ACOUSTIC EMISSION-BASED STRUCTURAL HEALTH MANAGEMENT AND PROGNOSTICS SUBJECT TO SMALL FATIGUE CRACKS
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One of the major concerns in structural health management (SHM) is the early detection of growing crack. Using this, future consequential damage due to crack propagation can be reduced or eliminated by scheduling maintenance which can prevent costly downtime. Early crack detection can also be used to predict the remaining useful life of a system. Acoustic Emission (AE) is a non-destructive testing (NDT) method with potential applications for locating and monitoring fatigue cracks during SHM and prognosis. The research presented in this dissertation focuses on the structural health monitoring using AE. In this research a correlation between AE signal characteristics and crack growth behavior is established, and a probabilistic model of fatigue crack length distribution based on certain AE signal features is developed. In order to establish the AE signal feature versus the fatigue crack growth model and study the consistency and accuracy of the model, several standard fatigue experiments have been performed using standard test specimens subjected to cyclic loading with different amplitude and frequencies. Bayesian analysis inference is used to estimate the parameters of the model and associated model error. The results indicate that the modified AE crack growth model could be used to predict the crack growth rate distribution at different test conditions. In the second phase of this research, an AE signal analysis approach was proposed in order to detect the time of crack initiation and assess small crack lengths, which happen during the early stages of damage accumulation. Experimental investigation from uniform cyclic loading tests indicated that initiation of crack could be identified through the statistical analysis of AE signals. A probabilistic AE-based model was developed and the uncertainties of the model were assessed. In addition, a probabilistic model validation approach was implemented to validate the results. The developed models were properly validated and the results were accurate. It was shown that the updated model can be used for detection of crack initiation as well as prediction of small crack growth in early stages of propagation. It was found that the novel AE monitoring technique facilitates early detection of fatigue crack, allows for the original life predictions to be updated and helps to extend the service life of the structure. Finally, a quantification framework was proposed to evaluate probability of failure of structural integrity using the observed initial crack length. The outcome of this research can be used to assess the reliability of structural health by estimating the probability density function of the length of a detected crack and quantifying the probability of failure at a specified number of cycles. The proposed method has applications in on-line monitoring and evaluation of structural health and shows promise for use in fatigue life assessment.