In the process of aircraft design, various experiments are required to validate design parameters under real flight conditions. Such experiments are costly, and currently there are limited solutions for effectively dealing with scenarios such as occasional faults during these experiments. This paper proposes an on-board remote high-definition collaborative troubleshooting and flight decision-making assistance device, aiming to achieve efficient test scenario replication and decision support through remote collaboration between ground personnel and flight crew. The device utilizes several image capture devices in the cockpit and employs algorithms such as action recognition and image recognition. It utilizes highspeed satellite communication technology to structure flight data, including pilot operations, faults, and aircraft status, into a timeline script. Ground personnel can use this output script to provide corresponding solutions. Flight crew members receive real-time guidance and decision support through smart glasses. It is evaluated that this approach may save a significant amount of travel expenses and time in ground tests, flight tests, field fault diagnosis, and crew training scenarios. The innovation of the proposed device lies in the application of cloud servers for timeline script generation and the use of remote retrieval by monitoring personnel for key operation fault diagnosis. Furthermore, it incorporates intelligent recognition technology for flight guidance, enabling efficient troubleshooting and reducing the number of flight tests. Therefore, research and development costs could be reduced, and engineering change progress could be accelerated.


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    Title :

    Onboard remote high-definition collaborative troubleshooting and flight decision-making assistance device


    Contributors:
    Jabbar, M. A. (editor) / Lorenz, Pascal (editor) / Shao, Cheng (author) / Song, Jinze (author) / Guo, Lei (author)

    Conference:

    Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024) ; 2024 ; Kuala Lumpur, Malaysia


    Published in:

    Proc. SPIE ; 13184


    Publication date :

    2024-07-05





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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