Browsing by Author "Pelham, Jonathan Gerald"

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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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    Friction and wear monitoring methods for journal bearings of geared turbofans based on acoustic emission signals and machine learning
    (MDPI, 2020-03-07) Mokhtari, Noushin; Pelham, Jonathan Gerald; Nowoisky, Sebastian; Bote-Garcia, José-Luis; Gühmann, Clemens
    In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume.