Abstract
As critical components of spacecraft systems, electronic equipment requires accurate remaining useful life (RUL) prediction to ensure reliable operations. Traditional physics-based and data-driven methods are limited by poor generalization and substantial data demands, respectively. While Physics-Informed Neural Networks (PINNs) integrate physical laws to enhance accuracy, they remain susceptible to performance degradation under shifting operational conditions due to catastrophic forgetting. This paper proposes a novel online PINN framework integrated with continual learning to dynamically adapt to new degradation patterns. Validated on NASA's IGBT dataset, our method reduces RUL prediction error to 27.2% of a traditional LSTM model and achieves an online update time of less than 800ms/cycle. These results demonstrate a significant improvement in robustness and accuracy, providing a superior solution for RUL estimation of aerospace power electronics under extreme environments.