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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한기계학회 Journal of Mechanical Science and Technology Journal of Mechanical Science and Technology Vol.21 No.7
- 발행연도
- 2007.7
- 수록면
- 1,058 - 1,065 (8page)
이용수
초록· 키워드
오류제보하기
This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The main purpose is to investigate different fault conditions for rotating machinery, such as imbalance, misalignment, base looseness and combination of imbalance and misalignment. In this study, we measured the non-stationary vibration signals induced by these fault conditions. Applying wavelet packets transform to these signals, the fractal dimension of each frequency channel was extracted and the box counting dimension was used to depict the failure characteristics of the fault conditions. The failure modes were then identified by a radial basis function neural network. An experiment was conducted and the results showed that the proposed method can detect and recognize different kinds of fault conditions. Therefore, it is concluded that the combination of wavelet packets-fractal technology and neural networks can provide an effective method to diagnose fault conditions of rotating machinery.
목차
Abstract
1. Introduction
2. Theory
3. Experimental analysis and discussion
4. Conclusion
References
참고문헌 (20)
참고문헌 신청
Arbib, Michael A., 2002, “The Handbook of Brain Theory and Neural Networks Second Edition, Massachusetts MIT Press.
Boulahbal, D., Golnaraghi, M. F. and Ismail, F., 1999, “Amplitude and Phase Wavelet Maps for the Detection of Cracks in Geared Systems,” International Journal of Mechanical Systems and Signal Processing, Vol. 13, No. 3, pp. 423~436.
Catelani, M. and Fort, A., 2000, “Fault Diagnosis of Electronic Analog Circuits Using a Radial Basis Function Network Classifier,” Measurement, Vol. 28, pp. 147~158.
Chen, S., Cowan, C. F. N. and Grant, P. M., 1991, “Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks,” IEEE Trans. on Bio. Eng., Vol. 2, pp. 302~309.
Fan, C., Jin, Z., Zhang, J. and Tian, W., 2002, “Application of Multisensor Data Fusion Based on RBF Neural Networks for Fault Diagnosis of SAMs,” Seventh International Conference on Control, Automation, Robotics and Vision, Vol. 3, pp. 1557~1562.
Boulahbal, D., Golnaraghi, M. F. and Ismail, F., 1999, “Amplitude and Phase Wavelet Maps for the Detection of Cracks in Geared Systems,” International Journal of Mechanical Systems and Signal Processing, Vol. 13, No. 3, pp. 423~436.
Catelani, M. and Fort, A., 2000, “Fault Diagnosis of Electronic Analog Circuits Using a Radial Basis Function Network Classifier,” Measurement, Vol. 28, pp. 147~158.
Chen, S., Cowan, C. F. N. and Grant, P. M., 1991, “Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks,” IEEE Trans. on Bio. Eng., Vol. 2, pp. 302~309.
Fan, C., Jin, Z., Zhang, J. and Tian, W., 2002, “Application of Multisensor Data Fusion Based on RBF Neural Networks for Fault Diagnosis of SAMs,” Seventh International Conference on Control, Automation, Robotics and Vision, Vol. 3, pp. 1557~1562.
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