Vibration analysis of a meshing gear pair by neural network (Visualization of meshing vibration and detection of a crack at tooth root by VGG16 with transfer learning)-猫眼课题宝

DOI：

10.1117/12.2514250

发表时间：

2019-03

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

D. Iba;Y. Ishii;Y. Tsutsui;N. Miura;T. Iizuka;A. Masuda;A. Sone;I. Moriwaki

本文展示了基于深度神经网络的裂纹检测系统，分析了塑料齿轮的啮合振动。一种齿轮操作试验台，具有附接在轴承箱上的加速度传感器和高速摄像机。测量了塑料齿轮在工作过程中的啮合振动，并采集了齿轮的齿面图像，以判断齿轮是否存在裂纹。通过FFT将啮合振动数据从时域转换到频域，将啮合振动的幅值和相位信息转换为图像数据。根据高速摄像机拍摄的图像，将成像的振动数据分为有裂纹和无裂纹两类，作为深度神经网络的训练数据。此外，构建了两个4层和16层卷积神经网络来分类裂缝存在或不存在，并从标记的数据集中学习系统。在训练中，准备卷积的随机加权函数，图像的数量为350，epoch的数量为125。4层卷积神经网络的学习已经完成，但是16层卷积神经网络的学习却没有任何进展。然后，将迁移学习方法用于16层卷积神经网络。对16层卷积神经网络进行了迁移学习，125步学习的正确率达到97.2%。

This paper shows crack detection systems based on deep neural networks, which analyze meshing vibration of plastic gears. A gear operating test rig has an acceleration sensor attached on a bearing housing and a high-speed camera. The meshing vibration of plastic gears during operation was measured and teeth images that enable us to decide whether cracks exists were captured. After transferring the meshing vibration data in the time domain to the frequency domain by FFT, the amplitude and phase information of the meshing vibration was converted to image data. According to the images from the high-speed camera, the imaged vibration data were separated to two classes, with or without crack, as the training data for deep neural networks. Furthermore, two convolutional neural networks, 4 layers and 16 layers were constructed for classification of crack existence or non-existence, and the systems were learned from the labeled data set. In the training, the random weighting functions of the convolution were prepared, and the number of images were 350 and the number of epoch was 125. The learning of the 4 layers convolutional neural network was finished appropriately, however, the learning of the 16 layers convolutional neural network did not progress at all. Then, the transfer learning method was used for the 16 layers convolutional neural network. The transfer learning of the 16 layers convolutional neural network was finished appropriately, and the accuracy at 125 learning steps reached to 97.2%.

Vibration analysis of a meshing gear pair by neural network (Visualization of meshing vibration and detection of a crack at tooth root by VGG16 with transfer learning)

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