Dynamic data-driven assessment of technical mission risk for unmanned aircraft systems
Final Report Abstract
In this project, the risk assessment of the operation of complex technical systems is investigated using data-based methods. This requires the consideration of degraded states of various subsystems and their interactions. As a use case, the monitoring of the mission risk of unmanned aerial vehicles (UAVs) is considered. Using the example of a hybrid quad-plane configuration, which combines the advantages of a multicopter with those of a fixed-wing aircraft, the subsystems are analyzed with regard to their criticality and subsequently, the actuators (drive and control systems) and the battery are selected as subsystems to be monitored. Diagnostic and prognostic approaches for monitoring the health of these subsystems are developed using test bench data, simulation models and public data sets. A flight simulation model is used to analyze the overall system. This enables an analysis of the influence of functional impairments of several UAV actuators on the flight stability. To simulate the degradation, a decreasing efficiency of the drive motors over time, an increasing variance of the servo actuator control and a decreasing battery capacity over time are modeled. Several UAV mission sequences are simulated, with randomly generated mission profiles supplemented by path planning taking airspace restrictions into account. The influence of wind and different flight modes, including take-off, landing and transition phases between fixed-wing and multicopter flight, is also modelled. For the risk assessment, a method is being developed that estimates the current system status using hidden semi-Markov models (HSMM). For training, the simulated flight data is divided into segments extending until mission failure and clustered using K-Means or decision tree algorithms. The trained HSMMs and decision trees then enable to analyze the probability of a mission failure.
Publications
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Approach to Condition Monitoring of BLDC Motors with Experimentally Validated Simulation Data. PHM Society European Conference, 7(1), 521-529.
Weigert, Max
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Analysis of Diagnostic Capabilities for Degradation of Brushless Direct Current Motors Depending on Varying Simulation Data. PHM Society Asia-Pacific Conference, 4(1).
Weigert, Max
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Varied Simulation Data of Degradation of Brushless Direct Current Motors. Technische Universität Darmstadt
Weigert, M.
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Estimating the System Reliability of Unmanned Aircraft Vehicles with a State Based Approach. In: 2nd International Conference for CBM in Aerospace
Weigert, M.
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Flight Simulation Data from Hybrid UAV Missions Affected by Degradation. Technische Universität Darmstadt
Weigert, M.
