Research for autonomous seagoing vessels with a focus on safe navigation and cybersecurity has enabled the reduction of required onboard personnel. Navigation and safety responsibilities historically completed by onboard watch personnel can now be achieved by a land-based crew or by using an autonomous system. However, limited research has been directed toward reducing the personnel required while underway to ensure the reliable operation of shipboard machinery systems. This article explores the use of a hardware-based multiphysics ship machinery plant (MSMP) within a laboratory setting for the simulation of real-world dynamic shipboard systems. The hardware simulation data align with system responses exhibited by real-world ship machinery systems. This validation and exploration of fault mitigation techniques enable the use of the laboratory system to explore data-driven technologies from run-to-failure (RTF) data profiles constructed with the laboratory system, such as artificial intelligence (AI) and nonintrusive load monitoring (NILM) for machinery system prognostics and alternative control of machinery systems for fault mitigation.


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    Titel :

    Laboratory-Scale Multiphysics Ship Machinery Plant for Autonomous Machinery Research


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    7368034 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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