The Aviation 4.0 concept is an industry-wide approach that leverages the latest technologies to increase aircraft safety and operational efficiency. Predictive maintenance is a key aspect of Aviation 4.0, which uses cloud computing, big data, and machine learning to anticipate problems before they occur, thereby increasing safety and decreasing downtime for aircraft. The application of predictive maintenance can enhance efficiency and reduce costs by enabling maintenance to be scheduled before the failure occurs. The development of “rules of flight” is being considered for Aviation 4.0 cyber-physical aircraft systems, which may automatically follow warnings if the crew fails to take appropriate action. This paper explores the implementation of Cloud-based Predictive Maintenance as a Service (CPMaaS) within the context of Aviation 4.0, utilizing C-MAPSS dataset. Leveraging advanced technologies such as LSTM models and SHAP analysis, the framework aims to enhance aircraft safety and operational efficiency by enabling proactive maintenance actions based on predictive insights derived from real-time sensor data. The study demonstrates the effectiveness of the proposed approach in reducing downtime and extending the lifespan of aircraft systems, paving the way for safer and more reliable aviation operations in the era of Industry 4.0.


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    Titel :

    Cloud based Predictive Maintenance Technique for Aviation System


    Beteiligte:


    Erscheinungsdatum :

    14.03.2024


    Format / Umfang :

    1440118 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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