Digital Twin Driven Remaining Useful Life Prediction for Aero-engine Turbine Discs

Abstract

A digital twin (DT) based remaining useful life (RUL) prediction method is proposed for on-line RUL prediction of aero-engine turbine disc. In the proposed DT model, a common representation model is first developed to depict the performance degradation process of the turbine disc based on the fatigue damage mechanism. Then, an individual representation model is established by using the state-space model with uncertainty analysis. Next, dynamic Bayesian network is employed to construct the dynamic evolution model, which depicts the dynamic performance degradation process of turbine disc. Finally, particle filter is used to make the DT model capable of tracking the performance degradation and predicting the RUL for turbine disc. Specially, the real-time sensor data is collected to update the DT model by Bayesian inference algorithm. The fatigue life test of turbine disc is carried out to validate the effectiveness of the proposed method. The results show that the DT model is capable to solve the on-line RUL prediction problem of turbine disc.