This thesis focused on developing a new wavelet for use with the continuous
wavelet transform, a new detection method and two de-noising algorithms for rolling
element bearing fault signals. The work is based on the continuous wavelet transform
and implements a unique Fourier Series estimation algorithm that allows for least squares
estimation of arbitrary frequency components of a signal. The final results of the
research also included use of the developed detection algorithm for a novel method of
estimating the center frequency and bandwidth for use with the industry standard
detection algorithm, envelope demodulation, based on actual fault data. Finally, the
algorithms and wavelets developed in this paper were tested against seven other wavelet
based de-noising algorithms and shown to be superior for the de-noising and detection of
inner and outer rolling element race faults.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-3013 |
| Date | 15 May 2009 |
| Creators | Weatherwax, Scott Eric |
| Contributors | Palazzolo, Alan |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
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