- Main
Model Network Methodology for Experimental Development of Industrial Monitoring Systems
- Poresky, Christopher Morris
- Advisor(s): Peterson, Per F
Abstract
Industrial systems enable modern life. They benefit tremendously by adapting digi-
tal 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 impera-
tive 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 ex-
trapolability. 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 back-
ground 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
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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 sec-
tion 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 moni-
toring system development process across a variety of industries with the following original
contributions: a methodology for iterative fault diagnostic system development using in-
terdisciplinary information, recommendations for choosing plant models to build context
between different monitoring objectives, a methodology for developing decision support rou-
tines, and guiding principles for plant monitoring system human-machine interface design
and implementation in modern industrial control rooms.
Main Content
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