Browsing by Author "McFeat, Jim"

Now showing 1 - 4 of 4
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    Application of an AIS to the problem of through life health management of remotely piloted aircraft
    (American Institute of Aeronautics and Astronautics, 2015-12-31) Pelham, Jonathan G.; Fan, Ip-Shing; Jennions, Ian K.; McFeat, Jim
    The operation of RPAS includes a cognitive problem for the operators(Pilots, maintainers, ,managers, and the wider organization) to effectively maintain their situational awareness of the aircraft and predict its health state. This has a large impact on their ability to successfully identify faults and manage systems during operations. To overcome these system deficiencies an asset health management system that integrates more cognitive abilities to aid situational awareness could prove beneficial. This paper outlines an artificial immune system (AIS) approach that could meet these challenges and an experimental method within which to evaluate it.
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    Artificial immune systems for case based reasoning of unmanned aircraft flight data
    (2017-09) Pelham, Jonathan Gerald; Fan, Ip-Shing; McFeat, Jim; Jennions, Ian K.
    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.
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    Development of probability of detection data for structural health monitoring damage detection techniques based on acoustic emission
    (Stanford University, 2013-12-12) Gagar, Daniel; Irving, Phil E.; Jennions, Ian K.; Foote, Peter; Read, Ian; McFeat, Jim
    Structural Health Monitoring (SHM) techniques have been developed as a cost effective alternative to currently adopted Non-Destructive Testing (NDT) methods which have well understood levels of performance. Quantitative performance assessment, as used in NDT, needs to be applied to SHM techniques to establish their performance levels as a basis for technique comparison and also as a requirement for practical aerospace application according to set regulations. One such measurand is Probability of Detection (POD). This paper reports experiments conducted to investigate the location accuracy of the Acoustic Emission (AE) system in monitoring events from HsuNielson and fatigue crack AE sources as a route to establish the POD of AE in SHM. It was found that fatigue crack tips could be located at 90% POD within 10 mm accuracy.
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    A v-diagram for the design of integrated health management for unmanned aerial systems
    (PHM Society, 2015-10) Heaton, Andrew E; Fan, Ip-Shing; Jennions, Ian K.; Lawson, Craig;; McFeat, Jim
    Designing Integrated Vehicle Health Management (IVHM) for Unmanned Aerial Systems (UAS) is inherently complex. UAS are a system of systems (SoS) and IVHM is a product-service, thus the designer has to take into account many factors, such as: the design of the other systems of the UAS (e.g. engines, structure, communications), the split of functions between elements of the UAS, the intended operation/mission of the UAS, the cost verses benefit of monitoring a system/component/part, different techniques for monitoring the health of the UAS, optimizing the health of the fleet and not just the individual UAS, amongst others. The design of IVHM cannot sit alongside, or after, the design of UAS, but itself be integrated into the overall design to maximize IVHM’s potential. Many different methods exist to help design complex products and manage the process. One method used is the V-diagram which is based on three concepts: decomposition & definition; integration & testing; and verification & validation. This paper adapts the V-diagram so that it can be used for designing IVHM for UAS. The adapted v-diagram splits into different tracks for the different system elements of the UAS and responses to health states (decomposition and definition). These tracks are then combined into an overall IVHM provision for the UAS (integration and testing), which can be verified and validated. The stages of the adapted V-diagram can easily be aligned with the stages of the V-diagram being used to design the UAS bringing the design of the IVHM in step with the overall design process. The adapted V-diagram also allows the design IVHM for a UAS to be broken down in to smaller tasks which can be assigned to people/teams with the relevant competencies. The adapted V-diagram could also be used to design IVHM for other SoS and other vehicles or products.