Vibration analysis based feature extraction for bearing fault detection

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Proceedings titleApplied sciences and engineering: modern topics in manufacturing and designing: selected, peer reviewed papers from the International conference on mechanical science and engineering (ICMSE 2012)
Series titleApplied Mechanics and Materials; Volume 197
ConferenceInternational Conference on Mechanical Science and Engineering, ICMSE 2012, July 20-22 2012, Beijing, China
Pages124128; # of pages: 5
SubjectBearing fault detection; Bearing fault signature; Catastrophic failures; Health condition; Nonstationary; Performance degradation; Research efforts; Rolling Element Bearing; Rotary machine; Bearings (structural); Condition monitoring; Fault detection; Rotating machinery; Signal processing; Vibration analysis; Feature extraction
AbstractRolling element bearings are widely used in various rotary machines. Most rotary machine failures are attributed to unexpected bearing faults. Accordingly, reliable bearing fault detection is critically needed in industries to prevent these machines' performance degradation, malfunction, or even catastrophic failures. Feature extraction plays an important role in bearing fault detection and significant research efforts have thus far been devoted to this subject from both academia and industry. This paper intends to provide a brief review of the recent developments in feature extraction for bearing fault detection, and the focus will be placed on the advances in methods for dealing with the nonstationary characteristics of bearing fault signatures. © (2012) Trans Tech Publications, Switzerland.
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AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21270040
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Record identifierf121dc26-853b-4076-9c19-aac5f434ae1f
Record created2013-12-16
Record modified2016-05-09
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