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    An Intelligent Prognostic System for Gear Performance Degradation Assessment and Remaining Useful Life Estimation

    Source: Journal of Vibration and Acoustics:;2015:;volume( 137 ):;issue: 002::page 21004
    Author:
    Wang, Dong
    ,
    Miao, Qiang
    ,
    Zhou, Qinghua
    ,
    Zhou, Guangwu
    DOI: 10.1115/1.4028833
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Gears are widely used in machines to transmit torque from one shaft to another shaft and to change the speed of a power source. Gear failure is one of the major causes for mechanical transmission system breakdown. Therefore, early gear faults must be immediately detected prior to its failure. Once early gear faults are diagnosed, gear remaining useful life (RUL) should be estimated to prevent any unexpected gear failure. In this paper, an intelligent prognostic system is developed for gear performance degradation assessment and RUL estimation. For gear performance degradation assessment, which aims to monitor current gear health condition, first, the frequency spectrum of gear acceleration error signal is mathematically analyzed to design a highorder complex Comblet for extracting gear fault related signatures. Then, two health indicators called heath indicator 1 and health indicator 2 are constructed to detect early gear faults and assess gear performance degradation, respectively, using two individual dynamic Bayesian networks. For gear RUL estimation, which aims to predict future gear health condition, a general sequential Monte Carlo algorithm is applied to iteratively infer gear failure probability density function (FPDF), which is used to predict gear residual lifetime. One case study is investigated to illustrate how the developed prognostic system works. The vibration data collected from a gearbox accelerated life test are used in this paper, where the gearbox started from a brandnew state, and ran until gear tooth failure. The results show that the developed prognostic system is able to detect early gear faults, track gear performance degradation, and predict gear RUL.
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      An Intelligent Prognostic System for Gear Performance Degradation Assessment and Remaining Useful Life Estimation

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    http://yetl.yabesh.ir/yetl1/handle/yetl/160012
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    contributor authorWang, Dong
    contributor authorMiao, Qiang
    contributor authorZhou, Qinghua
    contributor authorZhou, Guangwu
    date accessioned2017-05-09T01:24:55Z
    date available2017-05-09T01:24:55Z
    date issued2015
    identifier issn1048-9002
    identifier othervib_137_02_021004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/160012
    description abstractGears are widely used in machines to transmit torque from one shaft to another shaft and to change the speed of a power source. Gear failure is one of the major causes for mechanical transmission system breakdown. Therefore, early gear faults must be immediately detected prior to its failure. Once early gear faults are diagnosed, gear remaining useful life (RUL) should be estimated to prevent any unexpected gear failure. In this paper, an intelligent prognostic system is developed for gear performance degradation assessment and RUL estimation. For gear performance degradation assessment, which aims to monitor current gear health condition, first, the frequency spectrum of gear acceleration error signal is mathematically analyzed to design a highorder complex Comblet for extracting gear fault related signatures. Then, two health indicators called heath indicator 1 and health indicator 2 are constructed to detect early gear faults and assess gear performance degradation, respectively, using two individual dynamic Bayesian networks. For gear RUL estimation, which aims to predict future gear health condition, a general sequential Monte Carlo algorithm is applied to iteratively infer gear failure probability density function (FPDF), which is used to predict gear residual lifetime. One case study is investigated to illustrate how the developed prognostic system works. The vibration data collected from a gearbox accelerated life test are used in this paper, where the gearbox started from a brandnew state, and ran until gear tooth failure. The results show that the developed prognostic system is able to detect early gear faults, track gear performance degradation, and predict gear RUL.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleAn Intelligent Prognostic System for Gear Performance Degradation Assessment and Remaining Useful Life Estimation
    typeJournal Paper
    journal volume137
    journal issue2
    journal titleJournal of Vibration and Acoustics
    identifier doi10.1115/1.4028833
    journal fristpage21004
    journal lastpage21004
    identifier eissn1528-8927
    treeJournal of Vibration and Acoustics:;2015:;volume( 137 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian