A search for suitable mother wavelet in discrete wavelet transform based analysis of acoustic emission partial discharge signals

Abstract

Signal processing helps monitor the condition of power equipment. Partial discharge (PD) signals used in condition-based maintenance give crucial information in the diagnosis of degradation of insulation. The acoustic emission technique (AET) is one of the most widely used techniques in PD signal analysis due to its inherent advantages. Analyzing acoustic emission partial discharge (AEPD) signals in the wavelet-domain provides critical insights into the location and type of the sources of PD. Selection of the most suitable mother wavelet in applying discrete wavelet transform (DWT) on AEPD signals is important as it will directly impact the outcome. For this selection, 36 wavelets belonging to the Daubechies, Symlets, Coiflets, and Bi-orthogonal families are investigated. For this purpose, five experimentally collected AEPD test signals are used. The selection is based on the “accuracy of wavelet decomposition results” in this work, probably for the first time. One mother wavelet from each family is individually shortlisted for all three performances, namely (a) reconstruction, (b) denoising, and (c) compression, by computing and comparing their commonly used metrics. Further, based on percentage energy criteria, the most suitable mother wavelets are identified as coif3, coif4, and coif5, respectively, for the three performances.

Keywords

• acoustic emission partial discharge (aepd)
• condition monitoring
• discrete wavelet transform (dwt)
• mother wavelet
• multi-resolution analysis(mra)
• oil insulation
• power transformer