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    Probabilistic Deep Learning With Bayesian Networks for Predicting Complex Engineering Systems' Remaining Useful Life: A Case Study of Unmanned Surface Vessel 

    Source: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2025:;volume( 011 ):;issue: 004:;page 41203-1
    Author(s): Weiner, Matthew J.; Yang, Ruochen; Groth, Katrina; Azarm, Shapour
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Remaining useful life (RUL) serves as a key indicator of system health, and its accurate and timely prediction supports informed decision-making for efficient operation and maintenance. This is essential for complex ...
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    Prognostics and Health Management of Unmanned Surface Vessels: Past, Present, and Future 

    Source: Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 008:;page 80801-1
    Author(s): Hazra, Indranil; Weiner, Matthew J.; Yang, Ruochen; Chatterjee, Arko; Southgate, Joseph; Groth, Katrina M.; Azarm, Shapour
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: With the increasing popularity and deployment of unmanned surface vessels (USVs) all over the world, prognostics and health management (PHM) has become an indispensable tool for health monitoring, fault diagnosis, health ...
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