TY - GEN
T1 - Technology roadmap to migrate nuclear power plants to data driven monitoring
AU - Al Rashdan, Ahmad
AU - Germain, Shawn St
AU - Agarwal, Vivek
AU - Boring, Ronald
AU - Ulrich, Thomas
AU - Lybeck, Nancy
AU - Smith, James
AU - Ritter, Christopher
AU - Yadav, Vaibhav
N1 - Publisher Copyright:
© 2018 Westinghouse Electric Company LLC All Rights Reserved
PY - 2019
Y1 - 2019
N2 - Data-driven online monitoring of nuclear power plants aims to improve the economic viability of the plants by reducing the cost of operations and maintenance (O&M) activities. This can be accomplished by reducing the labor hours, frequency of activities, materials, and support activities needed. A technology roadmap to migrate plants from a manual inspection process to a data-driven online monitoring process is a systematic guideline to prioritize resource utilization and the amount and/or type of data collected, while taking advantage of improved analytical and visualization techniques to extract better insights from the data. This process maximizes the value of the migration to a data-driven approach, and tackles various change management challenges to the deployment of online monitoring methods. Without an end-state vision and migration plan, plants risk wasting resources by implementing multiple incremental system upgrades as each new technology or process is incorporated. This paper presents a summary of the migration process for each of six elements required to fully or partially automate manual processes in nuclear power plants. These elements are data collection, data management, data analytics, data visualization, value analysis, and change management.
AB - Data-driven online monitoring of nuclear power plants aims to improve the economic viability of the plants by reducing the cost of operations and maintenance (O&M) activities. This can be accomplished by reducing the labor hours, frequency of activities, materials, and support activities needed. A technology roadmap to migrate plants from a manual inspection process to a data-driven online monitoring process is a systematic guideline to prioritize resource utilization and the amount and/or type of data collected, while taking advantage of improved analytical and visualization techniques to extract better insights from the data. This process maximizes the value of the migration to a data-driven approach, and tackles various change management challenges to the deployment of online monitoring methods. Without an end-state vision and migration plan, plants risk wasting resources by implementing multiple incremental system upgrades as each new technology or process is incorporated. This paper presents a summary of the migration process for each of six elements required to fully or partially automate manual processes in nuclear power plants. These elements are data collection, data management, data analytics, data visualization, value analysis, and change management.
KW - Activities automation
KW - Data driven online monitoring
KW - Nuclear power plants
UR - https://www.scopus.com/pages/publications/85070993385
M3 - Conference contribution
AN - SCOPUS:85070993385
T3 - 11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019
SP - 850
EP - 857
BT - 11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019
PB - American Nuclear Society
T2 - 11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019
Y2 - 9 February 2019 through 14 February 2019
ER -