Artificial immune systems for case based reasoning of unmanned aircraft flight data

UAS(Unmanned Aerial Systems) mishaps are high, and their pilots face many control challenges. The reliability of UAS has been seen as a dominant mishap cause but in several instances the aircraft could have been saved if the health state of the aircraft had been understood at an earlier point by the pilot. Manned and unmanned aircraft pilots both benefit from the use of their own experience in the detection and mitigation of faults during flight. However it has been suggested that pilots within a GCS(Ground Control Station) face difficulties in maintaining their situational awareness due to the nature of remote control. The use of a cognitive framework as a basis for case based reasoning is suggested as a way to integrate through life learning into the Safety Management System. The population of the case base for such a system would require a large investment of time to create. The use of machine learning is suggested and evaluated to address this issue by generating cases for CBR. This has seen some success and even the use of an AIS(Artificial Immune System) in this thesis. An AIS was used in order to try to address the problem of cost and time caused by high pre-processing required by common machine learning methods. A simulation of the Aerosonde UAS was created and multiple flights simulated to build up a set of representative set flight data. Several fault cases were included in the simulated flights of varying severities. Different machine learning schemes were evaluated using the data set and their effectiveness compared in order to evaluate the ability of the algorithm to learn from flight data without extensive pre-processing. The complex dataset made the problem difficult but in analysis the AIS performed slightly better than the neural network with which it was compared. In due time and with development it's computational cost could be reduced and its effectiveness increased. The benefit of an automated method to learn from aircraft incidents and mishaps can readily be seen in a fleet scenario where it would be uneconomical to analyse flight data of unmanned aircraft in the same way that it would be done for manned aircraft. This semi-supervised approach reduces personnel requirements and enhances the ability of operators to learn from mishaps by relating mishap cases to the current situation and being transparent in their alerting criteria.