Automating the task of fault detection and diagnosis is crucial in the effort to reduce the
operation and maintenance cost in the nuclear industry. This paper describes a physics …

The data unavailability of critical machinery is an open issue in the field of condition-based
maintenance research. Acquiring signals in all possible health conditions is impractical in …

Fault diagnosis plays a critical role in system health management. However, practical fault
diagnosis encounters several challenges such as limited observational information, system …

Prediction of the autogenous shrinkage referred to as the reduction of apparent volume of
concrete under seal and isothermal conditions is of great significance in the service life …

This work introduces Physics-informed State-space neural network Models (PSMs), a novel
solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous …

An efficient maintenance policy allows for determining the current state of a system
(diagnosis phase) and its future state (prognosis phase). We show in this paper that …

Time-series data prediction, a predominant problem in fault prediction, enables effective and
efficient predictive maintenance. For time-series data, hybrid methods combining data …

For applications in condition-based maintenance of nuclear systems, the assessment of fault
severity is crucial. In this work, we developed a framework that allows for direct inference of …

In this document, a homologous model and one-dimensional line model for centrifugal pump
are described to provide the theoretical background of a pump model to be developed in …

Despite its significant advantages as a baseload, low-carbon energy source, the US nuclear
power industry has faced increasing difficulties in maintaining economic competitiveness in …