TY - GEN
T1 - Aeroengine remaining useful life prediction using an integrated deep feature fusion model
AU - Li, Xingqiu
AU - Jiang, Hongkai
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/16
Y1 - 2021/7/16
N2 - Aeroengine plays a significant role in advanced aircrafts. Predictive maintenance can enhance the safety and security, as well as save amounts of costs. Remaining useful life (RUL) prediction can help make a scientific maintenance schedule. Therefore, an integrated deep feature fusion model is proposed for aeroengine RUL prediction. Firstly, a nonnegative sparse autoencoder (NSAE) is applied for unsupervised deep feature fusion. Secondly, gated recurrent unit (GRU) is stacked upon the NSAE for temporal feature fusion to model the aeroengine degradation process by its powerful long term dependency learning ability. Finally, an integrated deep feature fusion model with NSAE and GRU is globally finetuned for RUL prediction. A simulated turbofan engine dataset is used to verify the effectiveness, and the results suggest that the proposed method is able to accurately predict the RUL of each test unit.
AB - Aeroengine plays a significant role in advanced aircrafts. Predictive maintenance can enhance the safety and security, as well as save amounts of costs. Remaining useful life (RUL) prediction can help make a scientific maintenance schedule. Therefore, an integrated deep feature fusion model is proposed for aeroengine RUL prediction. Firstly, a nonnegative sparse autoencoder (NSAE) is applied for unsupervised deep feature fusion. Secondly, gated recurrent unit (GRU) is stacked upon the NSAE for temporal feature fusion to model the aeroengine degradation process by its powerful long term dependency learning ability. Finally, an integrated deep feature fusion model with NSAE and GRU is globally finetuned for RUL prediction. A simulated turbofan engine dataset is used to verify the effectiveness, and the results suggest that the proposed method is able to accurately predict the RUL of each test unit.
KW - Aeroengine
KW - Deep feature fusion
KW - Gated recurrent unit
KW - Integrated
KW - Remaining useful life prediction
UR - https://www.scopus.com/pages/publications/85115356889
U2 - 10.1109/ICMAE52228.2021.9522561
DO - 10.1109/ICMAE52228.2021.9522561
M3 - 会议稿件
AN - SCOPUS:85115356889
T3 - 2021 12th International Conference on Mechanical and Aerospace Engineering, ICMAE 2021
SP - 215
EP - 219
BT - 2021 12th International Conference on Mechanical and Aerospace Engineering, ICMAE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Mechanical and Aerospace Engineering, ICMAE 2021
Y2 - 16 July 2021 through 19 July 2021
ER -