Graph Neural Network Approach to System-Level Health Index and Remaining Useful Life Estimation

Current methods for predicting health index and remaining useful life (RUL) in complex systems struggle to account for performance dependencies between components, leading to inaccurate system-level estimates. This research proposes a novel approach utilizing graph neural networks (GNNs) to improve system-level health index and RUL estimation. GNNs excel at capturing complex interdependencies within a system, making them ideal for this task. The proposed methodology is designed for systems with synchronously sampled process data. To illustrate the application of the proposed approach, we will use the Condensate Extraction Subsystem (CES) of a nuclear power plant (NPP) as a case study. Sensor data like temperature, pressure, and flow rates will be used to train GNNs to predict the overall health and RUL of the CES over time. To evaluate the effectiveness of GNNs, a custom NPP simulator will be used to model the CES under various realistic fault modes across a variety of components. The GNN's performance will be verified and its robustness will be tested under diverse scenarios. This research aims to demonstrate the effectiveness and resilience of GNNs for system-level prognostics. By providing valuable insights for maintenance decision-making, this approach can enhance operational reliability and safety in complex engineering systems. The proposed framework has the potential to be applied across various industries, leading to advancements in predictive maintenance practices.