期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 106, 期 -, 页码 24-39出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2017.12.031
关键词
Ensemble local mean decomposition (ELMD); Complete ensemble local mean decomposition with adaptive noise (CELMDAN); Rolling bearing; Fault diagnosis
资金
- National Natural Science Foundation of China [51675355, 51275554]
- National Natural Science Foundation of China
- Civil Aviation Administration of China [U1733107]
- Fundamental Research Funds for the central Universities [YJ201662]
Mode mixing resulting from intermittent signals is an annoying problem associated with the local mean decomposition (LMD) method. Based on noise-assisted approach, ensemble local mean decomposition (ELMD) method alleviates the mode mixing issue of LMD to some degree. However, the product functions (PFs) produced by ELMD often contain considerable residual noise, and thus a relatively large number of ensemble trials are required to eliminate the residual noise. Furthermore, since different realizations of Gaussian white noise are added to the original signal, different trials may generate different number of PFs, making it difficult to take ensemble mean. In this paper, a novel method is proposed called complete ensemble local mean decomposition with adaptive noise (CELMDAN) to solve these two problems. The method adds a particular and adaptive noise at every decomposition stage for each trial. Moreover, a unique residue is obtained after separating each PF, and the obtained residue is used as input for the next stage. Two simulated signals are analyzed to illustrate the advantages of CELMDAN in comparison to ELMD and CEEMDAN. To further demonstrate the efficiency of CELMDAN, the method is applied to diagnose faults for rolling bearings in an experimental case and an engineering case. The diagnosis results indicate that CELMDAN can extract more fault characteristic information with less interference than ELMD. (C) 2017 Elsevier Ltd. All rights reserved.
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