Bearing Health Condition Monitoring- A Brief Exposition

Industrial machines run continuously to meet the set target. These machines comprise different components, and the rolling element bearing is one such component. Bearings provide smooth relative motion between two moving parts. In an industry where the machines are working continuously in hostile operating conditions, the bearings experience heavy loads. Hence, it leads to faster deterioration of the bearing's health and starts surfacing in the form of various defects in the bearing components. If these defects are not identified in the developing stage, it will cause the disastrous failure of the complete system. Hence, bearing condition health monitoring is a crucial aspect of machinery health monitoring, and a substantial amount of study has been done in this area. Bearing fault signatures can be identified by using vibration data, acoustic signals, bearing temperature, wear debris analysis, etc. or in a combination of these parameters. The vibration data measurement is a quick, accurate and mostly followed technique in the bearing condition monitoring field. Various time, frequency & time-frequency domain vibration analysis methods are used to extract the bearing fault signatures from the vibration signal accurately. This review paper consolidates the methods used in bearing condition monitoring using vibration data and evaluates them based on their merits, demerits and limitations.