基于卷积注意力特征迁移学习的滚动轴承故障诊断

针对变工况条件下因源域和目标域样本数据分布差异大造成滚动轴承故障诊断准确率较低的问题,提出一种新的迁移学习方法——卷积注意力特征迁移学习(Convolutional Attention-based Feature Transfer Learning, CAFTL),并用于变工况条件下的滚动轴承故障诊断。在所提出的CAFTL中,将源域和目标域样本经过多头自注意力计算再经过归一化之后,输入到卷积神经网络中得到对应的源域和目标域特征；然后通过域自适应迁移学习网络将两域特征投影到同一个公共特征空间内；接着,利用由源域有标签样本构建的分类器进行分类；最后,利用随机梯度下降(Stochastic Gradient Descent, SGD)方法对CAFTL进行训练和参数更新,得到CAFTL的最优参数集后将参数优化后的CAFTL用于滚动轴承待测样本的故障诊断。滚动轴承故障诊断实例验证了所提出的方法的有效性。

Fault diagnosis for rolling bearings based on convolutional attention-based feature transfer learning

Aiming at the problem that the accuracy of rolling bearing fault diagnosis is low due to the large difference in the distribution of sample data between the source domain and the target domain under variable working conditions, a new transfer learning method called convolutional attention based Feature Transfer Learning (CAFTL) is proposed and used for fault diagnosisof rolling bearings under variable working conditions. In the proposed CAFTL, the source and target domain samples are input to the convolutional neural network after multi-head self-attentive computation and normalization to obtain the corresponding source and target domain features; then, the two domain features are projected into the same common feature space by the domain adaptive transfer learning network; then, the classifier constructed from the source domain labeled samples is used for classification; finally, the Stochastic Gradient Descent (SGD) method is used to train and update the parameters of CAFTL, after obtaining the optimal parameter set of CAFTL, the optimized CAFTL is used for fault diagnosis of rolling bearing samples to be tested. A fault diagnosis example for rolling bearings verifies the effectiveness of the proposed method.