Xiang-Yin Meng

Application of Feature Fusion-Driven Physics-Informed Neural Network in Remaining Useful Life Prediction

Addressing the issues of traditional data-driven methods lacking interpretability and physics-informed neural network (PINN) being susceptible to noise due to statistical feature inputs, this paper proposes a feature fusion-based physics-informed neural network method for predicting the remaining useful life (RUL) of b...