Monitoring degradation through predictive maintenance of components in load traffic

Increasing the availability of trucks through predictive maintenance

Over 20 components are detected in real time.

Preventive repairs can be carried out more quickly. 


Creation of a failure prediction dataset based on high-resolution telematics data, fault memory records and repair information. An ensemble model combines the predictions of different prediction models to provide the most reliable failure prediction in this case. The solution is run on the Hadoop cluster in speed mode for parallelised computation with Spark.



Failures of more than 20 components in the truck are detected and reported in real time. Based on this, the transport company can initiate measures for preventive repair.

Are you interested in your own use cases?


An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.


There will be a interactive and Flexible application, including of different maps with two different views implemented.


Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.