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dc.contributor.advisorMares, C-
dc.contributor.advisorSoua, S-
dc.contributor.authorTang, Jialin-
dc.descriptionThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University Londonen_US
dc.description.abstractThe aim of this work is to develop advanced acoustic emission (AE) techniques to investigate the behaviour and failure of complex composite structures in a fatigue loading environment. The work focuses on using acoustic emission to detect and characterize damage mechanisms within composite structures. A pattern recognition technique is developed to characterize different acoustic emission activities corresponding to different fracture mechanisms. Pattern recognition techniques are based on the classification between different acoustic emission signal types using signal features. Any parameters that affect the acoustic emission signal features will have an impact on the pattern recognition results. One of the main parameters that can alter the features of acoustic emission signals is sensor frequency characteristics. This effect is initially investigated using simulated acoustic emission waves and then using acoustic emission signals acquired during lab based experiments carried out on both metal and composite materials with a number of different types of sensors used. Variations in acoustic emission signal features of the signals obtained from different sensors are analysed. A pattern recognition method is developed to identify the characteristics of the acoustic emission signals from plastic deformation. Another important parameter that influences the acoustic emission signal features is the distance of wave propagation from acoustic emission source to the sensor. Acoustic emission signals lose energy as they propagate within the medium. This effect is called attenuation. An investigation of the effect that attenuation might have to the acoustic emission signals related to monitoring of failures in GFRP laminates used in wind turbine blades is carried out. The developed pattern recognition method is applied for damage characterization. Finally, based on the knowledge obtained through the work above, a laboratory study is reported regarding fatigue damage growth monitoring in a complete 45.7 m long wind turbine blade. The damage growth is successfully located and characterized.en_US
dc.description.sponsorshipNational Structural Integrity Research Centre (NSIRC)en_US
dc.publisherBrunel University Londonen_US
dc.subjectacoustic emissionen_US
dc.subjectwind energyen_US
dc.subjectcondition monitoringen_US
dc.subjectpattern recognitionen_US
dc.subjectcomposite materialsen_US
dc.titleCharacterization of fatigue damage types in fibre reinforced composites utilizing pattern recognition techniques applied to acoustic emission signalsen_US
Appears in Collections:Mechanical and Aerospace Engineering
Dept of Mechanical Aerospace and Civil Engineering Theses

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