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Research outputs

As an application-oriented research organisation, Fraunhofer aims to conduct highly innovative and solution-oriented research - for the benefit of society and to strengthen the German and European economy.

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Projects

Fraunhofer is tackling the current challenges facing industry head on. By pooling their expertise and involving industrial partners at an early stage, the Fraunhofer Institutes involved in the projects aim to turn original scientific ideas into marketable products as quickly as possible.

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Researchers

Scientific achievement and practical relevance are not opposites - at Fraunhofer they are mutually dependent. Thanks to the close organisational links between Fraunhofer Institutes and universities, science at Fraunhofer is conducted at an internationally first-class level.

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Institutes

The Fraunhofer-Gesellschaft is the leading organisation for applied research in Europe. Institutes and research facilities work under its umbrella at various locations throughout Germany.

Recent Additions

  • Publication
    Cognitive Power Electronics for Smart Drives in Unmanned Aerial Vehicles
    ( 2022)
    Huf, Tobias
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    ; ;
    Lorentz, Vincent R.H.
    ;
    Steinmetz, Harm-Friedrich
    For Unmanned Aerial Vehicles (UAV) with electric propulsion, the motors and propellers are key for safe operation. The propulsion system is exposed to varying loads and harsh ambient conditions, typically reducing the service interval of the whole UAV. A regular estimation of bearing condition enables a more predictable maintenance and more cost-efficient maintenance planning and efforts. In this paper, we propose an interpretable manifold learning approach towards the development of smart drives for UAVs. The approach enables visualization of the bearing condition based on the analysis of motor phase currents of the drone motors as a preceding result used in the following anomaly detection. We applied a sequence of machine learning algorithms to study and compare several healthy and damaged drone motors. The bearing condition was first examined by comparing specific peaks of the motor phase current in the frequency domain. A lower resolution of the spectra is chosen to simplify the visual analysis and enables the interpretation of the type of bearing wear. For use in machine learning, spectra with higher resolution are compressed with a kernel principal component analysis and allow a later optical separability. We show that the visual inspection of the spectra enables human interpretation, whereas highly resolved spectra will improve the separability of motor states in the machine learning process. The feasibility of using anomaly detection with a support-vector machine in a real application is discussed as a further step.
  • Publication
    Cognitive Power Electronics - An Enabler for Smart Systems
    Smart functionalities are a core requirement in modern electrical systems and applications and include the detection of anomalies and faults, condition monitoring, but also advanced predictive and prescriptive analyses, for example the early detection of an undesired system condition and the initiation of corrective measures. At the same time, power electronic devices are at the heart of such electrical and electronic equipment and applications in households, industrial plants or in mobility. They convert and store electrical energy, switch loads, control electrical drives and much more. For this purpose, they continuously record and frequently control parameters such as current, voltage and their change over time. When combined with artificial intelligence, such intelligent power electronics evolve to what we call "Cognitive Power Electronics" - an enabler for the aforementioned systems to become a smart energy network, a smart production plant, or a smart electric motor.
  • Publication
    Proven Power Cycling Reliability of SmartSiC™ Substrate for Power Devices
    ( 2022)
    Guiot, Eric
    ;
    Picun, Gonzalo
    ;
    Allibert, Frederic
    ;
    ; ;
    Schwarzenbach, Walter
    ;
    Drouin, Alexis
    ;
    Béthoux, Jean-Marc
    ;
    Widiez, Julie
    ;
    Rouchier, Severin
    ;
    The Smart Cut™ technology enables the integration of a high-quality SiC layer transfer for device yield optimization, combined with a low-resistivity handle wafer (below 5mOhm.cm) to lower device conduction and switching losses. More than 550000 cycles without any failure have been demonstrated during Power Cycling Tests, with a temperature swing of 120K. Evolution of thermal resistance is within the specification of AQG324 standards (2021 revision). This test is a validation of the reliability of our SmartSiC™ engineered substrate.

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