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%.
Dynamic Fault Diagnosis of Aeroengine Control System Sensors Based on LSTM-CNN
18.07.2023
546538 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
LPV Robust Filter Based Fault Diagnosis Method for Aeroengine Control System
Springer Verlag | 2024
|Aeroengine fault diagnosis based on multi-classification AdaBoost
British Library Online Contents | 2018
|Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy
DOAJ | 2021
|Aeroengine Gas Path Parameter Trend Prediction Based on LSTM
SAE Technical Papers | 2023
|