Enhancing Realistic Remaining Useful Life Prediction using Multi-Fidelity Physic-Informed Neural Network Approach

Abstract

The prediction of remaining useful life (RUL) for a component requires a certain failure threshold to be reached. However, using only monitoring data available up to the current time in prognosis can lead to unrealistic RUL predictions. To address this issue, this study proposes a multi-fidelity approach that uses discrepancy predictions to inform a physical model for RUL estimation. Discrepancy predictions are obtained by training the difference between low- and high-fidelity models using a neural network. The low- and high-fidelity models are constructed using the exponential function and monitoring data from experimental work, respectively. As the exponential function has a monotonically increasing trend, a multi-fidelity model can lead to realistic RUL predictions. This proposed method was tested on several failure cases involving rotating components, such as bearings and unbalanced cooling fans. The results show that the proposed method yields realistic and accurate RUL predictions, despite the lack of available monitoring data.