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    Research on Ferrographic Image Fault Diagnosis Based on Channel Overlapping Technique and Information Fusion Mechanism

    Source: Journal of Tribology:;2024:;volume( 146 ):;issue: 007::page 74601-1
    Author:
    Fei, Xie
    ,
    Haijun, Wei
    DOI: 10.1115/1.4064858
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Utilizing computer technology to realize the application of ferrographic intelligent fault diagnosis technology is a foundational investigation to oversee the operations of mechanical equipment. To continuously improve the accuracy of artificial intelligence recognition, the complexity and computation of the model will be increased. The proposal of the transformer model (the core technology of chatgpt) has fundamentally changed the intelligence level of artificial intelligence, but it has also greatly increased the demand for computer computing power. What's more, it is difficult to equip industrial quality inspection sites with high computing power computers. The channel overlapping technique developed in this paper is a technology to segment the three channels of image information and reserve overlapping areas for an information communication mechanism. With this mechanism, the model location channel overlapping convolutional neural network can obtain high recognition accuracy by using only one-half of the original training computing power. When channel overlapping combines with no position information, information fusion is formed. The model channel overlapping technique fusion convolutional neural network established by the information fusion mechanism will get a higher prediction accuracy through joint training with the original image. However, the computation consumption is nearly one-third of the pure traditional convolutional neural network algorithm.
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      Research on Ferrographic Image Fault Diagnosis Based on Channel Overlapping Technique and Information Fusion Mechanism

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4295889
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    contributor authorFei, Xie
    contributor authorHaijun, Wei
    date accessioned2024-04-24T22:47:44Z
    date available2024-04-24T22:47:44Z
    date copyright3/25/2024 12:00:00 AM
    date issued2024
    identifier issn0742-4787
    identifier othertrib_146_7_074601.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4295889
    description abstractUtilizing computer technology to realize the application of ferrographic intelligent fault diagnosis technology is a foundational investigation to oversee the operations of mechanical equipment. To continuously improve the accuracy of artificial intelligence recognition, the complexity and computation of the model will be increased. The proposal of the transformer model (the core technology of chatgpt) has fundamentally changed the intelligence level of artificial intelligence, but it has also greatly increased the demand for computer computing power. What's more, it is difficult to equip industrial quality inspection sites with high computing power computers. The channel overlapping technique developed in this paper is a technology to segment the three channels of image information and reserve overlapping areas for an information communication mechanism. With this mechanism, the model location channel overlapping convolutional neural network can obtain high recognition accuracy by using only one-half of the original training computing power. When channel overlapping combines with no position information, information fusion is formed. The model channel overlapping technique fusion convolutional neural network established by the information fusion mechanism will get a higher prediction accuracy through joint training with the original image. However, the computation consumption is nearly one-third of the pure traditional convolutional neural network algorithm.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleResearch on Ferrographic Image Fault Diagnosis Based on Channel Overlapping Technique and Information Fusion Mechanism
    typeJournal Paper
    journal volume146
    journal issue7
    journal titleJournal of Tribology
    identifier doi10.1115/1.4064858
    journal fristpage74601-1
    journal lastpage74601-12
    page12
    treeJournal of Tribology:;2024:;volume( 146 ):;issue: 007
    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
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