automatically, without any user interference.
UR - http://www.scopus.com/inward/record.url?scp=33646728675&partnerID=8YFLogxK
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U2 - 10.1007/11590316_31
DO - 10.1007/11590316_31
M3 - Conference contribution
AN - SCOPUS:33646728675
SN - 3540305068
SN - 9783540305064
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 229
EP - 235
BT - Pattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings
T2 - 1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005
Y2 - 20 December 2005 through 22 December 2005
ER -