Boosting holistic railway infrastructure monitoring and health prediction by integrated data sets and analysis

Kurzfassung

The digitalization and automatization of railway infrastructure health surveillance using various kinds of embedded wayside and onboard sensors in combination with common monitoring and inspection motivates a more and more integrated health analysis for all the relevant asset components such as rail, ballast, switches and crossings, point machines, and others. In this context, the present contribution discusses how an integrated research data set - as currently being collected by partners within the Europe's Rail project IAM4Rail - is going to stimulate new research and developments as well as innovative solutions with regard to several use cases from the field of railway infrastructure maintenance. This includes the application of modern AI-based data fusion techniques as well as physical and hybrid modelling approaches.