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Title: Structural health monitoring of fatigue cracks using acoustic emission technique
Author: Shamsudin, Mohd Fairuz bin
ISNI:       0000 0004 7972 8546
Awarding Body: Brunel University London
Current Institution: Brunel University
Date of Award: 2019
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In the oil and gas industry, failure due to vibration-induced fatigue is considered as an important cause of loss of integrity. There have been increasing incidents of vibration-induced fatigue in pipelines both in offshore and onshore petrochemical plants. To mitigate this problem, a Structural Health Monitoring (SHM) method using Acoustic Emission (AE) was developed, using specific damage identification strategies. A number of contributions are highlighted in this research work, which includes the fundamental understanding of wave propagation in thick and thin structures. The application of Rayleigh waves and Lamb waves is important in damage identification, being demonstrated in experimental works and finite element analysis (FEA). The identification of damage by vibration-induced fatigue crack demands a dedicated instrumentation and signal processing techniques. A novel method using Bayesian estimation of the effective coefficient (EC) was introduced, which measure the uncertainties of the located events measured by AE technique, calibrated using the information derived from pencil lead break tests. The EC was formulated in such a way the AE signals from each sensor were cross-correlated and the calculated coefficients were corrected taking into account the minimum coefficients from each sensor combination. Cross-correlation was then used to improve the identification of Lamb waves generated by the crack development. The calculated time difference between the fundamental wave modes increases the accuracy of the located events using a Single Sensor Modal Analysis Localization technique (SSMAL). Using a similar approach, the ratio of symmetric (S0) and asymmetric waves (A0) amplitudes was determined and used for damage extent assessments. In summary the AE is shown to be a very effective method for SHM of vibration-induced fatigue damage.
Supervisor: Mares, C. ; Gan, T.-H. Sponsor: National Structure Integrity Research Centre
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Bayesian estimation ; Wavelet ; Waves ; Vibration ; Fine element analysis