Oscillatory behavior-based wavelet decomposition for the monitoring of bearing condition in centrifugal pumps

Bearing failure is one of the reasons for centrifugal pump breakdown. Existing methods developed for bearing fault diagnosis do not work satisfactorily when the vibration signature of bearing is overlapped by the signature from other defect sources such as an impeller defect. A vibration signal processing scheme making use of ensemble empirical mode decomposition and dual Q-factor wavelet decomposition is proposed to extract information of the bearing defect in a pump. A criterion called as frequency factor is also proposed to find the best decomposition level for the given high and low Q-factor wavelet decomposition parameters. The transient impulses due to bearing defect are effectively extracted separating traces of oscillatory signature of impeller defect and the noise in the signal. The same has been demonstrated using simulation analysis and experimental study. A comparison of the proposed method with existing signal processing methods is also presented.