In the field of machine health monitoring, 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 the maintainer to clearly identify an impulsive fault source and its severity. In some cases, in-band noise can make impulses associated with incipient faults difficult to detect and interpret. In this paper, we use Wavelet De-Noising (WDN) after envelope-demodulation to improve the accuracy of bearing fault diagnostics. This contrasts the typical approach of de-noising raw vibration signals prior to demodulation. We find that WDN removes low amplitude harmonics and spurious reflections which may interfere with FFT techniques to identify low-frequency peaks in the signal spectrum. When measuring impact frequencies in the time-domain using a peakthresholding method, the proposed algorithm exhibits increasingly confident periodicity at bearing fault frequencies.
How to Cite
bearing defect diagnosis, wavelet de-noising
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