A deep learning-based prognostic approach for predicting PWR degradation and remaining useful life using GNN-PTC-LSTM

Key Points

• Remaining useful life estimates for PWR systems show high accuracy through advanced predictive modeling techniques.
• The deep learning method combines GNN and LSTM layers to enhance the predictive capabilities for equipment longevity.
• Assessment using a novel algorithm demonstrates improved forecasting over traditional methods, leading to better decision-making.
• Overall implications suggest that this approach may enable more effective maintenance strategies and reduce downtime.