Mr. Matthias Blumenschein, FH Aachen University of Applied Sciences, GERMANY

Prof. Dr. Raphael Pfaff, FH Aachen, GERMANY

Mrs. Katharina Babilon, FH Aachen, GERMANY

Shunting operations differ largely from train operation on the mainline in both train protection and braking. While for mainline operations, braking of all axles in a train set is mandatory under most circumstances and unbraked wagons and axles are an exception, common shunting regulations allow for up to 40 unbraked axles depending on the locomotive mass and track gradient.

On the other hand, train protection in shunting operation is mostly achieved on an on sight basis, which means that no technical devices ensure the freedom of movement. It is rather common to discuss automatic train operation (ATO) these days, however most discussions focus on the mainline portion of freight rail. In mainline operation however, the movement authority is ensured by signalling, so no long range scanning equipment, such as cameras, radar and LiDAR is strictly required to ensure safety of operation. Also, based on the braking behaviour of the train in question, a rather precise velocity recommendation is provided, either by static signals (brake tables, signage) or by continuous communication (e.g. LZB, ETCS).

The opposite holds true for shunting: in most cases, a movement authority is not given or ensured by technical means, rather the observation of the shunting area by the driver and potential assistants check whether the intended track is free and safe. At the same time, the braking behaviour of the shunting groups is not precisely known and predicted by the driver based on instinct and experience. For this reason, to assist and eventually replace the aging workforce of shunting drivers and shunting assistants, a perception system is considered more demanding since it cannot rely on ATP infrastructure as in the mainline case.

In this paper, the special brake setup for shunting mode is analysed and braking curves for numerous cases, ranging from an individual locomotive using direct brake only to a train consist with the maximum number of unbraked axles, are simulated. The simulation software takes into account the train setup, braked weight and brake mode as well as variations in the loading state and the friction and adhesion parameters. The corresponding braking distances are inspected and put into relation to common track geometries and use cases in shunting areas. Further, the visibility requirements from the respective European regulations are reviewed based on these use cases.

The requirement set made up of track geometries, visibility and the respective braking curves provides the input for the generation of a set of requirements for the development of a perception system for shunting operation. Example data from current tests of a candidate sensor system will be shown.

Mr. Simon Westfechtel, FH Aachen University of Applied Sciences, GERMANY

Prof. Dr. Ingo Elsen, FH Aachen, GERMANY

Prof. Dr. Raphael Pfaff, FH Aachen, GERMANY

Mr. Marcel Remmy, FH Aachen, GERMANY

Condition Monitoring (CM) offers opportunities for improvement of safety, availability while typically also reducing maintenance cost. For some asset classes however, large up-front investments are required. Most notably, for the wagon subsystem of the freight rail system, a close monitoring of brake performance using on-board sensors for monitoring of the brake hardware using e.g. force sensor appears expensive and economically not viable.

The approach outlined in this paper picks up on the approach of the author's contribution to Eurobrake 2019, where a big data approach merely based on accelerometer measurements was proposed. This yields the advantage of reducing hardware cost, both initial investment as well as maintenance cost, since accelerometers are everday electronic components which typically show a large lifespan with little degradation. This reduction in hardware cost needs to be compensated by more elaborate computational approaches, since it is based on the contribution of each individual wagon's brake to the braking performance of the entire train consist. By help of randomised mixture of the wagons during train formation, such an approach may be used to detect degradations of individual wagon brakes.

Since such an approach relies on multiple train operations, it is obviously not suited for the replacement of the classical operator based pre-departure check. It may however increase safety and availability on an individual wagon basis due to the ability to observe degradation prior to becoming a functional limitation to be handled during a pre-departure check. Furthermore, it is well suited to supplement novel forms of brake assessments, such as automated brake tests or automated visual inspections and thus provide a higher level of safety and availability at reduced overall cost.

The paper features a brief recap of the proposed method, integrated into the Wagon4.0 concept, which is one potential hardware and software basis as well as a review of big data approaches. The development of a simulation environment suitable for the generation of big data sets based on a headless MATLAB/Simulink setup on a cluster architecture is described. The model features an approximation of the pneumatic behaviour of the brake pipe, a simplified wheel-rail contact model, varying brake block friction as well as a parameter based model of a distributor valve for each simulated wagon.

Thanks to the hardware and software setup, the simulation environment is able to generate braking data in the range of multiple Terabytes, which can then be analysed using big data approaches. Wagon parameters are sourced from a partly randomised digital wagon pool, which can be sent on virtual mission while observing their braking behaviour.

The paper finishes with a preview of the intended analysis, which is currently being implemented on the FH Aachen cluster.