An undecimated wavelet transform based enhancement, statistical feature extraction and detection-classification of PD signals

We address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) burried in excessive noise. The major bottleneck being the recognition and supression of stochastic pulsive interference (PI) which has similar frequency characteristics as PD pulse. In this paper, we provide techniques to de-noise, detect, estimate and classify the PD signal in a statistical perspective. 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. We characterize the PD and PI pulses using a statistical model as the first moment of multi variate Gaussian distribution and its parameters are estimated using maximum likelihood (ML) and maximum aposteriroi probability (MAP) based techniques. A statistical test known as generalized log likelihood ratio test (GLRT) was incorporated to ensure the existence of the pulse. The decision as to whether a pulse is a noise or a desired signal has been made based on a weighted-nearest neighbor methodology.