Bearings are in all moving parts from automobiles and trains to aircraft, production machinery and wind turbines. Bearings deteriorate with time and usage through rolling contact fatigue (RCF). Currently bearings are replaced during maintenance schedules (on-time) or when a bearing has failed.

Condition monitoring (CM) has been applied for years in many industries to avoid unexpected failures and machinery down-time (on-condition). CM is capable of flagging a change in condition in-service, but not yet capable of quantifying the severity of that damage, sometimes leading to premature extraction. This project will develop a tool (the BEARING-EYE system) able to map and interpret the surface damage observed in failing bearings based on the RCF expertise of the University of Southampton.

An innovative and intelligent software based on fractographic analysis - the examination of failed bearings - will be developed to interpret the damage severity and report this as a simple numeric output. Ultimately, the BEARING-EYE will be fleshed-out as an easy-to-use bench-top system that could be operated by maintenance staff to correlate the damage found in bearings with the associated CM data.The BEARING-EYE will provide the other half of the picture to understand the relationship between the outputs of a CM system and the corresponding RCF damage to determine how long bearings could be left running safely thus avoiding unexpected failure and machinery down-time. This will also change maintenance programs from 'on-time' to 'on-condition'.