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    Gearbox Fault Diagnosis Based on Selective Integrated Soft Competitive Learning Fuzzy Adaptive Resonance Theory

    Source: Journal of Computing and Information Science in Engineering:;2019:;volume( 019 ):;issue: 001::page 11008
    Author:
    Wan, Xiao-Jin
    ,
    Liu, Licheng
    ,
    Xu, Zengbing
    ,
    Xu, Zhigang
    DOI: 10.1115/1.4041776
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In this work, a soft competitive learning fuzzy adaptive resonance theory (SFART) diagnosis model based on multifeature domain selection for the single symptom domain and the single-target model is proposed. In order to solve the problem that the performance of traditional fuzzy ART (FART) is affected by the order of sample input, the similarity criterion of YU norm is introduced into the fuzzy ART network. In the meanwhile, the lateral inhibition theory is introduced to solve the wasteful problem of fuzzy ART mode node. By combining YU norm and lateral inhibition theory with fuzzy ART network, a soft competitive learning ART neural network diagnosis model that allows multiple mode nodes to learn simultaneously is designed. The feature parameters are extracted from the perspectives of time domain, frequency domain, time series model, wavelet analysis, and wavelet packet energy spectrum analysis, respectively. To further improve the diagnostic accuracy, the selective weighted majority voting method is integrated into the diagnosis model. Finally, the selected feature parameters are inputted to the integrated model to complete the fault classification and diagnosis. Finally, the proposed method is verified with a gearbox fault diagnosis test.
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      Gearbox Fault Diagnosis Based on Selective Integrated Soft Competitive Learning Fuzzy Adaptive Resonance Theory

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4256404
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    • Journal of Computing and Information Science in Engineering

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    contributor authorWan, Xiao-Jin
    contributor authorLiu, Licheng
    contributor authorXu, Zengbing
    contributor authorXu, Zhigang
    date accessioned2019-03-17T10:55:37Z
    date available2019-03-17T10:55:37Z
    date copyright11/19/2018 12:00:00 AM
    date issued2019
    identifier issn1530-9827
    identifier otherjcise_019_01_011008.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4256404
    description abstractIn this work, a soft competitive learning fuzzy adaptive resonance theory (SFART) diagnosis model based on multifeature domain selection for the single symptom domain and the single-target model is proposed. In order to solve the problem that the performance of traditional fuzzy ART (FART) is affected by the order of sample input, the similarity criterion of YU norm is introduced into the fuzzy ART network. In the meanwhile, the lateral inhibition theory is introduced to solve the wasteful problem of fuzzy ART mode node. By combining YU norm and lateral inhibition theory with fuzzy ART network, a soft competitive learning ART neural network diagnosis model that allows multiple mode nodes to learn simultaneously is designed. The feature parameters are extracted from the perspectives of time domain, frequency domain, time series model, wavelet analysis, and wavelet packet energy spectrum analysis, respectively. To further improve the diagnostic accuracy, the selective weighted majority voting method is integrated into the diagnosis model. Finally, the selected feature parameters are inputted to the integrated model to complete the fault classification and diagnosis. Finally, the proposed method is verified with a gearbox fault diagnosis test.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleGearbox Fault Diagnosis Based on Selective Integrated Soft Competitive Learning Fuzzy Adaptive Resonance Theory
    typeJournal Paper
    journal volume19
    journal issue1
    journal titleJournal of Computing and Information Science in Engineering
    identifier doi10.1115/1.4041776
    journal fristpage11008
    journal lastpage011008-13
    treeJournal of Computing and Information Science in Engineering:;2019:;volume( 019 ):;issue: 001
    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