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Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition
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Mien, Van, Kang, Hee-Jun and Shin, Kyoo-Sik (2014) Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition. IET Science, Measurement & Technology, 8 (6). pp. 571-578. 6985780. doi:10.1049/iet-smt.2014.0023 ISSN 1751-8822.
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Official URL: http://dx.doi.org/10.1049/iet-smt.2014.0023
Abstract
The presence of faults in the bearings of rotating machinery is usually observed with impulses in the vibration signals. However, the vibration signals are generally non-stationary and usually contaminated by noise because of the compounded background noise present in the measuring device and the effect of interference from other machine elements. Therefore in order to enhance monitoring condition, the vibration signal needs to be properly de-noised before analysis. In this study, a novel fault diagnosis method for rolling element bearings is proposed based on a hybrid technique of non-local means (NLM) de-noising and empirical mode decomposition (EMD). An NLM which removes the noise with minimal signal distortion is first employed to eliminate or at least reduce the background noise present in the measuring device. This de-noised signal is then decomposed into a finite number of stationary intrinsic mode functions (IMF) to extract the impulsive fault features from the effect of interferences from other machine elements. Finally, envelope analyses are performed for IMFs to allow for easier detection of such characteristic fault frequencies. The results of simulated and real bearing vibration signal analyses show that the hybrid feature extraction technique of NLM de-noising, EMD and envelope analyses successfully extract impulsive features from noise signals.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Journal or Publication Title: | IET Science, Measurement & Technology | ||||||||
| Publisher: | The Institution of Engineering and Technology | ||||||||
| ISSN: | 1751-8822 | ||||||||
| Official Date: | November 2014 | ||||||||
| Dates: |
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| Volume: | 8 | ||||||||
| Number: | 6 | ||||||||
| Page Range: | pp. 571-578 | ||||||||
| Article Number: | 6985780 | ||||||||
| DOI: | 10.1049/iet-smt.2014.0023 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access |
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