Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 23, Issue 4, Pages 1327-1338Publisher
ACADEMIC PRESS LTD ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2008.11.005
Keywords
Ensemble empirical mode decomposition; Empirical mode decomposition; Intrinsic mode function; Rotating machinery; Fault diagnosis
Categories
Funding
- National Nature Science Foundation of China [50335030]
- National Hitech Research and Development Program of China [2006AA04Z430]
Ask authors/readers for more resources
Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and non-stationary signals. It may decompose a complicated signal into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. The EMD method has attracted considerable attention and been widely applied to fault diagnosis of rotating machinery recently. However, it cannot reveal the signal characteristic information accurately because of the problem of mode mixing. To alleviate the mode mixing problem occurring in EMD, ensemble empirical mode decomposition (EEMD) is presented. With EEMD, the components with truly physical meaning can be extracted from the signal. Utilizing the advantage of EEMD, this paper proposes a new EEMD-based method for fault diagnosis of rotating machinery. First, a simulation signal is used to test the performance of the method based on EEMD. Then, the proposed method is applied to rub-impact fault diagnosis of a power generator and early rub-impact fault diagnosis of a heavy oil catalytic cracking machine set. Finally, by comparing its application results with those of the EMD method, the superiority of the proposed method based on EEMD is demonstrated in extracting fault characteristic information of rotating machinery. (C) 2008 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available