Model Network Methodology for Experimental Development of Industrial Monitoring Systems

Industrial systems enable modern life. They benefit tremendously by adapting digi-/ntal communication technologies and leveraging automation algorithms and data availability./nTheir importance to basic human needs such as electricity, heating, food, transportation,/nclothing, and more also means that their constant availability and reliability is impera-/ntive in modern societies. Plant monitoring strategies that can collect information and use/nit to analyze and understand plant behavior is a key technology for optimizing industrial/nsystems. Enabling plant monitoring insights to be communicated to human operators is/nessential to ensure the information can be used. While data-based methods continue to/nfind new applications, model-based methods that incorporate unique plant characteristics/nand industry-specific considerations alone have the dual benefits of explainability and ex-/ntrapolability. By developing plant monitoring systems that enable operators to understand/nplant state, quickly identify developing faults, and mitigate issues before they cause harm,/ndesigners can radically improve industrial system operations and management. Through/nthoughtful human-centered design of the interfaces between human and machine, they can/nelevate the role of industrial operators to orchestrate the plant monitoring system’s set of/nautonomous routines./nThis dissertation presents a methodology for the systematic design and implementation/nof a plant monitoring and operator support system running a fault diagnostic and decision/nsupport engine that can be adapted for a variety of industrial monitoring applications. It then/ndemonstrates, by proof-of-concept application to an experimental thermal-hydraulic facility/n- the Compact Integral Effects Test (CIET) - and advanced control room testbed - the/nAdvanced Reactor Control and Operations (ARCO) facility - the iterative plant monitoring/nsystem development process. The focus of this dissertation is the advanced nuclear power/nindustry and the Fluoride salt-cooled High-temperature Reactor (FHR)./nThis dissertation is organized into eight sections. The first section introduces the back-/nground and motivation for model-based industrial monitoring systems before the second/nsection provides an overview of the state-of-the-art for nuclear and other industry plant/n2/nmonitoring systems before focusing on nuclear industry challenges and opportunities. The/nthird section details the iterative fault diagnostic system development methodology and the/nfourth section describes one approach to decision support and fault mitigation algorithm/ndesign. These sections also walk the reader through an example application. The fifth sec-/ntion then introduces the ARCO-CIET facility used in the case study and the sixth section/ndescribes the operator support and human-machine interface design for ARCO. Finally, the/nseventh section presents the case study plant monitoring system design and results before/nthe eighth section discusses promising applications of the overall design methodology./nThis dissertation presents a methodology with the potential to guide the plant moni-/ntoring system development process across a variety of industries with the following original/ncontributions: a methodology for iterative fault diagnostic system development using in-/nterdisciplinary information, recommendations for choosing plant models to build context/nbetween different monitoring objectives, a methodology for developing decision support rou-/ntines, and guiding principles for plant monitoring system human-machine interface design/nand implementation in modern industrial control rooms.

Advisor: Peterson, Per F