多传感器监测飞机部件非线性退化评估

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.