Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
复制标题
深度卷积迁移学习网络：一种无标签数据机器智能故障诊断新方法
DOI：
10.1109/tie.2018.2877090
复制
发表时间：
2019-09-01
影响因子：
7.7
通讯作者：
Li, Naipeng
中科院分区：
文献类型：
--
作者：
Guo, Liang;Lei, Yaguo;Li, Naipeng
机器智能故障诊断的成功依赖于以下两个条件：1）具有故障信息的标记数据是可用的; 2）训练和测试数据来自相同的概率分布。然而，对于某些机器来说，很难获得大量的标记数据。此外，即使可以从某些机器中获得标记数据，使用这种标记数据训练的智能故障诊断方法也可能由于数据分布差异而无法对从其他机器中获得的未标记数据进行分类。这些问题限制了未标记数据的机器智能故障诊断的成功应用。作为一种潜在的工具，迁移学习将在源领域中训练的模型适应于其在目标领域中的应用。基于迁移学习，我们提出了一种新的智能方法，称为深度卷积迁移学习网络（DCTLN）。DCTLN由两个模块组成：条件识别和域自适应。条件识别模块由一维（1-D）卷积神经网络（CNN）构建，以自动学习特征并识别机器的健康状况。域自适应模块通过最大化域识别误差和最小化概率分布距离来促进1-D CNN学习域不变特征。六个转移故障诊断实验验证了该方法的有效性。
The success of intelligent fault diagnosis of machines relies on the following two conditions: 1) labeled data with fault information are available; and 2) the training and testing data are drawn from the same probability distribution. However, for some machines, it is difficult to obtain massive labeled data. Moreover, even though labeled data can be obtained from some machines, the intelligent fault diagnosis method trained with such labeled data possibly fails in classifying unlabeled data acquired from the other machines due to data distribution discrepancy. These problems limit the successful applications of intelligent fault diagnosis of machines with unlabeled data. As a potential tool, transfer learning adapts a model trained in a source domain to its application in a target domain. Based on the transfer learning, we propose a new intelligent method named deep convolutional transfer learning network (DCTLN). A DCTLN consists of two modules: condition recognition and domain adaptation. The condition recognition module is constructed by a one-dimensional (1-D) convolutional neural network (CNN) to automatically learn features and recognize health conditions of machines. The domain adaptation module facilitates the 1-D CNN to learn domain-invariant features by maximizing domain recognition errors and minimizing the probability distribution distance. The effectiveness of the proposed method is verified using six transfer fault diagnosis experiments.