Even after extensive efforts to enhance our understanding of materials, modeling, and system processes, uncertainty continues to be an inevitable factor that impacts system behavior, especially at the operational limits. The evaluation of uncertainty is now a common practice in engineering and scientific fields, encompassing the analysis of experimental data, as well as numerous computational models and process simulations. Non-destructive evaluation (NDE) techniques are widely utilized across a range of industries and applications to guarantee the safety, quality, and dependability of components, systems, and structures. However, NDE processes are often challenged by uncertainties stemming from factors such as material variations, environmental conditions, and measurement limitations, which can introduce complexities into the assessment process. Therefore, there is a need to quantify uncertainties in NDE, which can enhance our comprehension of the constraints and potential inaccuracies linked to NDE inspections and aid in making NDE assessments more robust and reliable. In this thesis, a comprehensive uncertainty quantification (UQ) framework: the Three-Legged Stool (TLS) is proposed to provide systematic guidance in uncertainty analysis for NDE applications. A Magnetic Flux Leakag (MFL) based defect characterization algorithm is proposed to classify the defect and handling uncertainties for pipeline inspection. The research compares Convolutional Neural Network (CNN) and Deep Ensemble (DE) methods for handling input uncertainties from MFL response data, while also employing Autoencoder for data augmentation to address limited experimental data. The study evaluates prediction accuracy and explores uncertainty analysis, emphasizing the importance of reliability assessment in MFL-based NDE decision-making.To estimate the fatigue life of martensitic-grade stainless-steel turbine blades, a magnetic Barkhausen noise (MBN) technique is applied. This work involves the extraction of time and frequency domain features, followed by the application of techniques such as Principal Component Analysis (PCA) and probabilistic neural network (PNN) for classifying and estimating the remaining fatigue life. An IMU-assisted robotic Structured light (SL) sensing system was developed for pipeline detection. This system improves registration and defect estimation through a RANSAC-assisted cylindrical fitting algorithm, integrates inertial and odometry measurements for precise 3D profiling, and employs customized defect sizing techniques to offer a reliable 3D defect reconstruction solution for various defect shapes and depths.The proposed TLS-based UQ framework highlights the interdependent dynamics among data, models, and learning when addressing uncertainties in NDE processes. Some advanced and commonly used techniques have been introduced to illustrate how uncertainties in the inputs or parameters of an NDE system, model, or measurement are propagated to the outputs or predictions. The uncertainty propagation is considered in terms of the forward modeling and inverse learning process separately. In order to demonstrate the efficiency and applicability of the proposed framework for NDE applications, the uncertainties in the previously mentioned NDE cases are investigated and quantified using the techniques outlined in the TLS model.In summary, the proposed UQ framework is able to provide guidance in dealing with uncertainties in NDE inspection with efficient and reliable solutions. It holds great promise and opens up avenues for further research and advancement within the industry.
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