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Abstract: A major life-limiting factor of the UK''s Advanced Gas-Cooled Reactors (AGRs) is the condition of the graphite core. Installation of new measurement equipment is difficult and expensive, therefore maximizing the information gained from existing equipment is highly desirable. The main approach to determining the health of an AGR core is through periodic inspections undertaken during planned outages. However, there is the desire to supplement this inspection activity through the analysis of data gathered as part of routine plant operation. One such source of data is measurements taken during refueling and this paper describes knowledge-directed characterization of this refueling data, both spatially across the reactor core and temporally across the operational lifetime of the core. Characterization provides information relating to the current condition of the reactor core and allows suspected ageing trends to be visualized and confirmed. A standard approach for characterizing reactor core data is presented and applied to a variety of different reactor core parameters. The benefit of this approach is that it allows engineers to distill large volumes of refueling data into a readily understandable format in a short period of time. It also allows hypothesized trends relating to the ageing process within the core to be tested and provides supporting evidence for these hypotheses. The trending data is also valuable as it can form the basis of a predictive model of ageing of the reactor core. The ageing process of nuclear graphite is understood from theoretical and experimental viewpoints and this empirical data, gathered from operating reactors, further supports this understanding. This paper represents the initial exploration of using refueling data to construct a predictive model of AGR reactor core ageing. [Copyright &y& Elsevier]

Unplanned outages can be especially costly for generation companies operating nuclear facilities. Early detection of deviations from expected performance through condition monitoring can allow a more proactive and managed approach to dealing with ageing plant. This paper proposes an anomaly detection framework incorporating the use of the Hidden Markov Model (HMM) to support the analysis of nuclear reactor core condition monitoring data. Fuel Grab Load Trace (FGLT) data gathered within the UK during routine refueling operations has been seen to provide information relating to the condition of the graphite bricks that comprise the core. Although manual analysis of this data is time consuming and requires considerable expertise, this paper demonstrates how techniques such as the HMM can provide analysis support by providing a benchmark model of expected behavior against which future refueling events may be compared. The presence of anomalous behavior in candidate traces is inferred through the underlying statistical foundation of the HMM which gives an observation likelihood averaged along the length of the input sequence. Using this likelihood measure, the engineer can be alerted to anomalous behaviour, indicating data which might require further detailed examination. It is proposed that this data analysis technique is used in conjunction with other intelligent analysis techniques currently employed to analyse FGLT to provide a greater confidence measure in detecting anomalous behaviour from FGLT data. [ABSTRACT FROM AUTHOR]

A critical component of an advanced-gas cooled reactor (AGR) station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. As the core cannot be replaced the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. However, during weekly refueling activities measurements are taken from the core for protection and control purposes. It is shown in this paper that these measurements may be interpreted for condition monitoring purposes, thus potentially providing information relating to core condition on a more frequent basis. This paper describes the data-mining approach adopted to analyze this data and also describes a software system designed and implemented to support this process. The use of this software to develop a model of expected behavior based on historical data, which may highlight events containing unusual features possibly indicative of brick cracking, is also described. Finally, the implementation of this newly acquired understanding in an automated analysis system is described. [ABSTRACT FROM AUTHOR]