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Summary: A novel data synthesis method called deep feature enhanced generative adversarial network is proposed in this paper to improve the performance of imbalanced fault diagnosis. By integrating a pull-away function, a self-attention module, and an automatic data filter, the quality of synthesized data is improved, the stability of generative adversarial networks is enhanced, and the accuracy and diversity of synthesized samples are timely ensured.
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Xuan Liu et al.
Summary: In this paper, a transfer residual network augmented with explicit weight self-assignment strategy based on meta data (TRN-EWM) is proposed for cross-domain fault diagnosis. By extracting depth features and conducting class imbalanced cross-domain transfer, this method effectively addresses the diagnosis difficulties in actual working conditions and achieves high diagnosis accuracy.
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Chao Zhao et al.
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Summary: Data-driven fault diagnosis is crucial in modern industries, and our proposed CCDG approach effectively improves the model's generalization capability under different working environments. It achieves remarkable performance in fault diagnosis of rolling machinery, outperforming state-of-the-art methods on real-world datasets.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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Summary: In this article, a class-imbalance adversarial transfer learning (CIATL) method is proposed for real-industrial cross-domain diagnosis tasks. By embedding class-imbalance learning and double-level adversarial transfer learning, this method can learn domain-invariant knowledge, and extensive experiments have verified its effectiveness and generalization.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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Haeyong Kang et al.
Summary: The Maximum Margin (MM) learning algorithm is proposed to address the issue of class-imbalance data learning. By minimizing a margin-based generalization bound through shifting decision bound, the MM loss function is designed for better generalization on minority classes. The study evaluates the effectiveness of two types of hard maximum margin-based decision boundary shift on artificially imbalanced CIFAR-10/100 datasets.
2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
(2021)
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Computer Science, Information Systems
Hanen Karamti et al.
Summary: This paper proposes a new deep architecture based on Stacked Variant Autoencoders for multi-fault machinery identification with imbalanced samples. Experimental results show the efficiency of the proposed model which achieved an accuracy of 93.2%. In addition, for extensive comparative analysis issue, the proposed method reported better results in terms of training and testing time and overall accuracy compared to Generative Adversarial Network (GAN) and triNetwork Generative Adversarial Network (tnGAN).
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