Data-driven prognostics with low-fidelity physical information for digital twin: physics-informed neural network

In the absence of a high-fidelity physics-based prognostics model, data-driven prognostics methods are widely adopted. In practice, however, data-driven approaches often suffer from insufficient training data, which causes large training uncertainty that hinders the Digital twin (DT)-based decision-making. In such a case, the integration of low-fidelity physics with a data-driven method is highly demanded. This paper introduces physics-informed neural network (PINN)-based prognostics that can utilize low-fidelity physics information, such as monotonicity or the sign of curvature. Low-fidelity physics information is included as a constraint during the optimization process to reduce the training uncertainty in the neural network model by preventing unrealistic predictions. The proposed method is applied to two case studies to demonstrate the effect of reducing the prediction uncertainty and the robustness to the variability in test data. The two case studies show that PINN-based prognostics can successfully reduce the prediction uncertainty and yield more robust prognostics performance than the ordinary neural network.