[{"name":"Home","site_name":"Press | Company | Siemens","description":"","url_str":"\/global\/","level":0,"image":"","base_root":"https:\/\/press.siemens.com","base_nid":"5","base_nodepath":"\/node\/5","base_path":"\/global\/","base_secure_url":"https:\/\/press.siemens.com\/global","children":null}]
It looks like you are using a browser that is not fully supported. Please note that there might be constraints on site display and
usability.
For the best experience we suggest that you download the newest version of a supported browser:
Digital Services: DB Cargo and Siemens Mobility expand cooperation
Cooperation on condition-based and predictive maintenance is extended to the end of 2024 and now also includes 130 Vectron locomotives
Cooperation on condition-based and predictive maintenance is extended to the end of 2024 and now also includes 130 Vectron locomotives
DB Cargo AG has extended the cooperation agreement with Siemens Mobility concluded in 2019 by a further three years. The cooperation in the field of digital services now covers nearly 300 locomotives. Along with the more than 250 locomotives of the 189 and 152 series, the Vectron fleet has also been included in predictive maintenance and servicing since the beginning of this year.
“Intelligent data acquisition and analytics are the basis for predictive maintenance and thus the key to ensuring maximum availability and optimized lifecycle costs,” says Adam Leitner, Head of Customer Services Germany at Siemens Mobility. “We’re pleased that we’ll be continuing our successful cooperation with DB Cargo AG in the future-focused area of digital services in the coming years.”
Background: The start of the cooperation in 2019 marked the first time in Europe that a manufacturer and a railway company joined forces to continuously exchange and evaluate the condition data of locomotives following their delivery. The cooperation aims to increase the availability and thus economic efficiency of the locomotives and use the information gained through data analytics to further optimize maintenance processes and the locomotives.