Earticle

초록

영어

For rolling bearing fault, there is a certain mapping relationship between failure state and its each frequency band energy and distribution, this paper proposes a bearing fault diagnosis method based on wavelet packet decompotion energy distribution and gray similar correlation degree. Introducing a concept of soft morphological filtering, combined the EMD method on the original vibration signal de-noising pretreatment. Decomposing bearing vibration acceleration signal after noise reduction into different frequency band signal by adopting the method of wavelet packet decomposition and calculating the energy distribution. Due to the gray similar correlation degree analysis for the classification of the small sample pattern recognition has a good effect, element structure characteristic vector by energy distribution, to judge the fault types by calculating the bearing vibration signal under different status similar correlation degree. Experimental results show that, the proposed can be effectively applied to bearing fault diagnosis.

목차

Abstract
 1. Introduction
 2. Preprocessing Principle of Flexible Morphological Filtering Noise based on EMD
 3. Wavelet Packet Decomposition
 4. Gray Similarity Degree
 5. Fault Recognition of bearing based on Wavelet Packet Decomposition Energy Distribution and Gray Similarity Degree
 6. Application
 7. Conclusion
 References

키워드

저자

• Zhang Zhigang [ Zhejiang Agriculture Business College, Shaoxing 312000, China ]
• Qin Hongmao [ State Key Laboratory of Automotive Safety and Energy, Beijing 100084, China ] Corresponding Author
• Sun Ning [ Society of Automotive Engineers of China, Beijing 100055, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605