Neural Network-Driven Methodology for Predictive Health Monitoring and Aging Management in Nuclear Power Plant Operations

Abstract In the nuclear power plant field, ensuring the safety and longevity of critical components like Class I/II components is paramount. Long-term operation (LTO) and aging management of nuclear power plants (NPPs) are critical for ensuring safe, reliable, and sustainable energy production. LTO involves systematic evaluation and maintenance to extend a plant’s operational life beyond its initial design period. The proposed study introduces a novel approach leveraging neural networks, for feature extraction in predicting the health status of Class II pipe. The methodology uses a digital twin of the pipe, which provides a synthetic dataset crucial for the training of the neural network. The digital twin, represented as a Finite Element (FE) model of a Class II pipe, simulates progressive degradation caused by thinning. This model effectively mirrors the gradual wear and tear experienced by the pipe over time, providing a realistic representation of its aging process. The architecture of the neural network efficiently condenses the input data (sensors readings) into a single, comprehensive signal. This output signal can be considered as the health status of the pipe, and it is then used to forecast the Residual Useful Life of the component. The combination of a digital twin, neural network-based feature extraction, and advanced forecasting techniques provides a comprehensive and reliable tool for assessing the health of nuclear components.