PMDT: AI-Enabled Predictive Maintenance Digital Twins for Advanced Nuclear Reactors

Our team made substantial technical progress on various fronts during the course of the program. Multiple milestones were geared towards demonstrating the feasibility of machine learning based predictive maintenance digital twins towards reducing O&M costs, whereas some other milestones actually focused on identifying technical gaps and developing technologies such as humble AI to provide necessary robustness to the ML-based models. We were able to demonstrate in many cases that Machine learning-based methods can be successfully adapted for Nuclear plant environments especially for remote monitoring applications. Detailed analyses were carried out with plant and full scope simulation data along with capabilities of enhanced analytics to assess and set realistic expectations on cost reductions in O&M. These assessments are paving the way for investments towards reactor design improvements as well project planning for SMR projects as they develop and mature in the next few years. Technology developed under this program got direct visibility to GE Hitachi and their utility customers and resulted in positive intents to deploy some of the elements from design phase. The project additionally resulted in several reports, publications, software and data generation that will be useful in deployment and O&M services for BWRX300 fleets.. Authors: Goldfarb, Helena [GE Global Research, Niskayuna, New York (United States)]; Huang, Hao [GE Global Research, Niskayuna, New York (United States)]; Iyer, Naresh Sundaram [GE Global Research, Niskayuna, New York (United States)] (ORCID:0000000289999759); Roychowdhury, Subhrajit [GE Global Research, Niskayuna, New York (United States)]; Saxena, Abhinav [GE Global Research, Niskayuna, New York (United States)]. DOE Contract: AR0001290. Subjects: 21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; 22 GENERAL STUDIES OF NUCLEAR REACTORS; 96 KNOWLEDGE MANAGEMENT AND PRESERVATION; 97 MATHEMATICS AND COMPUTING; Digital Twins; Machine Learning; O&M costs; Preventive Maintenance; Remote monitoring; SMR; Small Modular Reactors