A comparison of cycle spinning versus stationary wavelet transform for the extraction of features of partial discharge signals

Abstract

This paper presents a comparison of three feature extraction methods to denoise partial discharge (PD) signals. The denoising technique employs the Stationary Wavelet Transform (SWT) associated to a spatially-adaptive selection procedure based on the coefficients propagation along decomposition levels (scales). The PD and noise related coefficients are identified and separated by an automatic data classifier using Support Vector Machines (SVM). The first and second feature extraction methods act directly on the SWT coefficients and differ only on the procedures to characterize the propagation. The third method relies on Cycle Spinning (CS) on the several translated Discrete Wavelet Transform (DWT) obtained from SWT. We conducted an empirical study using Analysis of Variance (ANOVA) to evaluate the influence of the methods on denoising performance and to guarantee the statistical significance of the tests. Afterwards, performance was evaluated considering real PD signals measured in air and in solid dielectrics, corrupted by several types of interferences, both stationary and time-varying. The results show that the three approaches allow robust signal recovering and significant noise rejection, but differ substantially on the quality of the reconstructed signals.

An Empirical Rate Balanced Alien Xtalk Mitigation Method for G.FAST Systems

“…Entre estes, a detecção de DPs por meios elétricos é geralmente a mais utilizada por fornecer informações mais detalhadas (MOTA et al, 2016). Percebendo que até então não há uma maneira convencional para realizar denoising em sinais de descargas parciais, buscou-se então uma alternativa aos métodos já existentes.…”

Proposta De Um Novo Algoritmo Qos-Aware Para O Escalonamento Downlink Lte-a Em Cenários De Tráfego Misto: Uma Comparação De Desempenho

“…localidade temporal e baixa energia, aparecendo em forma de pulsos elétricos de curta duração. Sua reincidência pode ocasionar defeitos nos sistemas de isolamento devido às alterações que causam nas estruturas físicas e químicas dos materiais (MOTA et al, 2016). Em casos mais extremos, podem ocorrer rupturas no isolamento causando graves falhas de operação, como por exemplo em sistemas de alta tensão.…”

Translation Invariance-Based Deep Learning for Rotating Machinery Diagnosis

“…When the collected signals are segmented to train the classification model, the nonstationary and nonlinear characteristics of the signal often limit the learning ability of the deep learning network. While the Fourier transform is just a powerful tool for stationary signal analysis [10], and although the traditional wavelet transform benefits from its adaptive and multiresolution capability, it is also difficult to guarantee the time-invariant characteristics of the signal [11]. e dual-tree complex wavelet transform first proposed by Kingsbury [12] was verified to enjoy super shift invariance and reducing spectral aliasing to traditional wavelet transform.…”