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    Neural Network and Fuzzy Logic Diagnostics of 1x Faults in Rotating Machinery

    Source: Journal of Engineering for Gas Turbines and Power:;2007:;volume( 129 ):;issue: 003::page 703
    Author:
    A. El-Shafei
    ,
    N. Rieger
    ,
    T. A. F. Hassan
    ,
    A. K. Soliman
    ,
    Y. Zeyada
    DOI: 10.1115/1.2227417
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In this paper, the application of neural networks and fuzzy logic to the diagnosis of faults in rotating machinery is investigated. The learning-vector-quantization (LVQ) neural network is applied in series and in parallel to a fuzzy inference engine, to diagnose 1x faults. The faults investigated are unbalance, misalignment, and structural looseness. The method is applied to a test rig (, 2003, ASME Paper No. GT 2003-38450), and the effectiveness of the integrated Neural Network and Fuzzy Logic method is illustrated.
    keyword(s): Fuzzy logic , Artificial neural networks , Machinery AND Networks ,
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      Neural Network and Fuzzy Logic Diagnostics of 1x Faults in Rotating Machinery

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/135696
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    • Journal of Engineering for Gas Turbines and Power

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    contributor authorA. El-Shafei
    contributor authorN. Rieger
    contributor authorT. A. F. Hassan
    contributor authorA. K. Soliman
    contributor authorY. Zeyada
    date accessioned2017-05-09T00:23:38Z
    date available2017-05-09T00:23:38Z
    date copyrightJuly, 2007
    date issued2007
    identifier issn1528-8919
    identifier otherJETPEZ-26960#703_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/135696
    description abstractIn this paper, the application of neural networks and fuzzy logic to the diagnosis of faults in rotating machinery is investigated. The learning-vector-quantization (LVQ) neural network is applied in series and in parallel to a fuzzy inference engine, to diagnose 1x faults. The faults investigated are unbalance, misalignment, and structural looseness. The method is applied to a test rig (, 2003, ASME Paper No. GT 2003-38450), and the effectiveness of the integrated Neural Network and Fuzzy Logic method is illustrated.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleNeural Network and Fuzzy Logic Diagnostics of 1x Faults in Rotating Machinery
    typeJournal Paper
    journal volume129
    journal issue3
    journal titleJournal of Engineering for Gas Turbines and Power
    identifier doi10.1115/1.2227417
    journal fristpage703
    journal lastpage710
    identifier eissn0742-4795
    keywordsFuzzy logic
    keywordsArtificial neural networks
    keywordsMachinery AND Networks
    treeJournal of Engineering for Gas Turbines and Power:;2007:;volume( 129 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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