Siemens to Equip DB Locomotives with Predictive Maintenance

DB Cargo AG has commissioned Siemens to equip its locomotive fleet for condition-based, predictive maintenance. The contract has a term of six years.

The upgrade applies to Siemens locomotives of the 152 Series Eurosprinter ES64F and locomotives of the 170 and 191 Series, both Vectron types. For the 152 Series locomotives, Siemens will retrofit the necessary telemetric systems and network all locomotives with the “TechLOK” system used by DB Cargo.

Predictive Maintenance of DB Locomotives

DB Cargo, Saxony Anhalt © Schnitzel_bank (license)

Predictive Maintenance of Rolling Stock

The telemetric systems continually collect data on the condition of the locomotives. With this data, experts at Siemens’ Mobility Data Services Centre will work with DB Cargo to develop identified applications and data analytics models. They will also use analytics models from Siemens’ Railigent platform that is linked with the Internet of Things operating system MindSphere. With the help of digitalised data analytics, incipient faults and disturbances can be identified at an early stage so workshop visits can be more flexibly planned on the basis of a locomotive’s actual condition. The goal is to optimise the availability and economic efficiency of the locomotives.

Dr. Jürgen Wilder, CEO of DB Cargo, said:

“With the digitalisation of our fleet, we are focusing primarily on one thing: further improving quality for our customers and optimising the locomotive value chain. Close to 1,000 locomotives equipped with the “TechLOK” system are already operating internationally. With the signing of this new cooperation with Siemens, we are intensifying efforts to reach our goal”

Johannes Emmelheinz, CEO of Siemens’ rail service business, added:

“The long-term partnership with Deutsche Bahn is strategically important for us. By linking data analytics and vehicle-specific know-how, we are supporting Deutsche Bahn with its digitalisation program and attaining its goal of hundred-percent availability”

Original article © Siemens.

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