Diagnostics of antifriction bearings through statistical moments

Abstract

Antifriction bearings or the rolling element bearings constitute the most predominant singular group of machine elements encountered in any industry. The performance of the industrial machinery depends primarily on the health of these beatings besides other machine components. A number of condition monitoring techniques are in use and more are emerging to augment industrial performance in terms of plant availability, productivity, and safety. The reasons for the antifriction bearings being the prime target of condition monitoring are basically threefold: first, their large population in industries and commonness to most of the machines; second, existence of definite catalogue life with high scatter resulting from failure through fatigue; and third, susceptibility to frequent damages attributed to a variety of inherent and environmental factors. Statistical moments technique has emerged as a powerful tool in the field of bearing diagnostics besides other applications. Various statistical moments like kurtosis have been used in many areas, such as signal processing, data communication and turbulent flow measurement. The fourth statistical moment, kurtosis is the commonest moment for bearing health monitoring. Both the kurtosis and the skew Sr detect the presence of various types of bearing defects, which cause most bearing failure problems. The lesser variability of Sr with changes in bearing load and speed is advantageous compared to kurtosis. But where there is a need for higher sensitivity to bearing health condition, the higher moments like kurtosis are more suitable because of their higher sensitivity to impulsive signals.