Self-tuning diagnosis of routine alarms in rotating plant items

Condition monitoring of rotating plant items in the energy generation industry is often achieved through examination of vibration signals. Engineers use this data to monitor the operation of turbine generators, gas circulators and other key plant assets. A common approach in such monitoring is to trigger an alarm when a vibration deviates from a predefined envelope of normal operation. This limit-based approach, however, generates a large volume of alarms not indicative of system damage or concern, such as operational transients that result in temporary increases in vibration. In the nuclear generation context, all alarms on rotating plant assets must be analysed and subjected to auditable review. The analysis of these alarms is often undertaken manually, on a case- by-case basis, but recent developments in monitoring research have brought forward the use of intelligent systems techniques to automate parts of this process. A knowledge- based system (KBS) has been developed to automatically analyse routine alarms, where the underlying cause can be attributed to observable operational changes. The initialisation and ongoing calibration of such systems, however, is a problem, as normal machine state is not uniform throughout asset life due to maintenance procedures and the wear of components. In addition, different machines will exhibit differing vibro- acoustic dynamics. This paper proposes a self-tuning knowledge-driven analysis system for routine alarm diagnosis across the key rotating plant items within the nuclear context common to the UK. Such a system has the ability to automatically infer the causes of routine alarms, and provide auditable reports to the engineering staff

Self-tuning routine alarm analysis of vibration signals in steam turbine generators

This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques

Investigation of gas circulator response to load transients in nuclear power plant operation

Gas circulator units are a critical component of the Advanced Gas-cooled Reactor (AGR), one of the nuclear power plant (NPP) designs in current use within the UK. The condition monitoring of these assets is central to the safe and economic operation of the AGRs and is achieved through analysis of vibration data. Due to the dynamic nature of reactor operation, each plant item is subject to a variety of system transients of which engineers are required to identify and reason about with regards to asset health. The AGR design enables low power refueling (LPR) which results in a change in operational state for the gas circulators, with the vibration profile of each unit reacting accordingly. The changing conditions subject to these items during LPR and other such events may impact on the assets. From these assumptions, it is proposed that useful information on gas circulator condition can be determined from the analysis of vibration response to the LPR event. This paper presents an investigation into asset vibration during an LPR. A machine learning classification approach is used in order to define each transient instance and its behavioral features statistically. Classification and reasoning about the regular transients such as the LPR represents the primary stage in modeling higher complexity events for advanced event driven diagnostics, which may provide an enhancement to the current methodology, which uses alarm boundary limits

Industrial implementation of intelligent system techniques for nuclear power plant condition monitoring

As the nuclear power plants within the UK age, there is an increased requirement for condition monitoring to ensure that the plants are still be able to operate safely. This paper describes the novel application of Intelligent Systems (IS) techniques to provide decision support to the condition monitoring of Nuclear Power Plant (NPP) reactor cores within the UK. The resulting system, BETA (British Energy Trace Analysis) is deployed within the UK’s nuclear operator and provides automated decision support for the analysis of refuelling data, a lead indicator of the health of AGR (Advanced Gas-cooled Reactor) nuclear power plant cores. The key contribution of this work is the improvement of existing manual, labour-intensive analysis through the application of IS techniques to provide decision support to NPP reactor core condition monitoring. This enables an existing source of condition monitoring data to be analysed in a rapid and repeatable manner, providing additional information relating to core health on a more regular basis than routine inspection data allows. The application of IS techniques addresses two issues with the existing manual interpretation of the data, namely the limited availability of expertise and the variability of assessment between different experts. Decision support is provided by four applications of intelligent systems techniques. Two instances of a rule-based expert system are deployed, the first to automatically identify key features within the refuelling data and the second to classify specific types of anomaly. Clustering techniques are applied to support the definition of benchmark behaviour, which is used to detect the presence of anomalies within the refuelling data. Finally data mining techniques are used to track the evolution of the normal benchmark behaviour over time. This results in a system that not only provides support for analysing new refuelling events but also provides the platform to allow future events to be analysed. The BETA system has been deployed within the nuclear operator in the UK and is used at both the engineering offices and on station to support the analysis of refuelling events from two AGR stations, with a view to expanding it to the rest of the fleet in the near future

Graphite core condition monitoring through intelligent analysis of fuel grab load trace data

As a graphite core ages, there is an increased requirement to monitor the distortions within the core to permit safe continued operation of the station. In addition to existing monitoring and inspection, new methods of providing information relating to the core are being investigated

Ideals in von Neumann algebras and in associated operator algebras

Bibliography: pages 127-130.The compact operators on a Hilbert space are those operators for which the image of the unit ball is relatively compact in the norm topology. These operators form an ideal, in the algebra of all continuous linear operators on the Hilbert space, which is closed in the uniform norm. In the case that the underlying Hilbert space is separable this is the only such ideal, while for non-separable Hilbert spaces the norm-closed ideals are easily characterised by means of cardinal numbers. The algebra of all continuous linear operators on a Hilbert space is a specific example of a van Neumann algebra, and the theory of compact operators and the ideal they form admit certain generalisations to van Neumann algebras. One of the characterisations of the ideal of compact operators is that it is the closure of the ideal of finite rank operators, and hence the closed ideal generated by the finite dimensional projections. Kaftal has considered the ideal of so called algebraically compact operators, which is defined to be the closed ideal generated by the algebraically finite projections in the von Neumann algebra, and has shown that this ideal consists of those operators which map the unit ball to sets which have compact-like properties. This characterisation was generalised to arbitrary norm-closed ideals by Stroh. In this thesis we explore the extent to which norm-closed ideals in van Neumann algebras resemble the ideal of compact operators on a Hilbert space. We extend the theory developed by Kaftal and Stroh, and show that arbitrary ideals in van Neumann algebras can be characterised in terms of homologies and topologies. We also consider continuity characterisations of norm-closed ideals in von Neumann algebras, generalising the characterisation of the compact operators as being those that are continuous from the unit ball equipped with the weak topology, to the Hilbert space equipped with the norm topology. Furthermore we briefly consider sequential continuity characterisations as first analysed by Kaftal in the case of the algebraically compact ideal. Finally, in the case of a semifinite von Neumann algebra equipped with a faithful semifinite normal trace T, we generalise the characterisation of the compact operators given in terms of the singular value sequence, by showing that the ideal of T-measurable operators whose generalised singular function decreases to 0 possess many of the same properties as the ideal of compact operators

Control rod monitoring of advanced gas-cooled reactors

The UK’s fleet of Advanced Gas Cooled Reactors (AGR) are approaching, and have in some cases exceeded, their original design lives. Continued operation is under enhanced safety cases based on monitoring, inspection and component condition assessment of the core and related systems. This paper presents an analysis of the regulating control rods of an AGR, which are used to manage the power and reactivity of the core. Current manual analyses attempt to detect possible restrictions in the motion of the rods due to degradation of the graphite core, however the development of an automated intelligent analysis of the control rod data provides a repeatable and auditable method of analyzing the data. It is shown, by means of an example data set, that despite some limitations in the scope of the recorded data, it is possible to estimate the performance of the rods and present this information to the engineer in a way that more easily indicates abnormal behavior than existing analyses. It is also noted that though this work was initially conceived as a method of detecting restrictions in the motion of the regulating control rods, the results are potentially more useful is characterizing control rod performance and has potential application in predictive maintenance

Graphite core brick crack detection through automated load trace analysis

This presentation looks at graphite core brick crack detection through automated load trace analysi

Automated video processing and image analysis software to support visual inspection of AGR cores

Remote visual inspection of fuel channels in Advanced Gas-cooled Reactor (AGR) cores provides nuclear operators with an understanding of the condition of the UK’s fleet of nuclear power plants. During planned, periodic outages, specialist inspection tools equipped with video cameras and other sensors are manipulated inside fuel channels selected for inspection and a video of the entire channel bore is recorded for each. If cracks are observed in this process, a montage of the entire crack region needs to be: produced, analysed and sentenced (classifying the crack morphology, location, orientation and size) before the station is returned to service – provided it is safe to do so. At the present time, the video analysis and crack montage production is done manually by an expert team of inspection engineers. In line with this process, bespoke image stitching software named “ASIST” (Automated Software Image Stitching Tool) has been trialled in the last 12 months and evaluated using data from: Dungeness, Hunterston B, Hinkley Point B, Heysham 1 and Torness outages. The software is now almost ready to replace the manual process and will provide higher quality images with 100% channel visualisation properties in a fraction of the time taken by the current approach. This paper provides a summary of the ASIST evaluation undertaken in the last year. It also describes recent research endeavours aiming to provide ASIST with: crack detection techniques; keyway locating algorithms and methods to compute Structure-from-Motion which will facilitate the extraction of 3D depth information directly from the 2D video footage