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%.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Li, Huihui (Autor:in) / Gou, Linfeng (Autor:in) / Li, Huacong (Autor:in) / Zhang, Meng (Autor:in)


    Erscheinungsdatum :

    2023-07-18


    Format / Umfang :

    546538 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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

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

    Freier Zugriff


    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