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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2011.11
- 수록면
- 1,020 - 1,028 (9page)
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As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.
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참고문헌 (19)
참고문헌 신청
1. [학술지(정기간행물)] - Crupi, V. / 2004 / Neural-network-based System for Novel Fault Detection in Rotating Machinery / Journal of Vibration and Control 10 : 1137 ~ 1150
2. [학술지(정기간행물)] - Li, F. / 2008 / Wavelet Transform-based Higher-order Statistics for Fault Diagnosis in Rolling Element Bearings / Journal of Vibration and Control 14 (11) : 1691 ~ 1709
3. [학술지(정기간행물)] - Li, F. / 2005 / Feature Extraction with Discrete Wavelet Transform for Drill Wear Monitoring / Journal of Vibration and Control 11 (11) : 1375 ~ 1390
4. [학술지(정기간행물)] - Goode, P. V / 1995 / Using a Neural/Fuzzy System to Extract Heuristic Knowledge of Incipient Faults in Induction Motors: Part I—t Methodology / IEEE Transactions On Industrial Electronics 42 (2) : 131 ~ 138
6. [학술지(정기간행물)] - Sanz, J. / 2007 / Fault Diagnosis of Rotating Machinery Based on Auto-associative Neural Networks and Wavelet Transforms / Journal of Sound and Vibration : 981 ~ 999
2. [학술지(정기간행물)] - Li, F. / 2008 / Wavelet Transform-based Higher-order Statistics for Fault Diagnosis in Rolling Element Bearings / Journal of Vibration and Control 14 (11) : 1691 ~ 1709
3. [학술지(정기간행물)] - Li, F. / 2005 / Feature Extraction with Discrete Wavelet Transform for Drill Wear Monitoring / Journal of Vibration and Control 11 (11) : 1375 ~ 1390
4. [학술지(정기간행물)] - Goode, P. V / 1995 / Using a Neural/Fuzzy System to Extract Heuristic Knowledge of Incipient Faults in Induction Motors: Part I—t Methodology / IEEE Transactions On Industrial Electronics 42 (2) : 131 ~ 138
6. [학술지(정기간행물)] - Sanz, J. / 2007 / Fault Diagnosis of Rotating Machinery Based on Auto-associative Neural Networks and Wavelet Transforms / Journal of Sound and Vibration : 981 ~ 999
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