Demonstration of a Digital Twin Framework for Model-Based Operational Condition Monitoring of Crossing Panels

Abstract

The wheel transition area in railway crossings is subjected to impact loads that cause an accumulation of structural degradation in crossing panels over time. This degradation leads to high maintenance costs and possibly traffic disturbances. There is therefore a demand from infrastructure managers to monitor the condition and predict maintenance needs for these assets without the need for regular on-site inspections. One solution for operational condition monitoring is to observe the structural response of the crossing under traffic loading via embedded accelerometers. From these measurements, relative changes in track dynamics over time can be observed. To derive a condition or predict maintenance needs, however, these measured accelerations need to be related to the status of the asset. A framework for this where measurement data, simulation models and maintenance history are combined to build an online model that can assess the status and predict future maintenance needs for a material asset is often called a Digital Twin. This paper will present a Digital Twin framework that uses measured accelerations, climate data, scanned running surface geometry and a multi-body simulation (MBS) model to estimate the status and degradation rate of crossing panels. Method developments for this framework are demonstrated for two in situ crossings.