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    Adaptive Weighted Cost-Sensitive Learning-Driven Improved Dense Convolutional Neural Network for Imbalanced Fault Diagnosis under Limited Fault Samples

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2025:;Volume ( 011 ):;issue: 002::page 04025013-1
    Author:
    Zihao Lei
    ,
    Shuaiqing Deng
    ,
    Yu Su
    ,
    Zhaojun Steven Li
    ,
    Ke Feng
    ,
    Guangrui Wen
    ,
    Zhixiong Li
    ,
    Xuefeng Chen
    DOI: 10.1061/AJRUA6.RUENG-1480
    Publisher: American Society of Civil Engineers
    Abstract: Offshore wind turbines play a crucial part in the transformation of wind energy into electricity, which significantly benefits the sustainable development of the economy and society. Nevertheless, offshore wind turbines in practice are often in extremely severe operating environments, giving them tremendous challenges for their safe operation. In particular, the scarcity of fault data in the actual operating scenarios makes it difficult to collect enough fault data for training, resulting in a long-tailed distribution of training data, which leads to the majority class dominance and minority class overfitting problems. For the above-mentioned problems, an adaptive weighted cost-sensitive learning-driven improved dense convolutional neural network is proposed. First, a large convolutional kernel and interactively replicated dense connections are utilized to extract more stable discriminative features with fewer parameters. Second, an activation function with self-normalization property enhances the stability of model training under imbalanced data conditions. Further, adaptive weighting of misclassification cost is achieved by integrating sample size distribution, sample importance information, and imbalanced classification assessment metrics. Finally, two cases and ablation experiments under the wind turbine simulator testbed are implemented to validate the effectiveness and superiority of the proposed method.
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      Adaptive Weighted Cost-Sensitive Learning-Driven Improved Dense Convolutional Neural Network for Imbalanced Fault Diagnosis under Limited Fault Samples

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4307835
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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

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    contributor authorZihao Lei
    contributor authorShuaiqing Deng
    contributor authorYu Su
    contributor authorZhaojun Steven Li
    contributor authorKe Feng
    contributor authorGuangrui Wen
    contributor authorZhixiong Li
    contributor authorXuefeng Chen
    date accessioned2025-08-17T23:03:08Z
    date available2025-08-17T23:03:08Z
    date copyright6/1/2025 12:00:00 AM
    date issued2025
    identifier otherAJRUA6.RUENG-1480.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4307835
    description abstractOffshore wind turbines play a crucial part in the transformation of wind energy into electricity, which significantly benefits the sustainable development of the economy and society. Nevertheless, offshore wind turbines in practice are often in extremely severe operating environments, giving them tremendous challenges for their safe operation. In particular, the scarcity of fault data in the actual operating scenarios makes it difficult to collect enough fault data for training, resulting in a long-tailed distribution of training data, which leads to the majority class dominance and minority class overfitting problems. For the above-mentioned problems, an adaptive weighted cost-sensitive learning-driven improved dense convolutional neural network is proposed. First, a large convolutional kernel and interactively replicated dense connections are utilized to extract more stable discriminative features with fewer parameters. Second, an activation function with self-normalization property enhances the stability of model training under imbalanced data conditions. Further, adaptive weighting of misclassification cost is achieved by integrating sample size distribution, sample importance information, and imbalanced classification assessment metrics. Finally, two cases and ablation experiments under the wind turbine simulator testbed are implemented to validate the effectiveness and superiority of the proposed method.
    publisherAmerican Society of Civil Engineers
    titleAdaptive Weighted Cost-Sensitive Learning-Driven Improved Dense Convolutional Neural Network for Imbalanced Fault Diagnosis under Limited Fault Samples
    typeJournal Article
    journal volume11
    journal issue2
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
    identifier doi10.1061/AJRUA6.RUENG-1480
    journal fristpage04025013-1
    journal lastpage04025013-15
    page15
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2025:;Volume ( 011 ):;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
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