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Title: Characterization of fatigue damage types in fibre reinforced composites utilizing pattern recognition techniques applied to acoustic emission signals
Author: Tang, Jialin
ISNI:       0000 0004 7968 3325
Awarding Body: Brunel University London
Current Institution: Brunel University
Date of Award: 2019
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The 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.
Supervisor: Mares, C. ; Soua, S. Sponsor: National Structural Integrity Research Centre (NSIRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: acoustic emission ; wind energy ; condition monitoring ; pattern recognition ; composite materials