In today's highly integrated and automated aviation field, the stable operation of avionics systems is the foundation for maintaining flight safety and efficient operations. Because of the shortcomings in research on fault classification of avionics modules, this study proposes an Inception-CBAM network model, aiming to improve the accuracy and efficiency of fault diagnosis. This model combines the powerful multi-scale feature extraction capability of the Inception network with the attention mechanism of the CBAM module, which greatly enhances the ability to identify complex fault patterns. Experimental verification shows that the model can effectively classify various faults, enhance the scientificity and systematicness of diagnosis, and contribute important technical support to the formulation of aviation safety maintenance and fault prevention strategies.


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    Titel :

    Multi-channel Avionics Module Fault Classification Based on Inception-CBAM Network


    Beteiligte:
    Yu, Lubin (Autor:in) / Peng, YiXi (Autor:in) / Han, Xingjian (Autor:in) / Liang, Shuqi (Autor:in) / Yang, Peiliang (Autor:in) / Liu, Junbin (Autor:in) / He, Shilie (Autor:in)


    Erscheinungsdatum :

    31.05.2024


    Format / Umfang :

    831475 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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