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A New Transfer Learning Based on VGG-19 Network for Fault Diagnosis

机译：基于VGG-19网络的故障诊断新转移学习。

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摘要

Deep learning (DL) has been widely applied in the fault diagnosis field. However, the depth of DL models in fault diagnosis is very shallow compared with benchmark convolutional neural network (CNN) models for ImageNet. But it is hard to train a very deep CNN model without the large amount well-organized datasets like ImageNet. In this research, a new transfer learning based on pre-trained VGG-19 (TranVGG-19) is proposed for fault diagnosis. Firstly, a time-domain signals to RGB images conversion method is proposed. Then, the pre-trained VGG-19 is applied as feature extractor to obtained the features of converted images. Finally, a softmax classifier is trained on the features. The proposed TranVGG-19is tested on the famous motor bearing dataset from Case Western Reserve University. The final prediction accuracy of TCNN is 99.175% and the training time of TranVGG-19is only near 200 seconds. These results outperform many DL and machining learning methods.
机译：深度学习（DL）已广泛应用于故障诊断领域。但是，与ImageNet的基准卷积神经网络（CNN）模型相比，DL模型在故障诊断中的深度非常浅。但是，如果没有像ImageNet这样的大量组织良好的数据集，很难训练出非常深的CNN模型。在这项研究中，提出了一种基于预训练的VGG-19（TranVGG-19）的新的转移学习方法，用于故障诊断。首先，提出了一种时域信号到RGB图像的转换方法。然后，将经过预训练的VGG-19用作特征提取器，以获取转换后图像的特征。最后，在功能上训练softmax分类器。拟议的TranVGG-19在凯斯西储大学的著名运动轴承数据集上进行了测试。 TCNN的最终预测精度为99.175％，TranVGG-19的训练时间仅接近200秒。这些结果优于许多DL和加工学习方法。

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