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Rolling element bearing condition monitoring using acoustic emission technique and advanced signal processing

University of British Columbia

Acoustic emission (AE) signals generated from defects in rolling element bearings are investigated using simulated defects and experimental measurements in this thesis. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics. In this thesis the effect of defect size, operating speed, and loading conditions on statistical parameters of AE signals, using design of experiment method (DOE), have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings. A novel signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by background noise under different operating conditions. To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double AE impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals. Fatigue life estimation of rolling element bearing has also been investigated utilizing defect size measurements combined with an adaptive algorithm. Experimental results show the effectiveness of recursive least square algorithm for predicting the future defect size on the outer race.

Text

2012-09-10

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/43190

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/