Author Type

Graduate Student

Date of Award

2014

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Khoshgoftaar, Taghi M.

Thesis/Dissertation Co-Chair

Beaujean, Pierre-Philippe

Abstract

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.

Extent

71 p.

Comments

Includes bibliography.|Thesis (M.S.)--Florida Atlantic University, 2014.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00004080

Format

pdf

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