Dynamic data-driven assessment of technical mission risk for unmanned aircraft systems

• Applicant: Professor Dr.-Ing. Uwe Klingauf
• Subject Area: Traffic and Transport Systems, Intelligent and Automated Traffic
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 447676110

The safe integration of unmanned aircraft systems into civil airspace presents a major challenge. To address this issue the European Aviation Safety Agency has proposed a risk-based approach, in which aircraft are classified according to their inherent risk. For the "specific" category, which is most relevant for commercial applications, it is envisaged that the aircraft operator must conduct a mission-specific risk assessment before executing flight missions and define counter-measures to reduce identified risks. These countermeasures shall either prevent the critical event "loss of Control" or mitigate the consequences of a loss of control.To further increase flight safety, it may be advantageous to extend this concept by dynamically updating the risk assessment during the flight mission. The aim of this project is to provide a new method that integrates failure prediction techniques for safety-critical aircraft components into a dynamic risk assessment. The combination of stochastic prognostic models with the criticality of possible component failures allows for the continuous estimation of technical risk as part of overall mission risk. The resulting quantified risk supports the operator in the decision-making process, e.g. the necessity to terminate or to modify the flight mission. In this way, an additional prevention barrier is introduced to substantially reduce the probability of loss of control due to a technical failure.Based on existing experience, data-based machine learning methods are promising for the diagnosis and prognosis of the system status. When selecting and implementing suitable algorithms, special requirements with regard to real-time capability and transparency must be considered in the present case. Methods such as fault tree analysis will be used to represent functional dependencies of safety-critical components in the system. Both methods will then be combined in a new approach to quantify the current and future technical mission risk in real-time. The methods developed as part of the proposed project will be applied to a hybrid unmanned aircraft and validated by hardware-in-the-loop and Monte-Carlo simulations.

• DFG Programme: Research Grants