Journal
MECHANISM AND MACHINE THEORY
Volume 90, Issue -, Pages 175-186Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.mechmachtheory.2015.03.014
Keywords
Local mean decomposition; Singular value decomposition; Extreme learning machine; Variable conditions; Rolling bearing; Fault diagnosis
Categories
Funding
- National Natural Science Foundation of China [61074083]
- Technology Foundation Program of National Defense [Z132013B002]
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Fault diagnosis for rolling bearings under variable conditions is a hot and relatively difficult topic, thus an intelligent fault diagnosis method based on local mean decomposition (LMD)-singular value decomposition (SVD) and extreme learning machine (ELM) is proposed in this paper. LMD, a newself-adaptive time-frequency analysis method, was applied to decompose the nonlinear and non-stationary vibration signals into a series of product functions (PFs), from which instantaneous frequencies with physical significance can be obtained. Then, the singular value vectors, as the fault feature vectors, were acquired by applying SVD to the PFs. Last, for the purpose of lessening human intervention and shortening the fault-diagnosis time, ELM was introduced for identification and classification of bearing faults. From the experimental results it was concluded that the proposed method can accurately diagnose and identify different fault types of rolling bearings under variable conditions in a relatively shorter time. (C) 2015 Elsevier Ltd. All rights reserved.
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