Scalable Risk-Informed Predictive Maintenance Strategy for Operating Nuclear Power Plants

Over the years, the nuclear fleet has relied on labor-intensive, time-consuming preventive maintenance programs, driving up operation and maintenance costs to achieve high capacity factors. The primary objective of the research presented in this paper is to develop scalable technologies deployable across plant assets and the nuclear fleet in order to achieve a risk-informed predictive maintenance (PdM) strategy at commercial nuclear power plants (NPPs). We developed a well-constructed risk-informed PdM approach for an identified plant asset in this research, taking advantage of advancements in data analytics, machine learning, artificial intelligence, risk models, and visualization. The demonstration and deployment of these technologies would allow commercial NPPs to reliably transition from the current labor-intensive preventive maintenance program to a technology-driven PdM program, eliminating unnecessary operation and maintenance costs. The research and development approach presented in the paper is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear LLC. This paper presents a scalable risk-informed predictive maintenance framework with a brief discussion on scalable predictive modeling approach using the federate-transfer learning approach. The paper describes component to plant-level risk modeling based on a three-state Markov chain and its integration with circulating water system health information using the proportional hazard modeling approach. The state probabilities obtained are used to estimate the profit as part of the economic model. The paper also outlines the development of a user-centric visualization application to ensure the right information is available to the right person, in the right format, and at the right time. The research outcomes presented in this paper lay the foundation and provide a much-needed technical basis to start focusing on additional needs, such as the explainability and trustworthiness of machine-learning- and artificial-intelligence-based technologies as part of future research.