Effective Condition Monitoring of Line Side Assets

In this paper the results of current research into the state-of-the-art in predictive fault detection and diagnosis methods for railway line-side assets is presented. Research to date has mainly focussed on point machines, track circuits and level crossing systems. Within the paper it is demonstrated through the use of data collected from line-side equipment and lab-based test rigs that it is possible to predict failures with sufficient notice to be of use to maintainers and infrastructure owners. It is shown that to acquire useful information from the line-side collected data, a hybrid of quantitative and qualitative techniques must be used that considers both the inherent failure modes of the asset as well its particular operating conditions. It is argued, through the use of examples, that the most appropriate method for robust fault detection is based around generic models that are tuned for a particular instance of an asset. Furthermore, once a fault has been detected, it is necessary to have an a priori knowledge of the symptoms that are observable under fault conditions to reliably diagnose faults