Sample Augmentation Using Enhanced Auxiliary Classifier Generative Adversarial Network by Transformer for Railway Freight Train Wheelset Bearing Fault Diagnosis.

IF 2.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY Entropy Pub Date : 2024-12-20 DOI:10.3390/e26121113
Jing Zhao, Junfeng Li, Zonghao Yuan, Tianming Mu, Zengqiang Ma, Suyan Liu
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Abstract

Diagnosing faults in wheelset bearings is critical for train safety. The main challenge is that only a limited amount of fault sample data can be obtained during high-speed train operations. This scarcity of samples impacts the training and accuracy of deep learning models for wheelset bearing fault diagnosis. Studies show that the Auxiliary Classifier Generative Adversarial Network (ACGAN) demonstrates promising performance in addressing this issue. However, existing ACGAN models have drawbacks such as complexity, high computational expenses, mode collapse, and vanishing gradients. Aiming to address these issues, this paper presents the Transformer and Auxiliary Classifier Generative Adversarial Network (TACGAN), which increases the diversity, complexity and entropy of generated samples, and maximizes the entropy of the generated samples. The transformer network replaces traditional convolutional neural networks (CNNs), avoiding iterative and convolutional structures, thereby reducing computational expenses. Moreover, an independent classifier is integrated to prevent the coupling problem, where the discriminator is simultaneously identified and classified in the ACGAN. Finally, the Wasserstein distance is employed in the loss function to mitigate mode collapse and vanishing gradients. Experimental results using the train wheelset bearing datasets demonstrate the accuracy and effectiveness of the TACGAN.

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基于变压器的增强辅助分类器生成对抗网络样本增强在铁路货运列车轮对轴承故障诊断中的应用。
轮对轴承故障诊断对列车安全至关重要。主要的挑战是在高速列车运行过程中,只能获得有限数量的故障样本数据。这种样本的稀缺性影响了轮对轴承故障诊断深度学习模型的训练和准确性。研究表明,辅助分类器生成对抗网络(ACGAN)在解决这一问题方面表现出良好的性能。然而,现有的ACGAN模型存在复杂、计算成本高、模式崩溃和梯度消失等缺点。针对这些问题,本文提出了变压器和辅助分类器生成对抗网络(TACGAN),该网络增加了生成样本的多样性、复杂性和熵,并使生成样本的熵最大化。变压器网络取代了传统的卷积神经网络(cnn),避免了迭代和卷积结构,从而减少了计算开销。此外,为了防止耦合问题,在ACGAN中集成了一个独立的分类器,其中鉴别器在ACGAN中同时被识别和分类。最后,在损失函数中使用Wasserstein距离来减轻模态崩溃和梯度消失。列车轮对轴承数据集的实验结果验证了该算法的准确性和有效性。
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来源期刊
Entropy
Entropy PHYSICS, MULTIDISCIPLINARY-
CiteScore
4.90
自引率
11.10%
发文量
1580
审稿时长
21.05 days
期刊介绍: Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.
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