http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Kanglei Lv (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
A fault diagnosis method for rolling element bearings based on ICEEMDAN and Bayesian network
Zengkai Liu,Kanglei Lv,Chao Zheng,Baoping Cai,Gang Lei,Yonghong Liu 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.5
As commonly used components in rotating machinery, rolling element bearings (REBs) can fail due to complex working conditions and high-speed rotation. The failure of bearings may cause great damage. It is necessary to identify the faults of bearings to prevent property losses and heavy casualties. This paper proposes a fault diagnosis approach based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and Bayesian network. The intrinsic mode functions (IMFs) extracted by ICEEMDAN algorithm are applied to construct feature vectors based on the energy entropy, and then the fault diagnosis model of the bearing is constructed by Bayesian network. The influence of load and sampling frequency on diagnostic accuracy of the bearing with different fault types is studied in this paper. And the research results show that the ICEEMDAN-BN method can improve the uncertainty reasoning ability and accuracy of the developed fault diagnosis model.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
