The Institute of Flight Systems and Automatic Control (FSR) has been conducting successful research in the field of Prognostics and Health Management (PHM) for many years and can look back on a large number of successfully completed industrial projects.
The aim of the research is to use intelligent sensor technology and algorithms to estimate the current status of a system, predict future changes in the status and derive recommendations for the system operator on how to proceed based on these estimates. By using PHM approaches, unexpected system failures can be avoided and the entire maintenance process, such as spare parts logistics, can be optimized. The methods developed thus contribute to increasing system safety and at the same time offer the potential to save costs.
As part of various research projects, the FSR is working on the development of PHM methods for use on complex systems in aviation. This offers a wide range of potential applications, including components of UAV systems, helicopters or modern commercial aircraft. The expertise of the FSR includes the development of algorithms for condition assessment as well as the creation of suitable concepts for sensor technology and signal processing. By setting up and operating its own test benches, the FSR has the expertise and possibilities to test and evaluate the developed methods and algorithms on real systems.
A deeper insight into our research is presented in our article about predictive maintenance in aviation, published in the “Ingenieurspiegel” (March 2017 issue), which can be found here (opens in new tab) .
Our current projects in this field include
DFG – Evaluation of the Technical Mission Risk of Unmanned Aerial Systems (2021-2024)
The evaluation of the technical mission risk of unmanned aerial systems is being progressed at the FSR funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). The goal of the project is to aggregate all technical risks of an unmanned flight operation in a dynamic data-based estimation. For this purpose, suitable methods from the field of machine learning will be combined at both the component and system level. More…
smartHUMS (2020-2023)
The project smartHUMS is a joint research project funded by the Federal Ministry of Economics and Energy (BMWi) as part of the Aviation Research Programme (LuFo VI-1). The aim of the project is to develop a Health and Usage Monitoring System (HUMS) for monitoring and forecasting the technical condition of helicopter gearboxes and rotor blade e-actuators. The focus is on the coordinated integration of sensor technology and machine learning algorithms for remaining useful life (RUL) estimation. More…
RTAPHM (2019-2022)
The RTAPHM (“Real Time Analytics, Prognostics and Health Management”) project, a research project funded by the German Federal Ministry of Economics and Energy from the German Aeronautics Research Programme (LuFo V-3), is concerned with the development of a digital platform for automated service provision for Unmanned Aerial Vehicle (UAV) fleets. More..
INDI (2018-2021)
INDI (“Intelligent Data Utilization in Maintenance“) is a LuFo (Aviation research program of the Federal Ministry of Economics and Energy) funded joint research project with project partner Lufthansa Technik. Within this project, the FSR assesses the development of intelligent algorithms and data analysis strategies in the field of machine learning and artificial intelligence in order to optimize the maintenance of aircrafts. More …
