Deutsche Bahn and Siemens Launch Predictive Maintenance Pilot Project
Deutsche Bahn (DB) and Siemens are launching a pilot application for the predictive servicing and maintenance of the high-speed Velaro D (Series 407 ICE 3) trains. Impending faults and malfunctions as well as the sources of these problems will be identified at an early point by means of digitalised data analysis, and recommendations for vehicle maintenance then derived from this data. Downtimes can thus be avoided and the fleet’s overall availability increased, resulting in more cost-efficient vehicle operations. The project is currently planned for a term of twelve months.
Jochen Eickholt, CEO of the Siemens Mobility Division, said:
“Siemens offers a digital service, unique in the industry, which makes it possible to precisely align maintenance work with the vehicle’s actual status. With intelligent algorithms and precise analytics, availability is increased.”
For a period of one year, data received from the Velaro D fleet while the trains are under way will be based on and supplement the onboard diagnostics and be systematically analysed. For this purpose, Siemens is the first company in the rail industry to operate a special data analysis centre, the Mobility Data Services Centre in Munich. Data from the vehicles will be received and analysed in a central diagnostics system to calculate failure predictions. These predictions will be used by specialists as the basis for validated recommendations for action and directly communicated to technicians in the DB workshops for either acute or planned maintenance activities. The data analyses are based on algorithms and models that enable highly reliable predictions to be made about the future behaviors of vehicles and components.
All diagnostic data is available to the operator and maintenance staff during operations and is presented in an easily understandable, user-friendly display. The current condition of a vehicle can thus be quickly monitored and appropriate action taken.
Please visit Siemens for the original article.