Neural methods in process monitoring, visualization and early fault detection

This technical report is based on five our recent articles: ”Self-organizing map based visualization techniques and their assessment”, ”Combining neural methods and knowledge-based methods in accident management”, ”Abnormal process state detection by cluster center point monitoring in BWR nuclear power plant”, “Generated control limits as a basis of operator-friendly process monitoring”, and “Modelling power output at nuclear power plant by neural networks”. Neural methods are applied in process monitoring, visualization and early fault detection. We introduce decision support schemes based on Self-Organizing Map (SOM) combined with other methods. Visualizations based on various data-analysis methods are developed in large Finnish research project many Universities and industrial partners participating. In our subproject the industrial partner providing data into our practical examples is Teollisuuden Voima Oy, Olkiluoto Nuclear power plant. Measurement of the information value is one challenging issue. On long run our research has moved from Accident Management to more Failure Management. One interesting case example introduced is detecting pressure drift of the boiling water reactor by multivariate methods including innovative visualizations. We also present two different neural network approaches for industrial process signal forecasting. Preprosessing suitable input signals and delay analysis are important phases in modelling. Optimized number of delayed input signals and neurons in hidden-layer are found to make a possible prediction of an idle power process signal. Algorithms on input selection and finding the optimal model for one-step-ahead prediction are developed. We introduce a method to detect abnormal process state based on cluster center point monitoring in time. Typical statistical features are extracted, mapped to n-dimensional space, and clustered online for every step. The process signals in the constant time window are classified into two clusters by the K-means method. In addition to monitoring features of the process signals, signal trends and alarm lists, a tool is got that helps in early detection of the pre-stage of a process fault. We also introduce data generated control limits, where alarm balance feature clarifies the monitoring. This helps in early and accurate fault detection

Understanding the Application and Benefits of Learning-based Methods in Nuclear Science and Engineering

Known as the fourth industrial revolution, digitization is an ongoing trend in all fields, in which various industries are integrating information technologies to support and improve their businesses. Nuclear technology industries have also increased their interest in data-driven methods by leveraging the potential of pattern recognition to identify anomalies and to take actions more rapidly, in areas such as health and monitoring, radiation detection, and optimization. By acknowledging the practicality and popularity of these methods, it is imperative to understand the benefits and barriers of implementing such methodologies to create better research plans and identify project risks and opportunities. This dissertation discusses different technologies and their integration and challenges within the nuclear industry. It is recognized that concepts of complexity and emergent behavior, as well as the importance of such properties as a distinctive aspect of nuclear and radiological engineering problems in which holistic approaches are crucial to innovation. Overall, the development and application of learning-based methods can be promising in the nuclear industry and many related tasks as long as expert knowledge is considered in the desired application to ensure a robust application of such methods. Cross-discipline studies and the creation of benchmarks are highly suggested for future practices

Combining Neural Methods and Knowledge-Based Methods in Accident Management

Accident management became a popular research issue in the early 1990s. Computerized decision support was studied from many points of view. Early fault detection and information visualization are important key issues in accident management also today. In this paper we make a brief review on this research history mostly from the last two decades including the severe accident management. The author’s studies are reflected to the state of the art. The self-organizing map method is combined with other more or less traditional methods. Neural methods used together with knowledge-based methods constitute a methodological base for the presented decision support prototypes. Two application examples with modern decision support visualizations are introduced more in detail. A case example of detecting a pressure drift on the boiling water reactor by multivariate methods including innovative visualizations is studied in detail. Promising results in early fault detection are achieved. The operators are provided by added information value to be able to detect anomalies in an early stage already. We provide the plant staff with a methodological tool set, which can be combined in various ways depending on the special needs in each case