Data analysis and predictive maintenance for rail vehicles
Every year, Deutsche Bahn carries well over 100 million passengers in national and international long-distance transport. With comprehensive digitalisation, it is making itself fit for the future.
Together with optiMEAS DB Systemtechnik GmbH is developing a predictive maintenance system with the aim of predicting the "state of health" of relevant drive components of ICEs and being able to schedule targeted maintenance work in the depots and workshops.
In order to develop predictive maintenance algorithms, mathematical-physical models are first required that describe the regular behaviour. In order to be able to compare these models with the real conditions, the operating data of the components must be recorded.
To this end, the following have been developed in various Deutsche Bahn ICE smartRAIL-recording systems in connection with smartI/O-modules from optiMEAS installed.
The measurement data is acquired at high sampling rates and stored in the central cloud (optiCLOUD(called FALKOS at DB) are provided as historical data for analyses.
Every day, around 50 gigabytes of data come together. In order to be able to process this amount of "big data" according to the models, DB uses an analysis cluster based on Hadoop for parallel processing. Current methods for machine and deep learning and parameter determination for neural networks are used. Technologically, open source frameworks such as Tensorflow or Keras are behind it.
In order to be able to apply artificial intelligence in practice, perhaps the most important component is the domain knowledge from the specialist departments and workshops. This is also the experience of the engineers at DB Systemtechnik: only by combining mathematics, IT and application knowledge can patterns be identified that allow anomalies to be recognised and recommendations for maintenance to be made.
Through the cooperation with optiMEAS we have already learned a lot about the vehicles, which can be used for maintenance planning and improves the quality and availability of the fleet.
Rico Gottschald
Physicist and data analyst / DB Systemtechnik GmbH
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