Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning
Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning
复制标题
使用深度迁移学习进行高精度机器故障诊断
DOI：
10.1109/tii.2018.2864759
复制
发表时间：
2019-04-01
影响因子：
12.3
通讯作者：
Baldi, Pierre
中科院分区：
文献类型：
--
作者：
Shao, Siyu;McAleer, Stephen;Baldi, Pierre
我们开发了一种新的深度学习框架，利用迁移学习来实现高精度的机器故障诊断，并加速了深度神经网络的训练。与现有方法相比，该方法训练速度更快，准确率更高。首先，对传感器原始数据进行小波变换，得到图像时频分布；接下来，使用预训练的网络提取较低级别的特征。然后使用标记的时频图像对神经网络结构的更高层次进行微调。本文建立了机器故障诊断管道，并在分别为6000、9000和5000个时间序列样本的感应电机、齿轮箱和轴承三个主要机械数据集上进行了实验，验证了该管道的有效性和泛化性。我们在每个数据集上都取得了最先进的结果，大多数数据集的测试准确率接近100%，在变速箱数据集上，我们实现了从94.8%到99.64%的显著改进。我们创建了一个包含这些数据集的存储库，位于mlmechanics.ics.uci.edu。
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.