Flight Tests of a Remaining Flying Time Prediction System for Small Electric Aircraft in the Presence of Faults

Edward F. Hogge, Chetan S Kulkarni, Sixto L. Vazquez, Kyle M. Smalling, Thomas H. Strom, Boyd L. Hill, and Cuong C. Quach
Submission Type: 
Full Paper
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phmc_17_006.pdf1.01 MBAugust 16, 2017 - 10:23am

This paper addresses the problem of building trust in the online prediction of a battery powered aircraft’s remaining flying time. A series of flight tests are described that make use of a small electric powered unmanned aerial vehicle (eUAV) to verify the performance of the remaining flying time prediction algorithm. The estimate of remaining flying time is used to activate an alarm when the predicted remaining time is two minutes. This notifies the pilot to transition to the landing phase of the flight. A second alarm is activated when the battery charge falls below a specified limit threshold. This threshold is the point at which the battery energy reserve would no longer safely support enough repeated aborted landing attempts. During the test series, the motor system is operated with the same predefined timed airspeed profile for each test. To test the robustness of the prediction, half of the tests were performed with and half were performed without a simulated power train fault. The pilot engages a resistor bank at a specified time during the test flight to simulate a partial power train fault. The flying time prediction system is agnostic of the pilot’s activation of the fault and must adapt to the vehicle’s state. The time at which the limit threshold on battery charge is reached is then used to measure the accuracy of the remaining flying time predictions. Accuracy requirements for the alarms are considered and the results discussed.

Publication Year: 
2017
Publication Volume: 
8
Publication Control Number: 
006
Submission Keywords: 
Aircraft Avionics
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Health management system design and engineering
Model-based methods for fault detection, diagnostics, and prognosis
Verification and validation
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