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AuthorHemmati, Farzad
AuthorAlqaradawi, Mohammed
AuthorGadala, Mohamed S.
Available date2021-09-05T05:40:21Z
Publication Date2016
Publication NameProceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology
ResourceScopus
ISSN13506501
URIhttp://dx.doi.org/10.1177/1350650115591233
URIhttp://hdl.handle.net/10576/22738
AbstractAcoustic emission (AE) signal generated from defects in rolling element bearings are investigated using simulated defects and experimental measurements in this paper. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings comprises four main stages, which are, statistical analysis, faults diagnostics, defect size calculation, and prognostics. A modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearing components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by the background noise under different operating conditions. 2015 Institution of Mechanical Engineers.
SponsorThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant from the Qatar National Research Fund (QNRF), Doha, Qatar.
Languageen
PublisherSAGE Publications Ltd
Subjectacoustic emission
de-noising
Signal processing
wavelet packet transform
TitleRolling element bearing fault diagnostics using acoustic emission technique and advanced signal processing
TypeArticle
Pagination64-77
Issue Number1
Volume Number230
dc.accessType Abstract Only


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