Industrial systems enable modern life. They benefit tremendously by adapting digital communication technologies and leveraging automation algorithms and data availability. Their importance to basic human needs such as electricity, heating, food, transportation, clothing, and more also means that their constant availability and reliability is imperative in modern societies. Plant monitoring strategies that can collect information and use it to analyze and understand plant behavior is a key technology for optimizing industrial systems. Enabling plant monitoring insights to be communicated to human operators is essential to ensure the information can be used. While data-based methods continue to find new applications, model-based methods that incorporate unique plant characteristics and industry-specific considerations alone have the dual benefits of explainability and extrapolability. By developing plant monitoring systems that enable operators to understand plant state, quickly identify developing faults, and mitigate issues before they cause harm, designers can radically improve industrial system operations and management. Through thoughtful human-centered design of the interfaces between human and machine, they can elevate the role of industrial operators to orchestrate the plant monitoring system’s set of autonomous routines.
This dissertation presents a methodology for the systematic design and implementation of a plant monitoring and operator support system running a fault diagnostic and decision support engine that can be adapted for a variety of industrial monitoring applications. It then demonstrates, by proof-of-concept application to an experimental thermal-hydraulic facility - the Compact Integral Effects Test (CIET) - and advanced control room testbed - the Advanced Reactor Control and Operations (ARCO) facility - the iterative plant monitoring system development process. The focus of this dissertation is the advanced nuclear power industry and the Fluoride salt-cooled High-temperature Reactor (FHR).
This dissertation is organized into eight sections. The first section introduces the background and motivation for model-based industrial monitoring systems before the second section provides an overview of the state-of-the-art for nuclear and other industry plant monitoring systems before focusing on nuclear industry challenges and opportunities. The third section details the iterative fault diagnostic system development methodology and the fourth section describes one approach to decision support and fault mitigation algorithm design. These sections also walk the reader through an example application. The fifth section then introduces the ARCO-CIET facility used in the case study and the sixth section describes the operator support and human-machine interface design for ARCO. Finally, the seventh section presents the case study plant monitoring system design and results before the eighth section discusses promising applications of the overall design methodology.
This dissertation presents a methodology with the potential to guide the plant monitoring system development process across a variety of industries with the following original contributions: a methodology for iterative fault diagnostic system development using interdisciplinary information, recommendations for choosing plant models to build context between different monitoring objectives, a methodology for developing decision support routines, and guiding principles for plant monitoring system human-machine interface design and implementation in modern industrial control rooms.
|Advisor:||Peterson, Per F.|
|Commitee:||Fratoni, Max, Moura, Scott, Boring, Ronald L.|
|School:||University of California, Berkeley|
|School Location:||United States -- California|
|Source:||DAI-B 81/5(E), Dissertation Abstracts International|
|Keywords:||Cybersecurity, Distributed control systems, Fault detection, Health monitoring, Human-machine interface, Industrial control|
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