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    Fault Adaptive Mission Planning: Increasing Useful-Life and Reducing Downtime Through Condition-Based Decision-Making

    Source: Journal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 006::page 061013-1
    Author:
    DeStefano, Charlie
    ,
    Jensen, David
    DOI: 10.1115/1.4047354
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper presents a novel Fault Adaptive Mission Planning (FAMP) framework for complex systems aimed at increasing useful-life and reducing downtime through condition-based decision-making. A hallmark of complex systems is that they typically have access to multiple mission plans that allow their mission objectives to be accomplished in a variety of ways. In hopes of exploiting this characteristic, FAMP is the process of increasing a system's useful-lifespan by first determining how each potential mission plan affects the system's degradation differently, and then by implementing a planning strategy that utilizes this information to repeatedly recalculate a new mission plan as the system degrades. Fault-augmented physics models identify how component degradation will affect the system's current and future performance for a given mission plan. Then, at various degradation-based thresholds, new mission plans are installed such that whenever possible, the healthiest components are used more, or in different ways, than the more degraded components. This process promotes balanced degradation, preventing useful-life from being wasted and reducing downtime through synchronized maintenance schedules. This work expands the prognostics and health management paradigm by enabling life extension and maintenance reduction through real-time FAMP.
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      Fault Adaptive Mission Planning: Increasing Useful-Life and Reducing Downtime Through Condition-Based Decision-Making

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4274922
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    contributor authorDeStefano, Charlie
    contributor authorJensen, David
    date accessioned2022-02-04T22:07:29Z
    date available2022-02-04T22:07:29Z
    date copyright6/22/2020 12:00:00 AM
    date issued2020
    identifier issn1530-9827
    identifier otherjcise_20_6_061013.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4274922
    description abstractThis paper presents a novel Fault Adaptive Mission Planning (FAMP) framework for complex systems aimed at increasing useful-life and reducing downtime through condition-based decision-making. A hallmark of complex systems is that they typically have access to multiple mission plans that allow their mission objectives to be accomplished in a variety of ways. In hopes of exploiting this characteristic, FAMP is the process of increasing a system's useful-lifespan by first determining how each potential mission plan affects the system's degradation differently, and then by implementing a planning strategy that utilizes this information to repeatedly recalculate a new mission plan as the system degrades. Fault-augmented physics models identify how component degradation will affect the system's current and future performance for a given mission plan. Then, at various degradation-based thresholds, new mission plans are installed such that whenever possible, the healthiest components are used more, or in different ways, than the more degraded components. This process promotes balanced degradation, preventing useful-life from being wasted and reducing downtime through synchronized maintenance schedules. This work expands the prognostics and health management paradigm by enabling life extension and maintenance reduction through real-time FAMP.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleFault Adaptive Mission Planning: Increasing Useful-Life and Reducing Downtime Through Condition-Based Decision-Making
    typeJournal Paper
    journal volume20
    journal issue6
    journal titleJournal of Computing and Information Science in Engineering
    identifier doi10.1115/1.4047354
    journal fristpage061013-1
    journal lastpage061013-9
    page9
    treeJournal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 006
    contenttypeFulltext
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