In the field of machine prognostics, vibration analysis is a proven method for
detecting and diagnosing bearing faults in rotating machines. One popular method
for interpreting vibration signals is envelope demodulation, which allows a technician
to clearly identify an impulsive fault source and its severity. However incipient faults -faults in early stages - are masked by in-band noise, which can make the associated impulses difficult to detect and interpret. In this thesis, Wavelet De-Noising (WDN) is implemented after envelope-demodulation to improve accuracy of bearing fault diagnostics. This contrasts the typical approach of de-noising as a preprocessing step.
When manually measuring time-domain impulse amplitudes, the algorithm
shows varying improvements in Signal-to-Noise Ratio (SNR) relative to background
vibrational noise. A frequency-domain measure of SNR agrees with this result. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_13415 |
| Contributors | Bertot, Edward Max (author), Khoshgoftaar, Taghi M. (Thesis advisor), Beaujean, Pierre-Philippe (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 71 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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