Nuclear power plants (NPPs) have been experiencing significant cost challenges to remain competitive with other power industries. This places a large burden on NPPs to minimize costs while sustaining excellent safety records. The Light Water Reactor Sustainability Program is a research and development program sponsored by the United States Department of Energy that supports the industry in overcoming this challenge. One of the main focus areas of the program is plant modernization, with automation as one of the key elements of modernization.NPPs costs are distributed primarily across operations and maintenance activities in the plant. Regulatory burdens, increased market costs for skilled labor, and increased surveillance and maintenance activities in pursuance of extended life cycles of the nuclear fleet are among the main cost drivers at play. Work processes at NPPs involve many reviews and layers of approval to ensure safe execution of operation and maintenance tasks. The work process is burdensome for maintenance activities and involves some disparate processes, systems, and departments across the organization. These activities add costs due to inefficiencies and manual processes. This makes the work process a great candidate for automation technologies.

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The research and development reported here is part of the Technology Enabled Risk-Informed Maintenance Strategy project sponsored by the U.S. Department of Energy's Light Water Reactor Sustainability program. The primary objective of the research presented in this report is to produce a technical basis for developing explainable and trustable artificial intelligence (AI) and machine learning (ML) technologies. The technical basis will lay the foundation for addressing the technical and regulatory adoption challenges of AI/ML technologies across plant assets and the nuclear industry at scale and to achieve seamless cost-effective automation without compromising plant safety and reliability.The technical basis ensuring wider adoption of AI/ML technologies presented in this report was developed by Idaho National Laboratory (INL), in collaboration with Public Service Enterprise Group (PSEG) Nuclear, LLC. To develop the initial technical basis, the circulating water system (CWS) at the PSEG-owned plant sites was selected as the identified plant asset. Specifically, the issue of waterbox fouling diagnosis in the CWS using different types of CWS data is presented to address the said challenge. The approach presented in this report is based on the closed-loop forward-backward process that tries to capture the advancements in data science addressing the explainability of AI/ML outcomes, user-centric interpretability of those outcomes, and how user interpretation can be used as feedback to further simplify the process. A prototype interface is developed to present a focused component-level display of the ML model outputs in a usable and digestible form.

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