Abstract
Advancements in sensors, communication protocols, data analytics, and visualization technologies are redefining and reshaping the economics of operation, plant performance, and maintenance activities within the power industries. The nuclear industry is currently moving towards digital innovation to (1) address the Nuclear Energy Institute's Delivering the Nuclear Promise Initiative; (2) support life extension of the current domestic nuclear fleet beyond 60 years via improved plant performance; and (3) stay competitive in the domestic energy market. Development and implementation of wireless sensor technologies and data analytics for predictive maintenance are both critical and enabling for this purpose. The paper will discuss a framework that supports the application of advanced sensor technologies (particularly wireless sensor technologies) and data science-based analytic capabilities, to advance online monitoring and predictive maintenance in nuclear plants and improve plant performance (efficiency gain and economic competitiveness). Predictive maintenance will allow plants to better prepare for upcoming maintenance activities by optimizing allocation of resources including tools and labor, resulting in economic benefits and addressing safety by performing timely maintenance and preventing undesirable asset failures and associated consequences.
| Original language | English |
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| Title of host publication | PBNC 2018 - Pacific Basin Nuclear Conference |
| Publisher | American Nuclear Society |
| Pages | 405-409 |
| Number of pages | 5 |
| ISBN (Electronic) | 9780894487743 |
| State | Published - 2019 |
| Externally published | Yes |
| Event | 2018 Pacific Basin Nuclear Conference, PBNC 2018 - San Francisco, United States Duration: Sep 30 2018 → Oct 4 2018 |
Publication series
| Name | PBNC 2018 - Pacific Basin Nuclear Conference |
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Conference
| Conference | 2018 Pacific Basin Nuclear Conference, PBNC 2018 |
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| Country/Territory | United States |
| City | San Francisco |
| Period | 09/30/18 → 10/4/18 |
Funding
The presented framework research is funded by the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Program under the Advanced Sensors and Instrumentation Pathway. The presented framework research is funded by the U.S. Department of Energy's Nuclear Energy Enabling Technologies Program under the Advanced Sensors and Instrumentation Pathway.