Browsing by Author "Khan, Faisal"
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Item Open Access Swarm Eye: A Distributed Autonomous Surveillance System(Cranfield University, 2011) Khan, Faisal; Mehnen, JornHaving precise information in complex, dynamic, highly demanding and stressful situations is of utmost importance for quick, objective and well informed decision making. Knowing the exact position of a unit in complex, unknown or confusing environment such as ragged mountain areas or narrow labyrinthine alleys of a city can be very challenging and is of high relevance in tactical decision making. Conventional means such as GPS and satellite photos are important information sources but provide only limited and static information. In tactical situations rich 3D images and dynamically self‐adapting information are needed to overcome this restriction; this information should be collected where it is available. Distributed ground teams or swarms of UAVs can provide different and dynamic views of a tactical scene. Swarms are sets of interconnected units that can be arranged and coordinated in any flexible way to execute a specific task in a distributed manner. The Swarm Eye is a concept that provides a platform for combining the powerful techniques of swarm intelligence, emergent behaviour and computer graphics in one system. Swarm Eye allows the testing of new image processing concepts for a better and well informed decision making process. By using advanced collaboratively acting eye units, the system can observe, gather and process images in parallel to provide high value information. To capture visual data from an autonomous airborne unit, the unit has to be in the right position in order to get the best visual sight. This Swarm Eye system also provides autonomous formations for UAV or airborne units to form a better formation autonomously in distributed manner in accordance with the situation.Item Open Access Vehicle level health assessment through integrated operational scalable prognostic reasoners(Cranfield University, 2015-11) Khan, Faisal; Tsourdos, Antonios; Sreenuch, TarapongToday’s aircraft are very complex in design and need constant monitoring of the systems to establish the overall health status. Integrated Vehicle Health Management (IVHM) is a major component in a new future asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimising downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics and diagnostics engine and providing recommended maintenance actions. The data driven prognostics methods usually use a large amount of data to learn the degradation pattern (nominal model) and predict the future health. Usually the data which is run-to-failure used are accelerated data produced in lab environments, which is hardly the case in real life. Therefore, the nominal model is far from the present condition of the vehicle, hence the predictions will not be very accurate. The prediction model will try to follow the nominal models which mean more errors in the prediction, this is a major drawback of the data driven techniques. This research primarily presents the two novel techniques of adaptive data driven prognostics to capture the vehicle operational scalability degradation. Secondary the degradation information has been used as a Health index and in the Vehicle Level Reasoning System (VLRS). Novel VLRS are also presented in this research study. The research described here proposes a condition adaptive prognostics reasoning along with VLRS.