Bayesian reliability modelling for railway infrastructure

This new context driven Bayesian maintenance scenario will promote sustainable and cost-effective asset efficiency optimization in railway PHM and it will help us move closer to the ultimate goal of intelligent maintenance.

This project aims to address the challenge in prolonging lifetime of railway assets by developing new context driven Bayesian maintenance approaches for prognostics and health management (PHM). The major drawback in current railway PHM is most studies are focusing on components’ level but not on system’s level or system of system’s level, which means valuable information can be lost; in particular, as maintenance context has changed. This new context driven Bayesian maintenance scenario will promote sustainable and cost-effective asset efficiency optimization in railway PHM and it will help us move closer to the ultimate goal of intelligent maintenance.

Sponsor: Trafikverket/JVTC

Researchers: Janet Lin

Duration: 2016-2018