Siemens Aktiengesellschaft

Ciprian Dan Raileanu

Mathaeus Dejori

Claus Neubauer

Nazif Cihan Tas

Ekkehard Toensing

Frank Popp

Adaptive remote maintenance of rolling stocks is provided by machine-learning () of rules. Existing rules or models are automatically updated. Machine learning () is applied to establish a more efficient rule set. Rules may be replaced, generalized, or otherwise adapted based on interaction () by the dispatchers with the results of the current rules. The acceptance or discarding of an event by the dispatcher is used as a ground truth for supervised machine learning () of a new rule. The machine learning () uses user feedback to update the rule set.

Adaptive remote maintenance of rolling stocks

In a framework for acquiring and analyzing data from a network of sensors, plug-in software interfaces are used to provide scalability and flexibility. Data collection set-up data is exchanged through one or more first plug-in software interfaces with data collection devices, to configure the processor to collect measurement data from the data collection devices. Analysis set-up data is exchanged through one or more second plug-in software interfaces with one or more data analysis software packages, to configure the processor to provide a predefined subset of the measurement data to the data analysis software packages and to accept analysis results from the data analysis software packages. Measurement data and analysis results are subsequently exchanged through the plug-in interfaces.

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Jersey City, NJ, United States of America

Ciprian Dan Raileanu