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%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dynamic Fault Diagnosis of Aeroengine Control System Sensors Based on LSTM-CNN


    Contributors:
    Li, Huihui (author) / Gou, Linfeng (author) / Li, Huacong (author) / Zhang, Meng (author)


    Publication date :

    2023-07-18


    Size :

    546538 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy

    Huihui Li / Linfeng Gou / Hua Zheng et al. | DOAJ | 2021

    Free access


    Aeroengine Gas Path Parameter Trend Prediction Based on LSTM

    Zhou, Qifeng / Chen, Xiaonan / Qing, Xinlin et al. | SAE Technical Papers | 2023



    Method of aeroengine fault diagnosis based on a Fingerprint map

    Li, Yao-hua / Gou, Ying-qi / Pan, Cheng et al. | SAGE Publications | 2023