Application of wavelet packet transform in roller bearing fault detection and life estimation Mohammad Miraskari1 a
Gadala, Mohamed S.
MetadataShow full item record
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 processin g 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. 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 and envelope detection. 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 acoustic emission 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 Recursive Least Square Estimation method which is an adaptive algorithm. Experimental results show the effectiveness of recursive least square algorithm for predicting the future defect size on the outer race.