| Summary: | Structural Health Monitoring (SHM) technology has been widely applied in different industrial areas. The technology promises to reduce the cost of required safety measures and extend the interval between manual inspections by providing a continuous automated monitoring. A major concern in developing ‘robust’ SHM systems, however, is the impact of uncertainty of input parameters in the accuracy and reliability of the monitoring. The purpose of this thesis is implementing advanced statistical pattern recognition techniques capable of considering variations in input parameters, and eventually arriving at a new structural monitoring system immune to uncertainty of parameters. For this purpose, first to show the need for such robust SHM systems in real-world case studies, two different composite structures, namely a T-joint and an airfoil are investigated to statistically evaluate the importance of potential manufacturing/loading errors compared to the presence of delamination. Results of this preliminary stage of the study proved the importance of uncertainty analysis in the development of a reliable and precise SHM system. Next, a complete neural network based SHM system was developed for the airfoil case study to investigate single damage scenarios in the form of artificial delaminations of variety of sizes at different locations. The reliability and robustness of the network was assessed in the presence of noisy input caused by inaccurate production process. It was seen that the poor predictability of the network can only be corrected by adding an oversized database of all the noisy scenarios in the training stage, which is practically unacceptable both time- and budget-wise. Next, a new concept of Signal-to-Noise (SN) ratio analysis in SHM was implemented to weigh the first layer of the neural network in the case of uncertain inputs. This approach worked remarkably well, but still a practical concern persisted and that is the precise estimation of the weighting factors. At last, Gaussian Processes (GP) was proposed to train the SHM system in the presence of large uncertainty effects. The new GP SHM in the given case study proved to be distinctively capable of analyzing the input data and predicting both the location and size of the single damage in the composite structure. === Applied Science, Faculty of === Engineering, School of (Okanagan) === Graduate
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