Fault detection and diagnostics of complex dynamic systems using Gaussian Process Models - nuclear power plant case study

The article examines the use of Gaussian Process Models to simulate the dynamic processes of a Pressurized Water nuclear Reactor for fault detection and diagnostics. The paper illustrates the potential of Gaussian Process Models as a tool for monitoring and predicting various fault conditions in Pressurized Water nuclear Reactor power plants, including reactor coolant flow and temperature variations, deviations from nominal working point or faulty power measurements. The article discusses the characteristics and benefits of Gaussian Process Models and how they can be utilized to improve: the reliability and accuracy of nuclear power plant anomaly detection, fault diagnosis and decision making process in states of emergency. Overall, this paper highlights the capabilities of Gaussian Process Models to enhance the safety, reliability and efficiency of nuclear power plants. The results of this study are expected to provide valuable insights for engineers and researchers in the fields of control engineering and nuclear power.