A mechanics and signal processing based approach for estimating the size of spall in rolling element bearing

Abstract

Rolling element bearing (REB) is a critical component in any rotating machinery. It is subjected to surface damage during operation due to localised fatigue. Any prediction about damage severity helps in effective maintenance planning. Defect size estimation is one of the methods of assessing damage severity in REB and is the subject of this study. Several researchers have formulated analytical as well as signal processing based methods to estimate the defect size in REB. The study proposes a method that is a blend of a mechanics-based approach and signal processing approach for estimation of the size of the defect. The mechanics-based approach is based on the Hertzian contact theory and considers bearing operational parameters along with the effect of the load zone. The analogy of correlating peak acceleration at impact with force in the load zone is proposed and validated experimentally. The signal processing approach for locating entry and exit events encompasses concepts of cross-correlation, Variational mode decomposition (VMD), curve fitting, and root location. Parameter optimization of VMD is done using non dominated sorting particle swarm optimization. The proposed method is used to estimate the size of the defect, which is in the form of an artificially generated spall. Experiments are performed at various speeds and sizes of spall on outer as well as inner raceways. The results advocate the improved efficacy of the proposed method in estimating the size of artificially generated spalls.