Refining Envelope Analysis Methods using Wavelet De-Noising to Identify Bearing Faults

Edward M. Bertot, Pierre-Philippe Beaujean, and David J. Vendittis
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phmce_14_049.pdf950.36 KBMay 23, 2014 - 5:33pm

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 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 peak-thresholding method, the proposed algorithm exhibits increasingly confident periodicity at bearing fault frequencies.

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Submission Keywords: 
bearing defect diagnosis
wavelet de-noising
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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