Data Analytics for Reliability and Integrity Management

Abstract

New reactor designs are focused on risk-informed processes to support all stages of development (design, licensing, operation, and retirement). Some of these processes are well-known since they are used for light-water reactors; however, the safety classification of systems, structures, and components (SSCs), development of performance requirements, and application of special treatments are unfamiliar to light-water reactors. More specifically, developing and monitoring performance requirements are a completely new problem. An industry initiative led by the American Society of Mechanical Engineers has been in development for a few years—requirements for a reliability and integrity management (RIM) program for nuclear power plants. The objective is to define, evaluate, and implement strategies to ensure that SSC performance requirements are defined, achieved, and maintained throughout the plant lifetime. This paper provides an overview of the data analytics methods designed to support the RIM program for advanced reactors, and it targets two research directions: SSC reliability target allocations and RIM strategy identification and evaluation. These methods are applied to specific case studies. These analyses present various possibilities and options for meeting RIM program requirements, including considerations of a tradeoff between reliability and economics and design option optimization.