数字孪生驱动的航空发动机涡轮盘剩余寿命预测

为解决航空发动机涡轮盘剩余寿命在线预测难题，提出一种数字孪生驱动的涡轮盘剩余寿命预测方法。在建立数字孪生模型的过程中，首先，分析涡轮盘疲劳裂纹损伤机理，构建性能退化指标，建立涡轮盘性能退化过程的共性表征模型；其次，分析多种不确定性因素，采用状态空间模型建立涡轮盘性能退化过程的个性表征模型；然后，通过动态贝叶斯网络描述状态空间模型随时间的演化规律，建立涡轮盘性能退化过程的动态演化模型；最后，采用粒子滤波算法实现涡轮盘退化状态追踪和剩余寿命预测，从而完成涡轮盘性能退化数字孪生模型的建立。融合涡轮盘实时传感数据，通过贝叶斯推理实现对该数字孪生模型的动态更新。通过某型涡轮盘试验数据对该方法进行验证，结果表明该数字孪生模型能够较好地解决涡轮盘剩余寿命在线预测问题。

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

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.