In-Flight Fault Diagnosis for Autonomous Aircraft Via Low-Rate Telemetry Channel

An in-flight diagnosis system that is able to detect faults on an unmanned aircraft using real-time telemetry data could provide operator assistance to warn about imminent risks due to faults. However, limited bandwidth of the air-ground radio-link makes diagnosis difficult. Loss of information about rapid dynamic changes and high parameter uncertainty are the main difficulties. This paper explores time-domain relations in received telemetry signals and uses
knowledge of aircraft dynamics and the mechanics behind physical faults to obtain a set of greybox models for diagnosis. Relating actuator fin deflections with angular rates of the aircraft, low order models are derived and parameters are estimated using system identification techniques. Change detection methods are applied to the prediction error of angular rate estimates and properties of the test statistics are determined. Techniques to overcome correlations in data and cope with non-Gaussian distributions are employed and threshold selection is obtained for the particular distributions of test statistics. Verification using real data showed that the diagnosis method is efficient and could have avoided incidents where faults led to loss of aircraft.