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Human-Machine Function Allocation Method for Submersible Fault Detection Tasks

Author

Listed:
  • Chenyuan Yang

    (School of Aeronautic Science and Engineering, Beihang University, No. 9, South Third Street, Higher Education Park, Beijing 102206, China
    These authors contributed equally to this work.)

  • Liping Pang

    (School of Aeronautic Science and Engineering, Beihang University, No. 9, South Third Street, Higher Education Park, Beijing 102206, China
    These authors contributed equally to this work.)

  • Wentao Wu

    (Department of Civil and Natural Resources Engineering, University of Canterbury, Christchurch 8041, New Zealand)

  • Xiaodong Cao

    (School of Aeronautic Science and Engineering, Beihang University, No. 9, South Third Street, Higher Education Park, Beijing 102206, China
    Tianmushan Laboratory, Hangzhou 311115, China)

Abstract

The operation and support (OS) officer is responsible for buoyancy regulation and fault detection of onboard equipment in the civil submersible. The OS officer carries out the above tasks through the human-machine interface (HMI) of a submersible buoyancy regulation and support (SBRS) system. However, the OS officer often faces uneven task frequency produced by fault tasks, which leads to an unbalanced mental workload and individual failures. To address this issue, we proposed a human-machine function allocation method based on level of automation (LOA) taxonomy and submersible task complexity (STC), aimed at improving human-machine cooperation in submersible fault detection tasks. Based on this method, we identified the LOA2 as the optimal human-computer function allocation scheme. In this study, three measurement techniques (subjective scale, work performance, and physiological status) were used to test 15 subjects to validate the effectiveness of the proposed optimal human-machine function allocation scheme. The GAMM test results also indicate that the proposed optimal human-machine function allocation scheme (LOA2) can improve the work performance of the operating system officials under low or high workloads and reduce the subjective workload.

Suggested Citation

  • Chenyuan Yang & Liping Pang & Wentao Wu & Xiaodong Cao, 2024. "Human-Machine Function Allocation Method for Submersible Fault Detection Tasks," Mathematics, MDPI, vol. 12(22), pages 1-18, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3615-:d:1524538
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    References listed on IDEAS

    as
    1. Xiaodong Cao & Piers MacNaughton & Leslie R. Cadet & Jose Guillermo Cedeno-Laurent & Skye Flanigan & Jose Vallarino & Deborah Donnelly-McLay & David C. Christiani & John D. Spengler & Joseph G. Allen, 2019. "Heart Rate Variability and Performance of Commercial Airline Pilots during Flight Simulations," IJERPH, MDPI, vol. 16(2), pages 1-16, January.
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