Utilising statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions
In this paper it is illustrated how statistical residual life estimates of bearings can be used to justify maintenance practices. Residual life estimates are based on Proportional Intensity Models for non-repairable systems utilising historic failure data and corresponding diagnostic measurements. A case study is presented where failure and diagnostic data obtained from roller bearings operating in the dryer section of a paper machine are used to predict future failure times of bearings. If these predictions are compared to the diagnostic measurements, i.e. vibration and lubrication levels, it becomes evident how changes in these diagnostic measurements influence the residual life of the bearings. From this it is possible to justify maintenance practices.