Data mining reactor fuel grab load trace data to support nuclear core condition monitoring

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

Intelligent graphite core condition monitoring

Condition monitoring of graphite cores has increased significantly in recent years, as the safety cases required to continue operation of Advanced Gas Cooled Reactors (AGR) have evolved. This paper describes the development and implementation of intelligent systems designed to automate and formalise the CM analyses and tasks performed within the remit of the Monitoring Assessment Panels, which are currently being rolled out across the AGR fleet. The implementation of such systems is found to depend crucially on the involvement of station staff at each step of design and implementation, which is highlighted by the successful deployment of second iterations of the IMAPS system for managing reactor observations and the BETA system for automatically analysing refuelling data. Though each system was initially based on an individual analysis or tasks, this paper describes more recent work to allow closer integration of the systems as they are developed, in order to maximise the use of the available data while minimising duplication of effort and required operator time

Insights into the high-energy γ-ray emission of Markarian 501 from extensive multifrequency observations in the Fermi era

We report on the γ-ray activity of the blazar Mrk 501 during the first 480 days of Fermi operation. We find that the average Large Area Telescope (LAT) γ-ray spectrum of Mrk 501 can be well described by a single power-law function with a photon index of 1.78 ± 0.03. While we observe relatively mild flux variations with the Fermi-LAT (within less than a factor of two), we detect remarkable spectral variability where the hardest observed spectral index within the LAT energy range is 1.52 ± 0.14, and the softest one is 2.51 ± 0.20. These unexpected spectral changes do not correlate with the measured flux variations above 0.3 GeV. In this paper, we also present the first results from the 4.5 month long multifrequency campaign (2009 March 15-August 1) on Mrk 501, which included the Very Long Baseline Array (VLBA), Swift, RXTE, MAGIC, and VERITAS, the F-GAMMA, GASP-WEBT, and other collaborations and instruments which provided excellent temporal and energy coverage of the source throughout the entire campaign. The extensive radio to TeV data set from this campaign provides us with the most detailed spectral energy distribution yet collected for this source during its relatively low activity. The average spectral energy distribution of Mrk 501 is well described by the standard one-zone synchrotron self-Compton (SSC) model. In the framework of this model, we find that the dominant emission region is characterized by a size ≲0.1 pc (comparable within a factor of few to the size of the partially resolved VLBA core at 15-43 GHz), and that the total jet power (≃1044 erg s-1) constitutes only a small fraction (∼10-3) of the Eddington luminosity. The energy distribution of the freshly accelerated radiating electrons required to fit the time-averaged data has a broken power-law form in the energy range 0.3 GeV-10 TeV, with spectral indices 2.2 and 2.7 below and above the break energy of 20 GeV. We argue that such a form is consistent with a scenario in which the bulk of the energy dissipation within the dominant emission zone of Mrk 501 is due to relativistic, proton-mediated shocks. We find that the ultrarelativistic electrons and mildly relativistic protons within the blazar zone, if comparable in number, are in approximate energy equipartition, with their energy dominating the jet magnetic field energy by about two orders of magnitude. © 2011. The American Astronomical Society

The design of a decision support system for the vibration monitoring of turbine generators

Condition Monitoring (CM) systems monitor the health of expensive plant items such as turbine generators. They interpret turbine parameters by signaling an alarm when pre-defined limits are breached. Often these alarms have no further operational consequence but still require investigation by an expert. This is a time consuming and laborious process due to the volume of data interpreted for each alarm. In order to reduce the burden of alarm assessment, a Decision Support System (DSS) is proposed. The DSS will feature a Routine Alarm Assessment (RAA) module which provides an initial analysis of the alarms, highlighting those with no further operational consequence and enabling the expert to focus on those which indicate a genuine problem with the turbine. The structured approach taken to capture and document the expert knowledge on RAA along with the generation of a module specification and the selected IS techniques are outlined. The implementation of an RAA prototype is discussed along with how this will act as a foundation for a full alarm interpretation and fault diagnostic system

Leveraging knowledge from historic engineering drawings

In the nuclear power industry, paper documents are in regular use as part of the life cycle of asset management, maintenance, and operations. Despite their importance, fully digitizing these documents is problematic due to being expensive and time consuming, while automation is made difficult by a combination of factors, primarily a significant time cost and required expertise by both software engineers and nuclear engineers to design digitization systems, label training data and validate the outputs of untrusted black-box solutions. In addition, a direct digitization of such documents, converting them to an equivalent digital format, is not necessarily the most useful representation of the contained knowledge. In this paper we present a framework for extracting and encoding both knowledge and data utilizing Knowledge Graphs (KGs); extracting the knowledge more efficiently than fully manual digitization, while still keeping the human supervision in place using a Human-in-the-loop approach. We then present a case study on using these techniques to convert engineering drawings into KGs, demonstrating the insights allowed by this form, as well as the advantages it provides to the development of intelligent systems. We show how this form can still be used to rebuild the original document in an equivalent format if desired, as well as how the accessible format makes it easier to analyze the data and develop a variety of other useful applications, such as tying in to other software packages or automatically suggesting redesigns

Early Measurement of Pulsatility and Resistive Indexes: Correlation with Long-term Renal Transplant Function

Purpose: To correlate pulsatility index (PI) and resistive index (RI) measured at early specific intervals after transplantation with 1-year estimated glomerular filtration rate (eGFR) and death-censored transplant survival to assess the long-term prognostic value of these Doppler indexes. Materials and Methods: The local ethics committee was consulted, and no formal approval was required. This retrospective review included 178 consecutive patients (111 male, 67 female; mean age, 43.9 years +/- 13.4 [standard deviation]; age range, 16-72 years) undergoing first deceased-donor renal transplantation between 1997 and 2000. All patients were identified from a prospectively maintained database. Spectral Doppler analysis was performed within 1 week after transplantation in all patients and between 1 week and 3 months after transplantation in 124 patients. Average PI and RI were determined from measurements obtained in the upper, lower, and interpolar regions. For statistical analysis, the x(2) test, analysis of variance, the Student t test, Kaplan-Meier survival plots, and Cox proportional hazards models were used. Results: Within 1 week after transplantation, there was a significant association between PI and 1-year eGFR when analyzed as tertiles (P = .02). Between 1 week and 3 months after transplantation, there was a significant relationship between 1-year eGFR and both PI and RI when comparing the lowest and highest tertiles (47.5 mL/min/1.73 m(2) for PI < 1.26 vs 32.7 mL/min/1.73 m(2) for PI > 1.49 [P = .01], 42.8 mL/min/1.73 m(2) for RI, < 0.69 vs 32.3 mL/min/1.73 m(2) for RI > 0.74 [P = .03]). Both PI and RI were independent predictors of death-censored transplant survival (hazard ratio, 1.68 per unit [P < .001] and 260.4 per unit, respectively [P = .02]). Conclusion: PI and RI in the early posttransplantation period correlate with long-term transplant function and can potentially be used as prognostic markers to aid risk stratification for future transplant dysfunction

Intelligent system applications for power system control and management

Engineers within the electrical power industry are becoming increasingly challenged by the volume of data that must be analysed. In reality, they are interested in the significant information that can be interpreted from the data. Intelligent systems are seen as a solution to this problem, as they are able to interpret data to provide information, thereby offering decision support. This article discusses the use of intelligent systems for decision support applied to power system control and management

Re-engineering a turbine generator diagnostic expert system to support plant wide condition monitoring

This paper looks at re-engineering a turbine generator diagnostic expert system to support plant wide condition monitorin