Condition monitoring in engine development
Expert: Michael Scharpf
Industry: Automotive & Engineering
Learn how to improve the long-term quality of engines in the automotive industry through effective condition monitoring.
OUR AI AND DATA SCIENCE Case studies:
EXPERIENCE FROM OVER 2,000 CUSTOMER PROJECTS
Our customer from the automotive industry had the need to monitor the long-term quality of components in the area of fuel systems. Our task was to develop a concept for data analysis and validate a prototypical application to optimise the condition monitoring of the engine.
To validate the concept and optimise the data analysis, we consolidated the existing database queries and added more data. We extended the data analysis by adding more engines, the scope of which can be varied via an external control list. The optimised database queries and the extended data were then further developed into a fully automated ETL load path.
The extended data enabled us to create vehicle life histories and analyse series-specific fault histories based on the long-term quality data. Our customer was able to improve the quality of the fuel systems and optimise the condition monitoring of the engine.
For the data analysis, we used data science methods to consolidate and extend the existing database queries. We used machine learning methods to automate the analysis and improve the results. The ETL load path was implemented using Apache Kafka and Apache Spark. By using these technologies, we were able to make the data analysis more effective and efficient.
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Michael Scharpf | Sr. Principal Key Account Manager | Alexander Thamm GmbH