One of the major life-limiting factors of an Advanced Gas-cooled Reactor (AGR) nuclear power station is the graphite core as it cannot be repaired or replaced and therefore detailed information about the health of the core is vital for continued safe operation. The graphite bricks that comprise the core experience gradual degradation during operation as a result of irradiation. Routine physical inspection of the graphite core fuel channels is performed by specialist inspection equipment during outages every 12 months to 3 years. It has also been shown to be advantageous to supplement this periodic inspection information with analysis of operational data which can provide additional insights into the core health. One such approach is through the use of online monitoring data called the Fuel Grab Load Trace (FGLT). An FGLT is a measure of the perceived load of the fuel assembly with contributions from aerodynamic forces and frictional forces, which is related to bore diameter. This paper describes enhancements to existing analysis of FGLT data which, to date, has focussed solely on using data from a single reactor at a time to build bore estimation models, by considering data from multiple reactors to produce a generalised model of bore estimation. This paper initially describes the process of producing a bore estimation from an FGLT by isolating the contribution that relates to the fuel channel bore and then discusses the limitations with the existing bore estimation model. Improvements are then proposed for the bore estimation model and a detailed assessment is undertaken to understand the effect of each of these proposed improvements. In addition, the effect of introducing non-linear regression models to further enhance the bore estimation is explored. The existing model is trained on data from one reactor in the UK and therefore the results produced from it are only applicable to this reactor. However, out of the remaining 13 nuclear reactors currently in operation, 3 also have a similar construction to the reactor the model is trained on, and these should all produce similar FGLT data. Therefore, a generalised model is proposed that produces bore estimations for four AGRs stations reactors, compared with one previously. It is shown that this approach offers an improved overall bore estimation model.

The use of aerial hyperspectral imagery for the purpose of remote sensing is a rapidly growing research area. Currently, targets are generally detected by looking for distinct spectral features of the objects under surveillance. For example, a camouflaged vehicle, deliberately designed to blend into background trees and grass in the visible spectrum, can be revealed using spectral features in the near-infrared spectrum. This work aims to develop improved target detection methods, using a two-stage approach, firstly by development of a physics-based atmospheric correction algorithm to convert radiance into reflectance hyperspectral image data and secondly by use of improved outlier detection techniques. In this paper the use of the Percentage Occupancy Hit or Miss Transform is explored to provide an automated method for target detection in aerial hyperspectral imagery.

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In critical infrastructure, such as nuclear power generation, constituent assets are continually monitored to ensure reliable service delivery through pre-empting operational abnormalities. Currently, engineers analyse this condition monitoring data manually using a predefined diagnostic process, however, rules used by the engineers to perform this analysis are often subjective and therefore it can be difficult to implement these in a rule-based diagnostic system. Knowledge elicitation is a crucial component in the transfer of the engineer's expert knowledge into a format suitable to be encoded into a knowledge-based system. Methods currently used to perform this include structured interviews, observation of the domain expert, and questionnaires. However, these are extremely time-consuming approaches, therefore a significant amount of research has been undertaken in an attempt to reduce this. This paper presents an approach to reduce the time associated with the knowledge elicitation process for the development of industrial fault diagnostic systems. Symbolic representation of the engineer's knowledge is used to create a common language that can easily be communicated with the domain experts but also be formalised as the rules for a rule-based diagnostic system. This approach is then applied to a case study based on rotating plant fault diagnosis, specifically boiler feed pumps for a nuclear power station. The results show that using this approach it is possible to quickly develop a system that can accurately detect various types of faults in boiler feed pumps.