Browsing by Author "Pelham, Jonathan G."
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Item Open Access Analysis of short form maintenance records for NFF using NLP, phrase matching, and Bayesian learning(Elsevier, 2017-03-02) Pelham, Jonathan G.; Hockley, ChrisNo Fault Found (NFF) is a well discussed phenomenon within the maintenance sector but which requires work to quantify how much of an issue it may be and provide metrics by which it may be tracked and various approaches to its reduction evaluated. Previous studies have relied on expert classification to identify NFF, however this approach is time consuming and costly. Maintainer classification (MC), expert classification (RC), phrase matching (PM), and Bayesian matching (NBPM) are all evaluated and contrasted as methods to identify NFF. The results demonstrate the utility of all 4 methods and discusses their place within a maintenance ecosystem.Item Open Access Application of an AIS to the problem of through life health management of remotely piloted aircraft(AIAA, 2015-12-31) Pelham, Jonathan G.; Fan, Ip-Shing; Jennions, Ian K.; McFeat, JimThe 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.Item Open Access Monitoring concept study for aerospace power gear box drive train(VDI Verlag GmbH, 2019-09-19) Nowoisky, Sebastian; Grzeszkowski, Mateusz; Mokhtari, Noushin; Pelham, Jonathan G.; Gühmann, ClemensUsing a gearbox in a turbojet engine implies additional monitoring tasks due to new introduced failure modes. This paper outlines monitoring options to address technical diagnosis of the world’s most powerful aerospace gearbox. For this novel technology different monitoring options are assessed to enable the trade between technical effort and monitoring capability. In this paper options to monitor the gears and journal bearings are described. To detect gear wear, pitting, and gear teeth cracks the use of acceleration, acoustic emission sensors, and different methods will be assessed. First stage results are based on Back2Back test run results in occurring pitting and gear teeth loss [1]. The journal bearing mixed friction will be detected by the use of an acoustic emission sensor [3], [5]. Due to the location of the journal bearing in the rotating area of the gearbox a Wireless Data Transfer Unit (WDTU) must be introduced [6], [7]. Results of early subscale component test runs are used to define requirements to adjust the WDTU and accommodate the new power gearbox (PGB) requirements. The electronics of the WDTU must cope with challenges such as the environmental conditions of the gearbox. To extract the mixed friction pattern by the applied signal processing steps from the noise disturbance caused by gear mesh is a technical challenge. Finally the paper closes with a recommendation on how to monitor such a gearbox and provides an outlook to the next test campaign, where the WDTU will be applied based on a back2back configuration of a subscale planetary gearbox [8].