최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기전력전자학회지 = The journal of the Korean Institute of Power Electronics, v.15 no.4, 2010년, pp.25 - 32
이홍희 (울산대 전기전자정보시스템공학부)
초록이 없습니다.
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Sugumaran, V., Muralidharan, V., and Ramachandran,K. I. Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mech. Syst. Signal Process., 2007. 21. pp. 930-942.
Sun,W ., Chen. J., and Li, J. Decision tree and PCA-based fault diagnosis of rotating machinery. Mech. Syst. Signal Process., 2007. 21. pp. 1300-1317.
Lim,D. S., Yang.B. S., and Kim,D. J. An expert system for vibration diagnosis of rotating machinery using decision tree. Int. J. COMADEM. 2000. 3. pp. 31-36.
Yang,Y., Yu,D., and Cheng, J. A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM.Measurement. 2007. pp. 40. pp. 943-950.
Samanta, B. and AI-Balushi, K. R. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Syst. Signal Process., 2003, 17, pp. 317-328.
Samanta, B., Al-Balushi, K. R., and Al-Araimi, S. A. Artificial neural networks and genetic algorithm for bearing fault detection. Soft Comput., 2006, 10, pp. 264-271.
Lei,Y., He, Z., Zi,Y., and Hu,Q. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mech. Syst. Signal Process., 2007, 21, pp. 2280-2294.
Benbouzid. M. E. H. and Kliman. G. B. What stator current processing-based technique to use for induction motor rotor faults diagnosis. IEEE Trans. Energy Convers., 2003, 18(2), pp. 238-244.
Combastel, C., Lesecq, S., Petropol. S., and Gentil. S. Model-based and wavelet approaches to induction motor on-line fault detection. Control Eng. Pract., 2002, 10(5), pp. 493-509.
Kim, K. and Parlos, A. G. Model-based fault diagnosis of induction motors using non-stationary signal segmentation. Mech. Syst. Signal Process., 2002, 16(2.3), pp. 223-253.
Lim, H. S., Chong,K. T., and Su, H.Motor fault detection method for vibration signal using FFT residuals. Int. J. Appl. Electromagn.Mech., 2006, 24(3.4), pp. 209-223.
Niu, G., Son, J. D., Widodo. A., Yang, B. S., Hwang. D. H., and Kang. D. S. A comparison of classifier performance for fault diagnosis of induction motor using multi type signals. Struct. HealthMonit., 2007, 6(3), pp. 215-229.
Quinlan. J. R. C4.5: programs for machine learning. 1993 (Morgan Kaufmann Publisher. Inc., San Mateo. California).
The C4.5 code comes from the Internet. available from http://rulequest.com/personal/c4.5r8.tar.gz
Rao, J. S. Vibratory condition monitoring of machines, 2003, pp. 361-382 (Alpha Science International Ltd. Pangbourne. UK).
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