EMA remaining useful life prediction with weighted bagging GPR algorithm

Electro-mechanical actuators (EMAs) are one type of the key components for the next generation aircraft. In order to ensure its safety and reliability, it is critical to predict the remaining useful life (RUL) of EMAs. The data-driven RUL prediction can be implemented by utilizing Gaussian process regression (GPR) due to its uncertainty representation and nonlinear modeling capability. In order to enhance the stability and achieve high precision of EMA RUL prediction, a weighted bagging GPR (WB_GPR) algorithm is presented in this work, in which ensemble learning is utilized. To be specific, the degradation features for EMA RUL prediction are analyzed and the parameters which can represent the degradation process and health status of EMAs are selected. Then the data-driven framework which estimates the RUL of EMAs is implemented with the proposed WB_GPR algorithm. Finally the RUL prediction performance based on WB_GPR is validated by utilizing the sensor data sets. Furthermore, the RUL prediction comparison with GPR and bagging GPR is also conducted. Experimental results demonstrate that the WB_GPR is superior in the RUL prediction with lower error rate and standard deviation.