Open AccessFeature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy
Author(s) -
Zhao Miaoying,
Xu Gang
Publication year - 2018
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2017.0188
Subject(s) - hilbert–huang transform , wavelet transform , wavelet , pattern recognition (psychology) , feature extraction , harmonic wavelet transform , vibration , entropy (arrow of time) , transformer , wavelet packet decomposition , computer science , discrete wavelet transform , second generation wavelet transform , artificial intelligence , mathematics , electronic engineering , acoustics , speech recognition , engineering , physics , voltage , telecommunications , electrical engineering , white noise , quantum mechanics
To achieve an effective feature extraction for power transformer vibration signals, the authors propose a method for signal feature extraction based on empirical wavelet transform (EWT) and multiscale entropy (MSE). First, transformer vibration signals are decomposed into several empirical wavelet functions (EWFs) with the method of EWT. Then, the frequency characteristics of signals are demonstrated in the time‐frequency representation by applying a Hilbert transform to each EWF component. Finally, in order to quantify the extracted features, the MSEs of components being highly correlated with the original signals are calculated to construct the eigenvectors of transformer vibration signals. Several experiments are presented showing the effectiveness of this method compared with the classic empirical mode decomposition method.