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The current MTU software uses typically two snapshots per flight to diagnose technical problems. This process shall now be improved by analyzing continuous engine data for a faster and improved diagnosis.
MTU Aero Engines AG has a long history in data science: We perform remote aircraft engine condition monitoring using live engine data since 2006 for our engine maintenance decisions. Based on engine parameters measured on board of the aircraft, the engine health can be assessed and necessary maintenance activities can be triggered. For this, MTU uses an in-house developed software solution called Engine Trend Monitoring (ETM).
We are continuously improving and extending our software in terms of functionality and data handling. One important trend in the industry is the increasing importance of so-called continuous engine operational data (CEOD), with data points recorded every second. These datasets will be included in our future decision-making process, which poses new challenges to our software and infrastructure, including changes in the database design, new approaches in data exploration and data analysis, implementation of services tailored to high frequency engine data, interfaces to data analysis tools and more.
We are looking for a motivated and independent student in the field of computer science with a reasonable background in data science to support us in the definition of these new solutions through a Master’s thesis.
A concept for the workflow on how to integrate continuous engine operational data into ETM shall be developed, showing by example how all the steps of the workflow could be implemented.
For the following points, the work should specify a proposal and realize the suggestion in a proof-of-concept:
- Pre-process continuous engine data, making it available in a standardized form from various external data sources for further processing
- Import of the data via a valid data transfer protocol (HTTPS or SFTP) into the MTU IT environment, where the core system of ETM is running
- Concept for the integration of the data into the currently emerging MTU landscape for data analysis, presumably based on the following tools:
- Tableau or RStudio for visualization
- Apache Spark for data processing
- Apache Kafka for MTU-internal data exchange
- InfluxDB as time series database for storing CEOD
- Development of a simple data analysis use case, proving that pre-processing, import and integration of data have succeeded
- Creation of a concept how interfaces between ETM and the data analysis solution could be realized:
- Integration of results of data analysis services directly in ETM
- Integration of data analysis views in the ETM user interface
- Interface to access the data analysis tool from the ETM user interface and vice versa
The main focus of the thesis is the integration of the data into the MTU environment and the exchange / interfaces between the data analysis tooling and ETM.
For the path to obtain normalized CEOD from a cloud service (either implemented in an MTU Cloud Instance or consumed from a different cloud service, e.g. a data hub), an effortless example shall be set up to show that the import concept is feasible.
- Master studies of Computer Science
- Programming knowledge, preferably in C# or R
- Knowledge of data bases, IT security, internet communication techniques
- Good knowledge of the English language
- Insights into practical work in aviation as an innovative, high-tech industry
- Exciting jobs that carry responsibility and are performed in an atmosphere of team spirit
- A personally assigned contact and individual support services
- Flexible working hours and compensatory time off
- Networking opportunities
Give your career a boost and send us your complete application (Letter of Motivation, CV, Transcript of Records, High School Diploma).
We are looking forward to getting to know you!