Feature analysis of acoustic emission signals in time-frequency representation from partial discharge sources using self-organizing map
Electrical systems throughout the world are experiencing problems with aging insulation. One of the major components, which degrade under stresses, is the paper insulation of a power transformer. In this research work, analysis of acoustic emission (AE) signal patterns due to the occurrence of parti...
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my.uniten.dspace-307702023-12-29T15:52:58Z Feature analysis of acoustic emission signals in time-frequency representation from partial discharge sources using self-organizing map Zakaria Z. Md Thayoob Y.H. Samsudin M.R. Ghosh P.S. Chai M.L. 35411030800 6505876050 35410298500 55427760300 24448195600 Acoustic emission(AE) Partial discharge sources Self-Organizing Map(SOM) Time-frequency representation Acoustic emissions Conformal mapping Fourier transforms Image processing Imaging systems Power transformers Wavelet transforms Acoustic emission signal AE signals Descriptors Electrical systems Feature analysis Paper insulation Partial discharge sources Short time Fourier transforms Spectrograms Time-frequency representation Time-frequency representations Partial discharges Electrical systems throughout the world are experiencing problems with aging insulation. One of the major components, which degrade under stresses, is the paper insulation of a power transformer. In this research work, analysis of acoustic emission (AE) signal patterns due to the occurrence of partial discharge (PD) has been carried out. In order to characterize the different types of PD sources, seven descriptors or features were extracted from the Short-Time Fourier Transform spectrogram of the AE signals. These descriptors are used to generate the Self-Organizing Map (SOM) for three different types of PD sources. From the component planes of the descriptors produced by the SOM map, the analysis of the features was carried out and the best features to represent the AE signal patterns in the Time-Frequency representation were selected. Final 2023-12-29T07:52:58Z 2023-12-29T07:52:58Z 2009 Conference paper 10.1109/ICSIPA.2009.5478716 2-s2.0-77954495736 https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954495736&doi=10.1109%2fICSIPA.2009.5478716&partnerID=40&md5=01eddf4ac99b006c096bfb55c95f9a75 https://irepository.uniten.edu.my/handle/123456789/30770 5478716 542 547 Scopus |
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Acoustic emission(AE) Partial discharge sources Self-Organizing Map(SOM) Time-frequency representation Acoustic emissions Conformal mapping Fourier transforms Image processing Imaging systems Power transformers Wavelet transforms Acoustic emission signal AE signals Descriptors Electrical systems Feature analysis Paper insulation Partial discharge sources Short time Fourier transforms Spectrograms Time-frequency representation Time-frequency representations Partial discharges |
| spellingShingle |
Acoustic emission(AE) Partial discharge sources Self-Organizing Map(SOM) Time-frequency representation Acoustic emissions Conformal mapping Fourier transforms Image processing Imaging systems Power transformers Wavelet transforms Acoustic emission signal AE signals Descriptors Electrical systems Feature analysis Paper insulation Partial discharge sources Short time Fourier transforms Spectrograms Time-frequency representation Time-frequency representations Partial discharges Zakaria Z. Md Thayoob Y.H. Samsudin M.R. Ghosh P.S. Chai M.L. Feature analysis of acoustic emission signals in time-frequency representation from partial discharge sources using self-organizing map |
| description |
Electrical systems throughout the world are experiencing problems with aging insulation. One of the major components, which degrade under stresses, is the paper insulation of a power transformer. In this research work, analysis of acoustic emission (AE) signal patterns due to the occurrence of partial discharge (PD) has been carried out. In order to characterize the different types of PD sources, seven descriptors or features were extracted from the Short-Time Fourier Transform spectrogram of the AE signals. These descriptors are used to generate the Self-Organizing Map (SOM) for three different types of PD sources. From the component planes of the descriptors produced by the SOM map, the analysis of the features was carried out and the best features to represent the AE signal patterns in the Time-Frequency representation were selected. |
| author2 |
35411030800 |
| author_facet |
35411030800 Zakaria Z. Md Thayoob Y.H. Samsudin M.R. Ghosh P.S. Chai M.L. |
| format |
Conference paper |
| author |
Zakaria Z. Md Thayoob Y.H. Samsudin M.R. Ghosh P.S. Chai M.L. |
| author_sort |
Zakaria Z. |
| title |
Feature analysis of acoustic emission signals in time-frequency representation from partial discharge sources using self-organizing map |
| title_short |
Feature analysis of acoustic emission signals in time-frequency representation from partial discharge sources using self-organizing map |
| title_full |
Feature analysis of acoustic emission signals in time-frequency representation from partial discharge sources using self-organizing map |
| title_fullStr |
Feature analysis of acoustic emission signals in time-frequency representation from partial discharge sources using self-organizing map |
| title_full_unstemmed |
Feature analysis of acoustic emission signals in time-frequency representation from partial discharge sources using self-organizing map |
| title_sort |
feature analysis of acoustic emission signals in time-frequency representation from partial discharge sources using self-organizing map |
| publishDate |
2023 |
| _version_ |
1806427905726611456 |
| score |
13.222552 |