대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2000년도 하계학술대회 논문집 C
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- Pages.1880-1884
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- 2000
웨이블렛 변환과 신경망을 이용한 음향방출신호의 자동분류에 관한연구
A Study on Auto-Classification of Acoustic Emission Signals Using Wavelet Transform and Neural Network
- 박재준 (중부대학교 정보공학부) ;
- 김면수 (중부대학교 정보공학부) ;
- 오승헌 (중부대학교 정보공학부) ;
- 강태림 (중부대학교 정보공학부) ;
- 김성홍 (순천청암대학 전기설비과) ;
- 백관현 (두원공과대학 전기과) ;
- 오일덕 (대전산업대학교 전자공학과) ;
- 송영철 (광운대학교 전기공학과) ;
- 권동진 (한전전력연구원)
- Park, Jae-Jun ;
- Kim, Meyoun-Soo ;
- Oh, Seung-Heon ;
- Kang, Tae-Rim ;
- Kim, Sung-Hong ;
- Beak, Kwan-Hyun ;
- Oh, Il-Duck ;
- Song, Young-Chul ;
- Kwon, Dong-Jin
- 발행 : 2000.07.17
초록
The discrete wavelet transform is utilized as preprocessing of Neural Network(NN) to identify aging state of internal partial discharge in transformer. The discrete traveler transform is used to produce wavelet coefficients which are used for Classification. The statistical parameters (maximum of wavelet coefficients, average value, dispersion, skewness, kurtosis) using the wavelet coefficients are input into an back-propagation neural network. The neurons whose weights have obtained through Result of Cross-Validation. The Neural Network learning stops either when the error rate achieves an appropriate minimum or when the learning time overcomes a constant value. The networks, after training, can decide if the test signal is Early Aging State or Last Aging State or normal state.
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