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An Undecimated Wavelet Transform Based Denoising, PPCA Based Pulse Modeling and Detection-Classification of PD Signals

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An Undecimated Wavelet Transform Based Denoising, PPCA Based Pulse Modeling and Detection-Classification of PD Signals
Authors 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 frequency characteristics as PD pulse. Also, the occurrence of PI is random like PD pulses. In this paper we provide techniques to de-noise, detect, estimate and classify the PD signal in a statistical perspective. To avoid aliasing due to interference of high frequency noise, PD signals are generally digitized in much higher sampling rates (in terms of tens of MHz), than actually required. A multi-resolution analysis based technique is incorporated to discard the huge amount of redundant data in acquired signal. A scale dependent MMSE based estimator is implemented in undecimated wavelet transform (UDWT) domain to enhance the noisy signal, due to its inherent advantages offered in the analysis of PD signal. The probabi...
Pradeep Kumar Shetty, T. S. Ramu
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Pradeep Kumar Shetty, T. S. Ramu
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