Estimating Mean Residual Life for a Case Study of Rail Wagon Bearings

This paper develops a prognostics model to estimate the Mean Residual Life of Rail Wagon Bearings within certain confidence intervals. The prognostics model is constructed using a Proportional Hazards approach, informed by imperfect data from a bearing acoustic monitoring system, and a failure database. The model supports prediction within a defined maintenance planning window from the time of receipt of the latest acoustic condition monitoring information. We use the model to decide whether to replace a bearing, or leave it until collection of the next condition monitoring indicators. The model is tested on a limited number of cases, and demonstrates good predictive capability. Opportunities to improve the performance of the model are identified, and the processes necessary and time required to build the model are described. Lessons learned from dealing with real field data will assist those interested in using prognostics to support maintenance planning activities.