Diagnostic Reasoning using Prognostic Information for Unmanned Aerial Systems

Johann Schumann, Indranil Roychoudhury, and Chetan Kulkarni
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_15_001.pdf2.43 MBAugust 14, 2015 - 9:49am

With increasing popularity of unmanned aircraft, continuous monitoring of their systems, software, and health status is becoming more and more important to ensure safe, correct, and efficient operation and fulfillment of missions. The paper presents integration of prognosis models and prognostic information with the R2U2 (Realizable, Responsive, and Unobtrusive Unit) monitoring and diagnosis framework. This integration makes available statistically reliable health information predictions of the future at a much earlier time to enable autonomous decision making. The prognostic information can be used in the R2U2 model to improve diagnostic accuracy and enable decisions to be made at the present time to deal with events in the future. This will be an advancement over the current state of the art, where temporal logic observers can only do such valuation at the end of the time interval. Usefulness and effectiveness of this integrated diagnostics and prognostics framework was demonstrated using simulation experiments with the NASA Dragon Eye electric unmanned aircraft.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
001
Submission Keywords: 
Battery discharge prognostics; Unscented Kalman Filtering; Unmanned Aerial Vehicle
FPGA
Linear Temporal Logic
Bayesian network
Submission Topic Areas: 
Health management system design and engineering
Model-based methods for fault detection, diagnostics, and prognosis
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