The present work aimed at the problems of less negative samples and more positive samples in rail fastener fault diagnosis and low detection accuracy of heavy manual patrol inspection tasks. Exploiting the capacity of a Convolution Neural Network (CNN) to process unbalanced data to solve tedious and inefficient manual processing, a fault diagnosis method based on a Generative Adversarial Network (GAN) and a Residual Network (ResNet) was developed. First, GAN was used to track the distribution of rail fastener failure data. To study the noise distribution, the mapping relationship between image data was established. Additional real fault samples were then generated to balance and extend the existing data sets, and these data sets were used as input to ResNet for recognition and detection training. Finally, the average accuracy of multiple experiments was used as the evaluation index. The experimental results revealed that the fault diagnosis of rail fastener based on GAN and ResNet could improve the fault detection accuracy in the case of a serious shortage of fault data.

中文翻译：

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基于生成对抗网络和残差网络模型的铁路紧固件故障诊断

目前的工作针对的是轨道扣件故障诊断中的负样本少，正样本多，重型手动巡逻检查任务的检测精度低的问题。利用卷积神经网络（CNN）处理不平衡数据的能力来解决繁琐且效率低下的手动处理的问题，开发了一种基于生成对抗网络（GAN）和残差网络（ResNet）的故障诊断方法。首先，GAN用于跟踪铁路扣件失效数据的分布。为了研究噪声分布，建立了图像数据之间的映射关系。然后生成其他实际故障样本，以平衡和扩展现有数据集，并将这些数据集用作ResNet的输入，以进行识别和检测培训。最后，多次实验的平均准确度作为评价指标。实验结果表明，在严重缺乏故障数据的情况下，基于GAN和ResNet的铁路紧固件故障诊断可以提高故障检测的准确性。