| DOI | Resolve DOI: https://doi.org/10.4028/www.scientific.net/AMM.197.124 |
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| Author | Search for: Liu, Jie; Search for: Yang, Chunsheng1; Search for: Lou, Qingfeng |
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| Affiliation | - National Research Council of Canada. Information and Communication Technologies
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| Format | Text, Article |
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| Conference | International Conference on Mechanical Science and Engineering, ICMSE 2012, July 20-22 2012, Beijing, China |
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| Subject | Bearing 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 |
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| Abstract | Rolling 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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| Publication date | 2012-09-26 |
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| Series | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21270040 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | f121dc26-853b-4076-9c19-aac5f434ae1f |
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| Record created | 2013-12-16 |
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| Record modified | 2020-04-21 |
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