期刊
ISA TRANSACTIONS
卷 106, 期 -, 页码 392-401出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2020.06.011
关键词
Empirical mode decomposition; Mean-optimized mode decomposition; Vibration signal; Rolling bearing; Fault diagnosis
资金
- National Key Research and Development Program of China [2017YFC0805100]
- National Natural Science Foundation of China [51975004]
- University Natural Science Key Project of Anhui Province, China [KJ2019A0053, KJ2019A0092]
- National Natural Science Foundation of Anhui Provence, China [2008085QE215]
- Anhui Key Laboratory of Mine Intelligent Equipment and Technology, Anhui University of Science & Technology, China [201902005]
As an effective signal separation method of non-stationary signal, empirical mode decomposition (EMD) has been widely used in the data or time series analysis of many engineering fields. However, the decomposing result of EMD often is affected by the fitting in mean curve construction and the sifting process. In this paper, the mean-optimized mode decomposition (MOMD) procedure is proposed to enhance the performance of the original EMD in mean curve construction. Also, the proposed MOMD algorithm is compared with original EMD through analyzing two artificial signals and the analysis results demonstrate that MOMD has much more significantly improvement in decomposition performance and precision than the original EMD. Last, MOMD is introduced to the signal processing stemming from the faulty rolling bearing and the rotor system with failure. Also, the comparison of the proposed MOMD method with EMD was made and the analysis results show that MOMD obtains much more accurate IMFs and fault diagnostic effect than the original EMD method. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
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