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Machine learning at the edge to improve in-field safeguards inspections

Journal Article · · Annals of Nuclear Energy
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  1. Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
  2. Stanford University, CA (United States)
  3. University of Michigan, Ann Arbor, MI (United States)
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Artificial intelligence (AI) and machine learning (ML) are near-ubiquitous in day-to-day life; from cars with automated driver-assistance, recommender systems, generative content platforms, and large language chatbots. Implementing AI as a tool for international safeguards could significantly decrease the burden on safeguards inspectors and nuclear facility operators. The use of AI would allow inspectors to complete their in-field activities quicker, while identifying patterns and anomalies and freeing inspectors to focus on the uniquely human component of inspections. Sandia National Laboratories has spent the past two and a half years developing on-device machine learning to develop both a digital and robotic assistant. This combined platform, which we term inspecta, has numerous on-device machine learning capabilities that have been demonstrated at the laboratory scale. Here this work describes early successes implementing AI/ML capabilities to reduce the burden of tedious inspector tasks such as seal examination, information recall, note taking, and more.

Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
Grant/Contract Number:
NA0003525
OSTI ID:
2311249
Report Number(s):
SAND--2024-01605J
Journal Information:
Annals of Nuclear Energy, Journal Name: Annals of Nuclear Energy Journal Issue: 1 Vol. 200; ISSN 0306-4549
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION
International Nuclear Safeguards
inspecta
machine learning
nonproliferation