Authors:
Thomas Otte
1
;
Andres Felipe Posada-Moreno
1
;
Fabian Hübenthal
1
;
Marc Haßler
2
;
Holger Bartels
2
;
Anas Abdelrazeq
1
and
Frank Hees
1
Affiliations:
1
RWTH Aachen University, Aachen, Germany
;
2
Deutsche Bahn (DB) Cargo AG, Frankfurt am Main, Germany
Keyword(s):
Pattern Recognition Application, Rail Freight Transport, Real-world Case Study, Shock Data Analysis, Condition Monitoring, Infrastructure Monitoring, Fleet Monitoring.
Abstract:
In various industry sectors all over the world, the ongoing digital transformation helps to unlock benefits for individual components, involved processes, stakeholders as well as the overarching system (e.g., the national economy). In this context, the rail transport sector can particularly benefit from the increased prevalence of sensor systems and the thereby increased availability of related data. As rail transport, by nature, is an integrated transport mode that contains both freight and passenger transport within the same transport network, benefits achieved for the service quality of freight transport also lead to improvements for passenger transport (e.g., punctuality or uptime of rolling stock). This technical paper presents a method to monitor the condition of the existing rail infrastructure as well as the rolling stock by obtaining insights from raw sensor data (e.g., locations and acceleration data). The data is collected with telemetry-units (i.e. multiple sensors integr
ated with a telematics device to enable data transmission) mounted on a fleet of on-rail freight wagons. In addition, the proposed method is applied to an exemplary set of extracted real-world data.
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