Once an aircraft fault occurs, it is likely to be a multiple, intricate fault. How to accurately and efficiently determine the cause of the fault is a major problem in the maintenance industry. Deep learning is a branch of artificial intelligence. It can mine the essential features of faults through feature extraction ability, and characterize the complex mapping relationship between fault phenomena and fault causes. This paper introduces several common models of deep learning, including DBN, CNN and RNN. Through the analysis of the characteristics of the model, their applications in aircraft maintenance and fault diagnosis are studied respectively, and the application prospects are analyzed. In addition, some characteristics of aircraft faults are analyzed, and the challenges of deep learning in the field of fault diagnosis are proposed


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    Titel :

    Application Research of Deep Leaming in the Field of Aircraft Fault Diagnosis


    Beteiligte:
    Liu, Yuelei (Autor:in)


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    185477 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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