多传感器监测飞机部件非线性退化评估

飞机部件一般采用多传感器进行状态监控，针对退化过程具有非线性特征的民机典型部件剩余寿命（RUL）预测及评估问题，首先建立了部件性能参数的一般非线性Wiener退化过程，推导出基于多传感器监测数据的剩余寿命预测框架和概率密度函数，随后利用状态空间模型进行隐退化状态估计并同时利用最大期望算法（EM）实现参数递推估计，最后形成了飞机部件多传感器监测下的剩余寿命非线性退化评估方法。通过数值仿真案例和民航发动机剩余寿命预测案例，对比线性退化模型和基于单一传感器监测数据的非线性退化模型，验证了所提方法在提高剩余寿命预测精度的有效性，可为飞机及其部件的剩余使用寿命预测和视情维护决策提供技术支撑。

Nonlinear degradation assessment of aircraft components monitored by multi-sensors

Aircraft components are generally monitored by multi-sensors. This paper investigates the Remaining Useful Lifetime (RUL) prediction and assessment of typical aircraft components with nonlinear degradation process. The general nonlinear Wiener degradation process of component performance parameters is first established, and the prediction framework and probability density function of the RUL based on multi-sensor monitoring data are derived. The state space model and the Expectation Maximization (EM) algorithm are then used to estimate the implicit degradation state and realize the parameter recursion estimation, respectively. Finally, a nonlinear degradation assessment method for the RUL of aircraft components under multi-sensor monitoring is developed. Compared with the linear degradation model and the nonlinear degradation model based on single sensor monitoring data, the effectiveness of the proposed method in improving the accuracy of RUL prediction is verified through the numerical simulation case and civil aircraft engine RUL prediction case. This method could provide technical support for the RUL prediction and condition based maintenance of aircraft and its components.