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Analysis of Big Data Streams to obtain Braking Reliability Information for Train Protection systems
(2017)
The first and last mile of a railway journey, in both freight and transit applications, constitutes a high effort and is either non-productive (e.g. in the case of depot operations) or highly inefficient (e.g. in industrial railways). These parts are typically managed on-sight, i.e. with no signalling and train protection systems ensuring the freedom of movement. This is possible due to the rather short braking distances of individual vehicles and shunting consists. The present article analyses the braking behaviour of such shunting units. For this purpose, a dedicated model is developed. It is calibrated on published results of brake tests and validated against a high-definition model for low-speed applications. Based on this model, multiple simulations are executed to obtain a Monte Carlo simulation of the resulting braking distances. Based on the distribution properties and established safety levels, the risk of exceeding certain braking distances is evaluated and maximum braking distances are derived. Together with certain parameters of the system, these can serve in the design and safety assessment of driver assistance systems and automation of these processes.
Schienenverkehrssysteme stehen in zunehmendem Wettbewerb, sowohl untereinander als auch mit anderen Verkehrsträgern. Als wichtiger Aspekt zur Steigerung der Kosteneffizienz wird die Digitalisierung des Betriebs und der Fahrzeuge betrachtet. Über eine Prognose der Ausfallwahrscheinlichkeit bzw. Restlebensdauer von Subsystemen können mittels Digitalisierung die Instandhaltungskosten gesenkt werden. Die geringen Fehlerraten im System Bahn machen die Nutzung besonderer Simulationstechniken notwendig. In diesem Beitrag wird gezeigt, wie sich die Subsystemverfügbarkeit aus den beobachteten Fehlerraten der Teilfunktionen vorhersagen lässt.