Knowledge-based inspection

Increased level of complexity in almost every discipline and operation today raises the demand for knowledge in order to successfully run an organization whether to generate profit or to attain a non-profit mission. Traditional way of transferring knowledge to information systems rich in data structures and complex algorithms continue to hinder the ability to swiftly turnover concepts into operations. Diagrammatic modelling commonly applied in engineering in order to represent concepts or reality remains to be an excellent way of converging knowledge from domain experts. The nuclear verification domain represents ever more a matter which has great importance to the World safety and security. Demand for knowledge about nuclear processes and verification activities used to offset potential misuse of nuclear technology will intensify with the growth of the subject technology. This Doctoral thesis contributes with a model-based approach for representing complex process such as nuclear inspections. The work presented contributes to other domains characterized with knowledge intensive and complex processes. Based on characteristics of a complex process a conceptual framework was established as the theoretical basis for creating a number of modelling languages to represent the domain. The integrated Safeguards Modelling Method (iSMM) is formalized through an integrated meta-model. The diagrammatic modelling languages represent the verification domain and relevant nuclear verification aspects. Such a meta-model conceptualizes the relation between practices of process management, knowledge management and domain specific verification principles. This fusion is considered as necessary in order to create quality processes. The study also extends the formalization achieved through a meta-model by contributing with a formalization language based on Pattern Theory. Through the use of graphical and mathematical constructs of the theory, process structures are formalized enhancing the ability to analyse, compare and transform models. In the example domain all possible connections between critical nuclear processes were formalized providing also for probability-based analysis of weapons acquisition paths that will help design objective-based inspection processes.Increased level of complexity in almost every discipline and operation today raises the demand for knowledge in order to successfully run an organization whether to generate profit or to attain a non-profit mission. Traditional way of transferring knowledge to information systems rich in data structures and complex algorithms continue to hinder the ability to swiftly turnover concepts into operations. Diagrammatic modelling commonly applied in engineering in order to represent concepts or reality remains to be an excellent way of converging knowledge from domain experts. The nuclear verification domain represents ever more a matter which has great importance to the World safety and security. Demand for knowledge about nuclear processes and verification activities used to offset potential misuse of nuclear technology will intensify with the growth of the subject technology. This Doctoral thesis contributes with a model-based approach for representing complex process such as nuclear inspections. The work presented contributes to other domains characterized with knowledge intensive and complex processes. Based on characteristics of a complex process a conceptual framework was established as the theoretical basis for creating a number of modelling languages to represent the domain. The integrated Safeguards Modelling Method (iSMM) is formalized through an integrated meta-model. The diagrammatic modelling languages represent the verification domain and relevant nuclear verification aspects. Such a meta-model conceptualizes the relation between practices of process management, knowledge management and domain specific verification principles. This fusion is considered as necessary in order to create quality processes. The study also extends the formalization achieved through a meta-model by contributing with a formalization language based on Pattern Theory. Through the use of graphical and mathematical constructs of the theory, process structures are formalized enhancing the ability to analyse, compare and transform models. In the example domain all possible connections between critical nuclear processes were formalized providing also for probability-based analysis of weapons acquisition paths that will help design objective-based inspection processes

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

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Recent developments in the application of risk analysis to waste technologies.

The European waste sector is undergoing a period of unprecedented change driven by business consolidation, new legislation and heightened public and government scrutiny. One feature is the transition of the sector towards a process industry with increased pre-treatment of wastes prior to the disposal of residues and the co-location of technologies at single sites, often also for resource recovery and residuals management. Waste technologies such as in-vessel composting, the thermal treatment of clinical waste, the stabilisation of hazardous wastes, biomass gasification, sludge combustion and the use of wastes as fuel, present operators and regulators with new challenges as to their safe and environmentally responsible operation. A second feature of recent change is an increased regulatory emphasis on public and ecosystem health and the need for assessments of risk to and from waste installations. Public confidence in waste management, secured in part through enforcement of the planning and permitting regimes and sound operational performance, is central to establishing the infrastructure of new waste technologies. Well-informed risk management plays a critical role. We discuss recent developments in risk analysis within the sector and the future needs of risk analysis that are required to respond to the new waste and resource management agenda

Medical image classification and symptoms detection using neuro fuzzy

The conventional method in medicine for brain MR images classification and tumor detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. MR images also always contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques, for instance, neural networks, fuzzy logic, neuro fuzzy have shown great potential in this field. Hence, in this project the neuro fuzzy system or ANFIS was applied for classification and detection purposes. Decision making was performed in two stages: feature extraction using the principal component analysis (PCA) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the tumors