TY - GEN
T1 - Dynamic Fault Diagnosis of Aeroengine Control System Sensors Based on LSTM-CNN
AU - Li, Huihui
AU - Gou, Linfeng
AU - Li, Huacong
AU - Zhang, Meng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - To address the problem that it is difficult to diagnose sensor faults when the operating state of the aeroengine control system changes dynamically, an LSTM-CNN based aeroengine sensor fault diagnosis method is established in this paper. First, a nonlinear dynamic prediction model of the engine is constructed by using Long short-Term memory (LSTM) network. The prediction model generates residual signals with each sensor measurement value to achieve decoupling between each sensor and between system state change and fault. Based on the constructed fault residual signal dataset, the classification of sensor faults is implemented based on Convolutional Neural Network (CNN). The simulation results show that the LSTM prediction network has high prediction accuracy, and the designed CNN classification network has high diagnosis accuracy with 91.33%.
AB - To address the problem that it is difficult to diagnose sensor faults when the operating state of the aeroengine control system changes dynamically, an LSTM-CNN based aeroengine sensor fault diagnosis method is established in this paper. First, a nonlinear dynamic prediction model of the engine is constructed by using Long short-Term memory (LSTM) network. The prediction model generates residual signals with each sensor measurement value to achieve decoupling between each sensor and between system state change and fault. Based on the constructed fault residual signal dataset, the classification of sensor faults is implemented based on Convolutional Neural Network (CNN). The simulation results show that the LSTM prediction network has high prediction accuracy, and the designed CNN classification network has high diagnosis accuracy with 91.33%.
KW - aeroengine control system
KW - convolutional neural network
KW - dynamic fault diagnosis
KW - long short-Term memory
KW - sensor
UR - https://www.scopus.com/pages/publications/85186745545
U2 - 10.1109/ICMAE59650.2023.10424519
DO - 10.1109/ICMAE59650.2023.10424519
M3 - 会议稿件
AN - SCOPUS:85186745545
T3 - 2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023
SP - 213
EP - 218
BT - 2023 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Mechanical and Aerospace Engineering, ICMAE 2023
Y2 - 18 July 2023 through 21 July 2023
ER -