Prediction of remaining useful life for electronic equipment based on online PINN

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.