Data-driven condition now- and forecasting of railway switches for

Abstract

This contribution presents a data-based model that exploitsthe power consumed by point engines during bladesmovement of railway switches to detect relevant anomaliesin switch behavior. The model incorporates local airtemperature at the time of the measurement to account forthe significant influence of the environmental conditions onnormal switch behavior. Anomaly detection by the model isvalidated against alerts triggered by the state-of-the-artmonitoring system POSS®, which is based on switchspecific and manually selected reference curves. The databased model leads to less in number and more reliable alertsin comparison to the current version of POSS®. Especiallyfalse alerts caused by temperature effects are significantlyreduced. Furthermore, the high sensitivity of the modelproves to be capable of detecting emerging switch failures atan early stage of development. The detection capabilities ofswitch condition (nowcast) and identification of emergingfailures at an early stage (required for failure forecast)proves that the model is useful for traffic interferenceprevention, condition-based predictive maintenance andswitch health enhancement.