Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings

Abstract

This paper presents a novel morphological undecimated wavelet (MUDW) decomposition scheme for fault diagnostics of rolling element bearings. The MUDW scheme is developed based on the morphological wavelet (MW) theory for both the extraction of impulse features and noise smoothing in signal processing. The analysis operators and the synthesis operator of MUDW strictly satisfy the pyramid condition. The MUDW scheme is used to extract impulse features from rolling element bearing defect signals imposed with noise. The efficiency of the MUDW scheme used for noise smoothing and the extraction of impulse components is evaluated using the simulated data and measured signals from the bearing test rig. Compared with enveloping demodulation analysis, the MW transform and the traditional wavelet transform (WT), the MUDW decomposition scheme is more effective and suitable for the on-line diagnostics of bearings in rotating machines.