A Combined fBm and PPCA Based Signal Model for On-Line Recognition of PD Signal
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.
KeywordsFractional Brownian Motion Partial Discharge Latent Variable Model Pulsive Interference Probabilistic Principal Component Analysis
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