期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 161, 期 -, 页码 -出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2021.107943
关键词
Adaptive periodic mode decomposition; Ramanujan subspace; Periodic component; Fault diagnosis
资金
- National Natural Science Foundation of China [51975193, 51875183]
By introducing the Ramanujan subspace and proposing the adaptive periodic mode decomposition (APMD) method, this study effectively identifies and extracts periodic components (PCs) in rolling bearing signals, providing a valid approach for rolling bearing fault diagnosis.
As is known to all, rolling bearing fault will induce periodic impulses. Although the existing fault diagnosis methods, such as wavelet transform (WT) and ensemble empirical mode decomposition (EEMD), can effectively diagnose rolling bearing fault, their weak period recognition abilities limit their application in rolling bearing fault diagnosis. The Ramanujan subspace is introduced from the perspective of period recognition and extraction, and an adaptive periodic mode decomposition (APMD) method that can extract periodic components (PCs) without setting any parameters is proposed in this paper. APMD determines the major periods (MPs) in the signal by periodicity measurement, and projects the signal into respective Ramanujan subspaces of MPs to form the corresponding PCs. The analysis results of rolling bearing signals show that APMD has excellent ability to identify and extract PCs and is a valid method for rolling bearing fault diagnosis. (c) 2021 Elsevier Ltd. All rights reserved.
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