IoT-based Condition Monitoring of Earthmoving and Mining Equipment

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  • Thesis Topic

    • The Internet of Things is changing the Earthmoving and Mining industry: remote condition monitoring is emerging and automation of up to 70% of today’s manual work can be achieved (McKinsey, 2017).
    • Failure diagnostics of individual components is still in it‘s infancy, but is gaining in importance. 
    • This requires sophisticated machine learning techniques for analyzing huge amounts of sensor data.
    • Within the thesis, students should prove the feasibility of cyber-physical system learning, using hydraulic seals in earthmoving and mining equipment as an example.

    Candidate Requirements

    • Very good theoretical and practical understanding of machine learning, hands on experience in Python
    • (Basic) electronics knowledge is a big plus

    Working Environment

    • Close mentoring from KSRI team and experts in machine learning, electronics, and hydraulics
    • The student will (partly) work in the IoT lab in the recently opened Innovation Center in Stuttgart, including free high-quality coffee, fitness room, gaming room, and much more
    • Start date: Flexible, October 2018 or later 


    If you are interested, send your CV, transcript of records and a brief description of your motivation to martin@kit.edu.