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Highlights: [•] Combining laboratory model outputs with operational data. [•] Isolation of single component from noisy data. [•] Better understanding of the health of graphite cores. [•] Extended plant operation through leveraging existing data sources. [ABSTRACT FROM AUTHOR]

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]

• Automated detection of physical features of interest in fuel channels can save time and reduce the time taken for inspection outages • Automated techniques are robust and repeatable for a given problem removing human subjectivity • Frameworks work very well for both detecting defects and accurately measuring trepanned hole dimensions • Generalisation was shown by testing both frameworks on data sets with imagery recorded from multiple reactors Remote Visual Inspection (RVI) of reactors in nuclear power plants allows station operators to assess the health and condition of their plant. In the UK, most nuclear stations are of the Advanced Gas-cooled Reactor (AGR) design. During planned periodic outages, a representative portion of each AGR core is inspected using specialist tools equipped with various sensors including a video camera for RVI. If cracks are observed in the core during data capture, a stitched image of the region needs to be created so that the crack can be analysed and sentenced (classifying the crack morphology, location, orientation and size) before the station is returned to service, provided return to service is justified. Currently, the crack analysis and sizing activities are conducted manually by expert analysts in a laborious process. In this paper, we present a new image processing approach capable of automating aspects of the crack analysis process. Specifically, we describe a set of techniques for quickly and accurately detecting the presence of cracks in AGR fuel channel inspection images. We also present a method for detecting circular channel features known as trepanned holes whose dimensions are known and can thus be used for scaling. The results of applying the proposed techniques are evaluated on image data from real AGR fuel channels and are shown to produce comparable results to those obtained manually. The advantage of the proposed approach is that it is fast, robust and more repeatable than the existing manual approach. [ABSTRACT FROM AUTHOR]

• Dimensional changes in fuel channels can potentially show signs of defects. • Understanding defects in fuel channels helps support the safety case. • Estimations of dimensional changes can be produced using monitoring data. • Generalization and improved performance was shown using data from multiple reactors. 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. [ABSTRACT FROM AUTHOR]