지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한기계학회 Journal of Mechanical Science and Technology Journal of Mechanical Science and Technology Vol.21 No.5
- 발행연도
- 2007.5
- 수록면
- 737 - 744 (8page)
이용수
초록· 키워드
오류제보하기
Machinery health monitoring is a key step in the implementation of Condition-based Maintenance in industry. In this procedure, a quantitative description of machine health condition is necessary for maintenance decision-making. In this paper, we applied singularity analysis with wavelet for data processing and a new concept, Lipschitz exponent function, was proposed based on wavelet transform. A kurtosis based health index was defined, which can be used for maintenance decision-making. The proposed method was validated with two sets of gearbox vibration data in comparison with three other indexes. The results show that kurtosis based health index demonstrates excellent performance.
목차
Abstract
1. Introduction
2. Singularity analysis with wavelet: A new data processing technique
3. Condition description: Definition of health index
4. Experimental validation
5. Conclusions and future work
Acknowledgment
References
참고문헌 (18)
참고문헌 신청
Al-Balushi, R. and Samanta, B., 2002, “Gear Fault Diagnosis Using Energy-based Features of Acoustic Emission Signals,” Journal of Systems and Control Engineering, Vol. 216, pp. 249~263.
Dalpiaz, G. and Rivola, A., 1997, “Condition monitoring and Diagnostics in Automatic machines: Comparison of Vibration Analysis Techniques,” Mechanical Systems and Signal Processing, Vol. 11, No. 1, pp.53~73.
Hong, J. -C., Kim, Y. Y., Lee, H. C., and Lee, Y. W., 2002, “Damage Detection Using the Lipschitz Exponent Estimated by the Wavelet Transform: Application to vibration modes of a beam,” International Journal of Solids and Structures, 39 (2002), pp. 1803~1
Jardine, A. K. S., Lin, D. and Banjevic, D., 2006, “A Review on Machinery Diagnostics and Prognostics Implementing Condition-based Mainte-nance,” Mechanical Systems and Signal Processing, Vol. 20, No. 7, pp. 1483~1510.
Kuoppala, R. J., Jantunen, E. O. and Enwald, P. A., 1991, “Condition Monitoring Methods for Rotating Machinery,” Operational Reliability and Systematic Maintenance, Elsevier Applied Science, New York.
Dalpiaz, G. and Rivola, A., 1997, “Condition monitoring and Diagnostics in Automatic machines: Comparison of Vibration Analysis Techniques,” Mechanical Systems and Signal Processing, Vol. 11, No. 1, pp.53~73.
Hong, J. -C., Kim, Y. Y., Lee, H. C., and Lee, Y. W., 2002, “Damage Detection Using the Lipschitz Exponent Estimated by the Wavelet Transform: Application to vibration modes of a beam,” International Journal of Solids and Structures, 39 (2002), pp. 1803~1
Jardine, A. K. S., Lin, D. and Banjevic, D., 2006, “A Review on Machinery Diagnostics and Prognostics Implementing Condition-based Mainte-nance,” Mechanical Systems and Signal Processing, Vol. 20, No. 7, pp. 1483~1510.
Kuoppala, R. J., Jantunen, E. O. and Enwald, P. A., 1991, “Condition Monitoring Methods for Rotating Machinery,” Operational Reliability and Systematic Maintenance, Elsevier Applied Science, New York.
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