Model
Digital Document
Publisher
Florida Atlantic University
Description
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.
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.
Member of