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Fuel rods in nuclear reactors consist of uranium fuel pellets that are encased in hollow metal tubes made from zirconium alloys. Due to the elevated temperature, pressure, and radiation inside of a reactor, fuel rods can fail during operation. The most common failure mechanism in Pressurized Water Reactors (PWRs) is grid-to-rod fretting wear while for Boiling Water Reactors, debris fretting is most common. Other mechanisms include crud and corrosion failures, and pellet cladding interaction cracks. The failure of a fuel rod can potentially lead to leaking of radioactive material into the primary coolant system. Identifying the individual leaking fuel rod presents a challenge. A magnetostrictive transducers and guided wave technology have been investigated recently with the focus on detection of through wall anomalies such as axial, skewed, circumferential notches and drilled holes. A probe coupled from the top of the rod was used. Due to the fact that the access to the fuel rods might be more practical from the side, a side coupling probe was developed in addition to the top coupling probe. The side coupling probe has gone through a number of evaluations and modifications with the major goal of improving its sensitivity to a pinhole type of defects. Fundamental T(0,1) guided wave mode at frequency range 100 - 350 kHz was used during tests. The probe design and results of mockup tests with known artificial anomalies will be discussed.

In the last decades, a significant portion of research in Structural Health Monitoring has been developed upon the principles of vibration-based methods, where monitored modal properties have been treated as a set of features for damage detection. Unfortunately, factors external to the structural system, such as environmental\slash operational effects, have been shown to mask relevant anomalous patterns. This paper presents a modular Bayesian damage identification framework which considers external effects explicitly, along with other sources of uncertainty. A calibrated finite element model of the Tamar bridge is used in order to identify anomalous features in the bridge main\slash stay cables and its bearings. Displacements, natural frequencies, temperature and traffic monitored throughout one year are used to form a reference baseline, which is compared against a posterior identification with one month of monitored data. The proposed framework allows to account for observation errors, estimation of damage and model discrepancy of the predictive model. Multiple response Gaussian processes emulate the model response surface and its discrepancy enhancing the identification task while minimising costly computations. Results indicate that the main cables and bearings have no structural defects. Although the stay cables strain increased considerably, this could be attributed to the inherent variability of their properties. Finally, a considerable amount of uncertainty is propagated when estimating the proposed damage metric, and some options to reduce it are discussed.

Extracting isolated signal patterns from continuously sampled data and discarding noise is of particular importance in the field of Structural Health Monitoring. Using Acoustic Emission (AE) for passive in-situ inspection, this is considered as a challenging task. Here, ultrasound stress waves are recorded passively to detect damage occurring in a material or structure in real-time. The corresponding waveforms recorded by AE transducers carry information regarding their underlying source mechanism, which can be related to either damage or spurious noises from the environment. Typically, these signals are transient and stochastically distributed in time. Many state-of-the-art AE systems use fixed thresholds to distinguish between relevant signals and noise. However, in practical applications these approaches are inaccurate or even unfeasible due to low signal-to-noise ratio. In this paper, a data driven approach to distinguish between relevant signals and noise is presented. To identify a model from the AE data Platt calibration is used, which was developed to map the output of support vector machines to probabilities. The advantage of this approach is that the mapping between signal characteristics and relevance can be derived from preliminary measurements such as Pencil-Lead Break (PLB) test. In a first step, the distance of the measurements to a baseline is computed. Furthermore, by determining the onset of the PLB signal manually or using a different method such as Akaike information criterion, the measurement can be split in two disjoint sections containing the relevant signal and noise, respectively. Finally, a parametric model is derived from the obtained class-conditional distributions of distances using a curve fitting procedure. Based on this model a characteristic function which serves as a probabilistic measure indicating the presence of relevant signals can be computed. The feasibility of the approach is demonstrated using AE measurements from indentation tests of composite plates.