TY - GEN
T1 - Weighted bagging gaussion process regression to predict remaining useful life of electro-mechanical actuator
AU - Zhang, Yujie
AU - Peng, Xiyuan
AU - Peng, Yu
AU - Pang, Jingyue
AU - Liu, Datong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/16
Y1 - 2017/1/16
N2 - Electro-Mechanical Actuator (EMA) is one of the key components of next generation aircraft. In order to ensure the safety of aircraft, it is critical to predict the remaining useful life (RUL) of EMA. And the RUL prediction can be implemented by utilizing Gaussian Process Regression (GPR). However, the GPR algorithm is extremely complex. Hence, a weighted bagging Gaussian Process Regression (WB-GPR) algorithm is presented in this article. To be specific, the significance of RUL prediction of EMA is analyzed, and the variable which can represent the degradation progress of EMA failure is selected. Then the framework to predict the RUL of EMA is realized, with the proposed WB-GPR. Finally the performance of RUL prediction based on WB-GPR is validated by utilizing the sensor data sets from National Aeronautics and Space Administration (NASA) Ames Research Center. Furthermore, the comparison of RUL prediction with GPR and bagging GPR has been achieved. Experimental results demonstrate that the WB-GPR is effective in the RUL prediction with low error rate and standard deviation.
AB - Electro-Mechanical Actuator (EMA) is one of the key components of next generation aircraft. In order to ensure the safety of aircraft, it is critical to predict the remaining useful life (RUL) of EMA. And the RUL prediction can be implemented by utilizing Gaussian Process Regression (GPR). However, the GPR algorithm is extremely complex. Hence, a weighted bagging Gaussian Process Regression (WB-GPR) algorithm is presented in this article. To be specific, the significance of RUL prediction of EMA is analyzed, and the variable which can represent the degradation progress of EMA failure is selected. Then the framework to predict the RUL of EMA is realized, with the proposed WB-GPR. Finally the performance of RUL prediction based on WB-GPR is validated by utilizing the sensor data sets from National Aeronautics and Space Administration (NASA) Ames Research Center. Furthermore, the comparison of RUL prediction with GPR and bagging GPR has been achieved. Experimental results demonstrate that the WB-GPR is effective in the RUL prediction with low error rate and standard deviation.
KW - Aircraft
KW - Degradation
KW - Electro-Mechanical Actuator
KW - Gaussian process regression
KW - Remaining Useful Life
KW - Weighted bagging GPR
UR - https://www.scopus.com/pages/publications/85015675362
U2 - 10.1109/PHM.2016.7819795
DO - 10.1109/PHM.2016.7819795
M3 - 会议稿件
AN - SCOPUS:85015675362
T3 - Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016
BT - Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016
A2 - Miao, Qiang
A2 - Li, Zhaojun
A2 - Zuo, Ming J.
A2 - Xing, Liudong
A2 - Tian, Zhigang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016
Y2 - 19 October 2016 through 21 October 2016
ER -