Risk-Informed Graded Approach for Reliability and Performance Assessment for Advanced Condition Monitoring Techniques

With the shift away from time-based maintenance and toward condition-based maintenance,
and to reduce overall maintenance costs, there has been an upsurge in the usage and development
of advanced condition monitoring (ACM) techniques for real-time monitoring of nuclear power
plant (NPP) components. ACM is particularly useful in the development of digital twins, which are
designed to predict the failure or degradation of
plant components. Successful implementation of ACM requires an assessment to inform
the development of a risk-informed approach to evaluate the use of ACM to meet Nuclear Regulatory
Committee (NRC) regulations for in-service testing (IST) programs. This includes the monitoring and
diagnostics of reactor components and systems in current, new,
and advanced reactors. A key component in ACM is the usage of machine learning (ML)
and artificial intelligence (AI) algorithms that can employ real-time data from instrumentation
and sensors to detect and predict reactor component degradations. Such predictive capabilities enable
early detection of component degradation so as to help plant personnel plan and execute necessary
maintenance. For successful implementation of
ML/AI in ACM such that regulatory requirements are met, a risk-informed graded approach is
needed to assess the reliability and performance of ML/AI for ACM. The American Society for
Mechanical Engineers (ASME) developed their Operations and Maintenance (O&M) Code to provide
guidance on safe, reliable O&M of NPPs. The IST section of the O&M Code specifically establishes
requirements for IST and examination to gauge operational readiness of components in water-cooled
NPPs. This paper presents a
state-of-the-art review of how reliability and risk assessment can be integrated with ACM
to assess component performance. This is followed by different methodologies and approaches for
conducting performance and reliability assessments so as to meet IST requirements for NPP
components.