Objective

Condition monitoring of the public transport track network through automatic and continuous localization and evaluation of rail defects

‍

Customer

Rheinbahn AG

‍

Period

In operation since 2017

‍

Challenges

• Clean and selective detection and evaluation of rail defects
• Integration of the measurement system into vehicles in regular operation
• Localization in the track network and display in a map system
• Localization of rail vehicles in the tunnel
• from Big Data to Smart Data at the edge: immediate data reduction from raw time data to relevant defect information decentralized on the moving measurement systems

‍

Realization

• Equipping several trains with acceleration sensors on the wheel bearings and one on-board measuring box each with edge computing capabilities
• Continuous measurement and evaluation of acceleration signals and linking of these with GPS position data
• Localization in the tunnel via beacon system and meter counter
• Power supply via 24 V vehicle-grid
• Data reduction to relevant information about local track condition by edge computing
• Data transmission of temporarily stored data via WiFi using MQTT as soon as the vehicle is in the depot
• Customer-oriented presentation of the acquired and evaluated information in a dashboard

‍

Advantages

• Reduction of loads on the vehicle
• Reduction of noise pollution on residents
• Focusing of maintenance measures
• Data reduction through edge computing by a factor of > 10,000