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    Online Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization

    Source: Journal of Manufacturing Science and Engineering:;2019:;volume( 141 ):;issue: 005::page 51011
    Author:
    Xia, Tangbin
    ,
    Fang, Xiaolei
    ,
    Gebraeel, Nagi
    ,
    Xi, Lifeng
    ,
    Pan, Ershun
    DOI: 10.1115/1.4043255
    Publisher: American Society of Mechanical Engineers (ASME)
    Abstract: In mass customization, a manufacturing line is required to be kept in reliable operation to handle product demand volatility and potential machine degradations. Recent advances in data acquisition and processing allow for effective maintenance scheduling. This paper presents a systematic framework that integrates a sensor-driven prognostic method and an opportunistic maintenance policy. The prognostic method uses degradation signals of each individual machine to predict and update its time-to-failure (TTF) distributions in real time. Then, system-level opportunistic maintenance optimizations are dynamically made according to real-time TTF distributions and variable product orders. The online analytics framework is demonstrated through the case study based on the collected reliability information from a production line of engine crankshaft. The results can effectively prove that the real-time degradation updating and the opportunistic maintenance scheduling can efficiently reduce maintenance cost, avoid system breakdown, and ensure product quality. Furthermore, this framework can be applied not only in an automobile line but also for a broader range of manufacturing lines in mass customization.
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      Online Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4259032
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    contributor authorXia, Tangbin
    contributor authorFang, Xiaolei
    contributor authorGebraeel, Nagi
    contributor authorXi, Lifeng
    contributor authorPan, Ershun
    date accessioned2019-09-18T09:06:57Z
    date available2019-09-18T09:06:57Z
    date copyright4/2/2019 12:00:00 AM
    date issued2019
    identifier issn1087-1357
    identifier othermanu_141_5_051011
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4259032
    description abstractIn mass customization, a manufacturing line is required to be kept in reliable operation to handle product demand volatility and potential machine degradations. Recent advances in data acquisition and processing allow for effective maintenance scheduling. This paper presents a systematic framework that integrates a sensor-driven prognostic method and an opportunistic maintenance policy. The prognostic method uses degradation signals of each individual machine to predict and update its time-to-failure (TTF) distributions in real time. Then, system-level opportunistic maintenance optimizations are dynamically made according to real-time TTF distributions and variable product orders. The online analytics framework is demonstrated through the case study based on the collected reliability information from a production line of engine crankshaft. The results can effectively prove that the real-time degradation updating and the opportunistic maintenance scheduling can efficiently reduce maintenance cost, avoid system breakdown, and ensure product quality. Furthermore, this framework can be applied not only in an automobile line but also for a broader range of manufacturing lines in mass customization.
    publisherAmerican Society of Mechanical Engineers (ASME)
    titleOnline Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization
    typeJournal Paper
    journal volume141
    journal issue5
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4043255
    journal fristpage51011
    journal lastpage051011-12
    treeJournal of Manufacturing Science and Engineering:;2019:;volume( 141 ):;issue: 005
    contenttypeFulltext
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