Abstract: Vibration signals acquired from bearing have been found to
demonstrate complicated nonlinear characteristics in literature. Fractal
geometry theory has provided effective tools such as fractal dimension for
characterizing the vibration signals in bearing faults detection. However, most
of the natural signals are not critical self-similar fractals; the assumption
of a constant fractal dimension at all scales may not be true. Motivated by
this fact, this work explores the application of the multi-scale fractal
dimensions (MFDs) based on morphological cover (MC) technique for bearing fault
diagnosis. Vibration signals from bearing with seven different states under
four operations conditions are collected to validate the presented MFDs based on
MC technique. Experimental results reveal that the vibration signals acquired
from bearing are not critical self-similar fractals. The MFDs can provide more
discriminative information about the signals than the single global fractal
dimension. Furthermore, three classifiers are employed to evaluate and compare
the classification performance of the MFDs with other feature extraction
methods. Experimental results demonstrate the MFDs to be a desirable approach to
improve the performance of bearing fault diagnosis.
Keywords: Rolling element bearing, fault diagnosis, feature extraction, mathematical morphology, multi-scale fractal dimensions (MFDs)
