We develop a novel deep learning framework to achieve highly accurate machine fault diagnosis using transfer learning to enable and accelerate the training of deep neural network. Compared with existing methods, the proposed method is faster to train and more accurate. First, original sensor data are converted to images by conducting a Wavelet transformation to obtain time-frequency distributions. Next, a pretrained network is used to extract lower level features. The labeled time-frequency images are then used to fine-tune the higher levels of the neural network architecture. This paper creates a machine fault diagnosis pipeline and experiments are carried out to verify the effectiveness and generalization of the pipeline on three main mechanical datasets including induction motors, gearboxes, and bearings with sizes of 6000, 9000, and 5000 time series samples, respectively. We achieve state-of-the-art results on each dataset, with most datasets showing test accuracy near 100%, and in the gearbox dataset, we achieve significant improvement from 94.8% to 99.64%. We created a repository including these datasets located at mlmechanics.ics.uci.edu.

中文翻译：

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使用深度转移学习进行高精度的机器故障诊断

我们开发了一种新颖的深度学习框架，可使用转移学习来实现并加速深度神经网络的训练，从而实现高度准确的机器故障诊断。与现有方法相比，该方法训练更快，更准确。首先，通过进行小波变换将原始传感器数据转换为图像，以获得时频分布。接下来，使用预训练的网络来提取较低级别的特征。然后将标记的时频图像用于微调神经网络体系结构的更高级别。本文创建了一条机器故障诊断管道，并在三个主要的机械数据集（包括感应电动机，齿轮箱和尺寸为6000、9000，和5000个时间序列样本。我们在每个数据集上都获得了最先进的结果，大多数数据集显示的测试准确性接近100％，而在变速箱数据集中，我们实现了从94.8％到99.64％的显着改进。我们创建了一个存储库，其中包括位于mlmechanics.ics.uci.edu的这些数据集。