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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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    A Reliability-Based Optimization Framework for Planning Operational Profiles for Unmanned Systems 

    Source: Journal of Mechanical Design:;2023:;volume( 146 ):;issue: 005:;page 51704-1
    Author(s): Hazra, Indranil; Chatterjee, Arko; Southgate, Joseph; Weiner, Matthew J.; Groth, Katrina M.; Azarm, Shapour
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Unmanned engineering systems that execute various operations are becoming increasingly complex relying on a large number of components and their interactions. The reliability, maintainability, and performance optimization ...
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